feat: Add utility functions for signal processing and event mapping

- Created a new module `utils/__init__.py` to consolidate utility imports. - Added `event_map.py` for mapping apnea event types to numerical values and colors. - Implemented various filtering functions in `filter_func.py`, including Butterworth, Bessel, downsampling, and notch filters. - Developed `operation_tools.py` for dataset configuration loading, event mask generation, and signal processing utilities. - Introduced `split_method.py` for segmenting data based on movement and amplitude criteria. - Added `statistics_metrics.py` for calculating amplitude metrics and generating confusion matrices. - Included a new Excel file for additional data storage.
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+# 项目背景文档（供 Agent 快速上手）
+
+## 1. 文档目的
+
+这份文档用于帮助新进入仓库的 agent 快速建立项目心智模型，重点回答下面几个问题：
+
+- 这个仓库在做什么
+- 核心处理链路是什么
+- 代码从哪里开始看
+- 输入数据长什么样，输出产物长什么样
+- 哪些模块已经可用，哪些模块还只是占位或半成品
+
+---
+
+## 2. 一句话理解项目
+
+`DataPrepare` 是一个睡眠呼吸暂停数据预处理仓库，主要用于把原始的 BCG / PSG 信号与呼吸暂停标签整理成可训练的数据集。
+
+当前主线可以概括为两步：
+
+1. 在 `event_mask_process/` 中把原始标注和规则检测结果整理成逐秒标签掩码。
+2. 在 `dataset_builder/` 中根据这些掩码切出固定长度窗口，并将信号和窗口索引保存为 `npz` 数据集。
+
+---
+
+## 3. 仓库总览
+
+| 目录 | 作用 | 备注 |
+| --- | --- | --- |
+| `dataset_config/` | 各数据集的 YAML 配置 | 包含路径、采样率、阈值、切窗参数 |
+| `event_mask_process/` | 生成逐秒标签掩码 | 主入口之一 |
+| `dataset_builder/` | 将信号切成窗口并保存数据集 | 主入口之二 |
+| `signal_method/` | 信号预处理、规则检测、特征计算 | 规则逻辑核心 |
+| `utils/` | 读文件、标签转换、切窗、滤波、通用工具 | 底层支撑 |
+| `draw_tools/` | 全夜信号图、分段图、统计图 | 调试与可视化 |
+| `dataset_tools/` | 辅助脚本 | 包括配对拷贝、SHHS 标注检查 |
+| `output/` | 中间结果样例 | 仓库内已有若干处理结果示例 |
+
+---
+
+## 4. 项目主线流程
+
+### 4.1 HYS（BCG 主线）
+
+对应文件：
+
+- `event_mask_process/HYS_process.py`
+- `dataset_builder/HYS_dataset.py`
+
+处理过程：
+
+1. 从 `root_path/OrgBCG_Aligned/<id>/OrgBCG_Sync_*.txt` 读取原始 BCG 同步信号。
+2. 从 `root_path/Label/<id>/SA Label_corrected.csv` 读取人工修正后的呼吸暂停标签。
+3. 对原始 BCG 进行 50Hz 陷波，再拆出：
+   - 呼吸分量 `resp_data`
+   - BCG 分量 `bcg_data`
+4. 根据配置做规则检测，生成逐秒掩码：
+   - `Resp_LowAmp_Label`
+   - `Resp_Movement_Label`
+   - `Resp_AmpChange_Label`
+   - `BCG_LowAmp_Label`
+   - `BCG_Movement_Label`
+   - `BCG_AmpChange_Label`
+   - `Disable_Label`（来自 `排除区间.xlsx`）
+   - `SA_Label` / `SA_Score`（来自人工标签）
+5. 将这些结果保存为 `output/HYS/<id>/<id>_Processed_Labels.csv`。
+6. 在数据集构建阶段读取上述逐秒标签，按 `window_sec` 和 `stride_sec` 切成窗口。
+7. 保存处理后的信号 `Signals/*.npz`、窗口索引 `Segments_List/*.npz`、标签副本 `Labels/*.csv`。
+
+### 4.2 HYS_PSG（PSG 主线）
+
+对应文件：
+
+- `event_mask_process/HYS_PSG_process.py`
+- `dataset_builder/HYS_PSG_dataset.py`
+
+处理过程：
+
+1. 从 `root_path/PSG_Aligned/<id>/` 读取 PSG 通道，包括：
+   - `Rpeak`
+   - `ECG_Sync`
+   - `Effort Tho`
+   - `Effort Abd`
+   - `Flow P`
+   - `Flow T`
+   - `SpO2`
+   - `5_class`
+2. 从同目录读取 `SA Label_Sync.csv`。
+3. 在 `HYS_PSG_process.py` 中生成较简化的逐秒标签：
+   - `SA_Label`
+   - `SA_Score`
+   - `Disable_Label`（主要由睡眠分期里的清醒期生成）
+   - 其余 `Resp_*` / `BCG_*` 掩码目前全部置零
+4. 在 `HYS_PSG_dataset.py` 中对胸腹带、流量、SpO2、RRI 做统一重采样与长度对齐。
+5. 对 SpO2 进行异常填补或置空，再切成窗口，保存为 PSG 数据集。
+
+### 4.3 设计上的共性
+
+无论是 HYS 还是 HYS_PSG，核心设计都是：
+
+1. 先把整夜记录整理成逐秒掩码。
+2. 再根据掩码和切窗规则提取训练片段。
+
+这意味着以后如果要改“可用性判断”或“切窗逻辑”，优先看：
+
+- `event_mask_process/`
+- `utils/split_method.py`
+
+---
+
+## 5. 数据与目录约定
+
+### 5.1 外部数据目录
+
+这个仓库本身不包含原始数据，真正的数据目录由 YAML 中的绝对路径指定，例如：
+
+- `/mnt/disk_wd/marques_dataset/DataCombine2023/HYS`
+- `/mnt/disk_wd/marques_dataset/shhs/polysomnography/shhs1`
+
+因此在新环境运行时，第一件事通常不是改代码，而是先改 `dataset_config/*.yaml` 里的绝对路径。
+
+### 5.2 HYS 数据目录约定
+
+代码默认外部目录至少包含：
+
+- `OrgBCG_Aligned/<id>/OrgBCG_Sync_<fs>.txt`
+- `PSG_Aligned/<id>/...`
+- `Label/<id>/SA Label_corrected.csv`
+
+### 5.3 HYS_PSG 数据目录约定
+
+代码默认 `PSG_Aligned/<id>/` 内文件命名符合下面的模式：
+
+- `Rpeak*.txt`
+- `ECG_Sync*.txt`
+- `Effort Tho*.txt`
+- `Effort Abd*.txt`
+- `Flow P*.txt`
+- `Flow T*.txt`
+- `SpO2*.txt`
+- `5_class*.txt`
+- `SA Label_Sync.csv`
+
+### 5.4 处理中间结果目录
+
+仓库内 `output/` 保存的是“中间标签与图像结果”，不是最终训练数据集。  
+最终数据集通常会写到 YAML 中 `dataset_save_path` 指向的外部目录。
+
+---
+
+## 6. 核心入口脚本
+
+### 6.1 主入口
+
+| 场景 | 脚本 |
+| --- | --- |
+| HYS 逐秒标签生成 | `event_mask_process/HYS_process.py` |
+| HYS 数据集构建 | `dataset_builder/HYS_dataset.py` |
+| HYS_PSG 逐秒标签生成 | `event_mask_process/HYS_PSG_process.py` |
+| HYS_PSG 数据集构建 | `dataset_builder/HYS_PSG_dataset.py` |
+
+### 6.2 辅助脚本
+
+| 脚本 | 作用 |
+| --- | --- |
+| `dataset_tools/resp_pair_copy.py` | 将 HYS/ZD5Y 的 BCG 与 PSG 原始文件拷贝为配对数据集 |
+| `dataset_tools/shhs_annotations_check.py` | 统计 SHHS XML 标注中的事件类型组合 |
+| `event_mask_process/SHHS1_process.py` | SHHS1 处理入口占位，目前未实现 |
+
+### 6.3 入口脚本的共同特点
+
+- 大多数脚本没有命令行参数接口。
+- 配置文件路径通常直接写在 `if __name__ == '__main__':` 中。
+- 运行逻辑依赖 YAML 中的绝对路径。
+
+所以如果 agent 要“跑脚本”，通常需要先确认：
+
+1. 当前机器是否存在对应外部数据目录。
+2. YAML 路径是否匹配当前环境。
+
+---
+
+## 7. 关键模块说明
+
+### 7.1 `utils/`
+
+#### `utils/HYS_FileReader.py`
+
+负责各种输入文件读取，是仓库最重要的底层模块之一：
+
+- `read_signal_txt`：读取单通道 txt，并根据文件名中的采样率推断 `fs`
+- `read_label_csv`：读取人工修正的 HYS 标签
+- `read_raw_psg_label`：读取 PSG 原始同步标签
+- `read_disable_excel`：读取 `排除区间.xlsx`
+- `read_mask_execl`：读取处理后的逐秒标签 CSV，并生成事件片段列表
+- `read_psg_channel`：按通道名读取 PSG 文件夹里的多通道数据
+
+#### `utils/operation_tools.py`
+
+负责标签转换、片段提取和通用处理：
+
+- `load_dataset_conf`：读取 YAML
+- `generate_event_mask`：把事件表转换成逐秒 `SA_Label`
+- `generate_disable_mask`：把 Excel 中的排除区间转换成逐秒 `Disable_Label`
+- `event_mask_2_list`：把 0/1 掩码转为 `[start, end]` 列表
+- `merge_short_gaps` / `remove_short_durations`：对逐秒掩码做时长后处理
+- `fill_spo2_anomaly`：修补 SpO2 异常段
+
+#### `utils/split_method.py`
+
+真正的切窗规则在这里：
+
+- 默认按 `window_sec` / `stride_sec` 滑窗
+- 只在 `EnableSegment` 内生成可用窗口
+- 如果一个窗口中 `Resp_Movement_Label | Resp_LowAmp_Label` 超过窗口时长的 2/3，则该窗口转入 `disable_segment_list`
+
+#### `utils/filter_func.py`
+
+提供滤波和采样率处理：
+
+- Butterworth / Bessel
+- 陷波
+- 整数倍降采样
+- 自动升降采样
+- 移动平均去趋势
+
+### 7.2 `signal_method/`
+
+#### `signal_method/signal_process.py`
+
+负责信号预处理：
+
+- `signal_filter_split`：把原始 OrgBCG 信号拆成呼吸分量和 BCG 分量
+- `psg_effort_filter`：处理 PSG 努力带 / 流量信号
+- `rpeak2hr` / `rpeak2rri_interpolation`：由 R 峰生成 HR / RRI
+
+#### `signal_method/rule_base_event.py`
+
+规则检测主逻辑：
+
+- `detect_movement`：基于滑窗标准差和局部幅值比较检测体动
+- `movement_revise`：对体动掩码做二次修正
+- `detect_low_amplitude_signal`：检测低幅值
+- `position_based_sleep_recognition_v2/v3`：根据体动前后幅值变化标记姿势/幅值变化段
+
+#### `signal_method/normalize_method.py`
+
+`normalize_resp_signal_by_segment` 会按“可用片段”做分段 z-score 标准化。  
+HYS 中通常按 `Resp_AmpChange_Label` 的反向片段来归一化，目的是减少整夜幅值漂移带来的影响。
+
+### 7.3 `draw_tools/`
+
+主要用于人工检查处理质量：
+
+- `draw_signal_with_mask`：画 HYS 全夜原始信号 + 规则掩码
+- `draw_psg_signal`：画 HYS_PSG 全夜 PSG 信号
+- `draw_psg_label` / `draw_psg_bcg_label`：按窗口导出分段图
+
+### 7.4 `dataset_builder/`
+
+核心职责是把“整夜记录 + 逐秒掩码”转换成训练样本：
+
+- 保存处理后的多通道信号到 `npz`
+- 保存窗口起止列表到 `npz`
+- 把标签 CSV 一并拷贝到数据集目录
+
+---
+
+## 8. 标签、通道和编码约定
+
+### 8.1 呼吸暂停事件编码
+
+定义于 `utils/event_map.py`：
+
+| 事件 | 编码 |
+| --- | --- |
+| `Hypopnea` | 1 |
+| `Central apnea` | 2 |
+| `Obstructive apnea` | 3 |
+| `Mixed apnea` | 4 |
+
+### 8.2 PSG 通道编号映射
+
+同样定义于 `utils/event_map.py`：
+
+| 编号 | 通道名 |
+| --- | --- |
+| 1 | `Rpeak` |
+| 2 | `ECG_Sync` |
+| 3 | `Effort Tho` |
+| 4 | `Effort Abd` |
+| 5 | `Flow P` |
+| 6 | `Flow T` |
+| 7 | `SpO2` |
+| 8 | `5_class` |
+
+### 8.3 睡眠分期编码
+
+`5_class` 在读取时会转成整数：
+
+| 分期 | 编码 |
+| --- | --- |
+| `N3` | 1 |
+| `N2` | 2 |
+| `N1` | 3 |
+| `R` | 4 |
+| `W` | 5 |
+
+### 8.4 逐秒标签 CSV 字段
+
+`Processed_Labels.csv` 的核心字段为：
+
+- `Second`
+- `SA_Label`
+- `SA_Score`
+- `Disable_Label`
+- `Resp_LowAmp_Label`
+- `Resp_Movement_Label`
+- `Resp_AmpChange_Label`
+- `BCG_LowAmp_Label`
+- `BCG_Movement_Label`
+- `BCG_AmpChange_Label`
+
+样例可见仓库内：
+
+- `output/HYS/220/220_Processed_Labels.csv`
+- `output/HYS_PSG/220/220_Processed_Labels.csv`
+
+---
+
+## 9. 主要配置文件
+
+### 9.1 HYS
+
+`dataset_config/HYS_config.yaml` 主要控制：
+
+- 样本 ID 列表
+- 原始数据根目录
+- 中间标签保存目录
+- 呼吸 / BCG 的滤波和降采样参数
+- 低幅值、体动、幅值变化检测阈值
+- 数据集窗口长度和步长
+
+### 9.2 HYS_PSG
+
+`dataset_config/HYS_PSG_config.yaml` 主要控制：
+
+- 样本 ID 列表
+- 目标统一采样率 `target_fs`
+- 努力带 / 流量滤波参数
+- SpO2 异常填补参数
+- 数据集输出路径
+
+### 9.3 其他配置
+
+- `dataset_config/ZD5Y_config.yaml`：另一套 BCG 规则配置
+- `dataset_config/SHHS1_config.yaml`：SHHS1 预留配置
+- `dataset_config/RESP_PAIR_HYS_config.yaml`：HYS 配对原始数据拷贝
+- `dataset_config/RESP_PAIR_ZD5Y_config.yaml`：ZD5Y 配对原始数据拷贝
+
+---
+
+## 10. 输出产物说明
+
+### 10.1 中间输出
+
+典型位置：
+
+- `output/HYS/<id>/<id>_Processed_Labels.csv`
+- `output/HYS/<id>/<id>_Signal_Plots.png`
+- `output/HYS_PSG/<id>/<id>_Processed_Labels.csv`
+- `output/HYS_PSG/<id>/<id>_Signal_Plots.png`
+- `output/HYS_PSG/<id>/<id>_Signal_Plots_fill.png`
+
+### 10.2 最终数据集输出
+
+由 `dataset_builder/*` 保存到 YAML 指定的外部目录，结构通常是：
+
+- `Signals/`
+- `Segments_List/`
+- `Labels/`
+
+HYS 的 `Signals/*.npz` 里主要保存：
+
+- `bcg_signal_notch`
+- `bcg_signal`
+- `resp_signal`
+
+HYS_PSG 的 `Signals/*.npz` 里主要保存：
+
+- `Effort Tho`
+- `Effort Abd`
+- `Effort`
+- `Flow P`
+- `Flow T`
+- `SpO2`
+- `HR`
+- `RRI`
+- `5_class`
+
+`Segments_List/*.npz` 中主要保存：
+
+- `segment_list`
+- `disable_segment_list`
+
+---
+
+## 11. 推荐阅读顺序
+
+如果 agent 是第一次接触这个仓库，建议按下面顺序阅读：
+
+1. `dataset_config/HYS_config.yaml`
+2. `event_mask_process/HYS_process.py`
+3. `utils/operation_tools.py`
+4. `utils/split_method.py`
+5. `dataset_builder/HYS_dataset.py`
+6. `signal_method/signal_process.py`
+7. `signal_method/rule_base_event.py`
+8. `utils/HYS_FileReader.py`
+
+如果要看 PSG 主线，再继续读：
+
+1. `dataset_config/HYS_PSG_config.yaml`
+2. `event_mask_process/HYS_PSG_process.py`
+3. `dataset_builder/HYS_PSG_dataset.py`
+4. `draw_tools/draw_label.py`
+
+---
+
+## 12. 常见修改入口
+
+如果你要改不同类型的问题，可以优先从这些文件入手：
+
+| 需求 | 优先看哪里 |
+| --- | --- |
+| 调整阈值、采样率、窗口长度 | `dataset_config/*.yaml` |
+| 修改逐秒标签生成逻辑 | `event_mask_process/*.py` |
+| 修改体动 / 低幅值 / 幅值变化规则 | `signal_method/rule_base_event.py` |
+| 修改滤波与重采样 | `signal_method/signal_process.py`、`utils/filter_func.py` |
+| 修改切窗规则 | `utils/split_method.py` |
+| 修改输入文件解析 | `utils/HYS_FileReader.py` |
+| 修改事件编码或通道映射 | `utils/event_map.py` |
+| 修改图像输出样式 | `draw_tools/*.py` |
+
+---
+
+## 13. 当前实现状态与注意事项
+
+下面这些点对 agent 很重要：
+
+1. 仓库没有依赖清单文件（如 `requirements.txt` / `pyproject.toml`），依赖需要从源码导入中反推，当前至少涉及 `numpy`、`pandas`、`scipy`、`matplotlib`、`seaborn`、`yaml`、`tqdm`、`rich`、`polars`、`lxml`、`mne`。
+2. 大部分脚本依赖绝对路径，迁移环境时优先修改 YAML。
+3. `event_mask_process/SHHS1_process.py` 目前基本为空，占位多于实现。
+4. `event_mask_process/HYS_PSG_process.py` 当前是“简化版标签生成”，核心只用了 SA 标签和睡眠分期，`Resp_*` / `BCG_*` 掩码还没有真正实现。
+5. `dataset_builder/HYS_dataset.py` 里虽然保留了分段可视化入口，但实际绘图调用被注释掉了，因此默认不会导出分段图。
+6. `utils/signal_process.py` 和 `utils/filter_func.py` 有部分重复实现；当前实际被 `utils/__init__.py` 导出并广泛使用的是 `filter_func.py` 中的版本。
+7. `README.md` 目前非常简略，真正的项目逻辑主要还是要靠源码理解。
+
+---
+
+## 14. 对 Agent 最有价值的结论
+
+如果只能记住几件事，请记住下面这些：
+
+1. 这是一个“先做逐秒掩码，再做固定窗口切片”的数据准备仓库。
+2. HYS 主线比 HYS_PSG 更完整，规则检测主要服务于 HYS。
+3. 切窗逻辑集中在 `utils/split_method.py`，不是分散在各个 builder 里。
+4. 原始数据不在仓库里，仓库只是代码和部分中间结果样例。
+5. 大多数运行问题都不是代码逻辑错，而是路径、文件命名、采样率和外部数据结构不匹配。
+
--- a/README.md
+++ b/README.md
@ -0,0 +1,191 @@
+# DataPrepare
+
+`DataPrepare` 是一个面向睡眠呼吸暂停数据的预处理仓库，用于把原始 BCG / PSG 信号与事件标签整理成可训练的数据集。
+
+当前仓库的主线是：
+
+1. 生成逐秒标签掩码
+2. 按固定窗口切分数据集
+3. 输出可视化结果用于人工检查
+
+更详细的项目背景说明见 [AGENT_BACKGROUND_CN.md](./AGENT_BACKGROUND_CN.md)。
+
+## 项目在做什么
+
+仓库主要服务两类数据：
+
+- `HYS`：以 `OrgBCG` 为核心，结合人工修正的呼吸暂停标签，提取呼吸分量、BCG 分量并生成逐秒可用性掩码
+- `HYS_PSG`：以 PSG 多通道信号为核心，整理胸腹带、流量、SpO2、RRI、睡眠分期与同步呼吸暂停标签
+
+两条主线都采用同一个设计：
+
+1. 先把整夜记录整理成逐秒标签
+2. 再根据标签切出固定长度窗口
+
+## 仓库结构
+
+| 目录 | 作用 |
+| --- | --- |
+| `dataset_config/` | 数据集配置文件，包含路径、采样率、阈值、切窗参数 |
+| `event_mask_process/` | 逐秒标签掩码生成脚本 |
+| `dataset_builder/` | 数据集切片与保存脚本 |
+| `signal_method/` | 信号预处理、规则检测、特征计算 |
+| `utils/` | 文件读取、标签转换、切窗、滤波等通用工具 |
+| `draw_tools/` | 全夜图、分段图和统计图绘制 |
+| `dataset_tools/` | 辅助脚本，如配对数据拷贝、SHHS 标注检查 |
+| `output/` | 仓库内的中间结果样例 |
+
+## 核心流程
+
+### HYS
+
+对应脚本：
+
+- `event_mask_process/HYS_process.py`
+- `dataset_builder/HYS_dataset.py`
+
+流程概要：
+
+1. 读取 `OrgBCG_Aligned/<id>/OrgBCG_Sync_*.txt`
+2. 读取 `Label/<id>/SA Label_corrected.csv`
+3. 对原始信号做陷波、呼吸分量提取和 BCG 分量提取
+4. 检测低幅值、体动、幅值变化，并结合 `排除区间.xlsx` 生成逐秒标签
+5. 保存 `Processed_Labels.csv`
+6. 根据 `window_sec` 与 `stride_sec` 切分训练窗口并保存 `npz`
+
+### HYS_PSG
+
+对应脚本：
+
+- `event_mask_process/HYS_PSG_process.py`
+- `dataset_builder/HYS_PSG_dataset.py`
+
+流程概要：
+
+1. 读取 `PSG_Aligned/<id>/` 下的多通道信号
+2. 读取 `SA Label_Sync.csv`
+3. 根据同步标签与睡眠分期生成逐秒标签
+4. 对努力带、流量、SpO2、RRI 做统一重采样和长度对齐
+5. 切分窗口并保存 PSG 数据集
+
+## 快速开始
+
+### 1. 先检查配置
+
+所有主脚本都依赖 `dataset_config/*.yaml` 中的绝对路径。  
+在新机器或新数据目录下，优先修改这些配置文件：
+
+- `dataset_config/HYS_config.yaml`
+- `dataset_config/HYS_PSG_config.yaml`
+- `dataset_config/ZD5Y_config.yaml`
+- `dataset_config/SHHS1_config.yaml`
+
+重点检查：
+
+- `root_path`
+- `mask_save_path` 或 `save_path`
+- `dataset_save_path`
+- `dataset_visual_path`
+- `select_ids`
+
+### 2. 生成逐秒标签
+
+HYS：
+
+```bash
+python event_mask_process/HYS_process.py
+```
+
+HYS_PSG：
+
+```bash
+python event_mask_process/HYS_PSG_process.py
+```
+
+执行后通常会在 `output/` 下生成：
+
+- `*_Processed_Labels.csv`
+- `*_Signal_Plots.png`
+
+### 3. 构建数据集
+
+HYS：
+
+```bash
+python dataset_builder/HYS_dataset.py
+```
+
+HYS_PSG：
+
+```bash
+python dataset_builder/HYS_PSG_dataset.py
+```
+
+输出目录由对应 YAML 中的 `dataset_save_path` 控制，通常包含：
+
+- `Signals/`
+- `Segments_List/`
+- `Labels/`
+
+### 4. 可视化与辅助工具
+
+配对拷贝原始数据：
+
+```bash
+python dataset_tools/resp_pair_copy.py
+```
+
+检查 SHHS XML 标注：
+
+```bash
+python dataset_tools/shhs_annotations_check.py
+```
+
+## 输入与输出约定
+
+### 输入
+
+仓库本身不包含原始数据，原始数据目录由 YAML 指定。代码默认外部数据目录中至少存在：
+
+- `OrgBCG_Aligned/<id>/OrgBCG_Sync_*.txt`
+- `PSG_Aligned/<id>/...`
+- `Label/<id>/SA Label_corrected.csv`
+- `PSG_Aligned/<id>/SA Label_Sync.csv`
+
+### 中间输出
+
+仓库内 `output/` 保存的是中间标签与图像样例，不是最终训练数据集。例如：
+
+- `output/HYS/<id>/<id>_Processed_Labels.csv`
+- `output/HYS_PSG/<id>/<id>_Processed_Labels.csv`
+
+### 最终输出
+
+由 `dataset_builder/` 写入 YAML 中配置的外部目录。典型结构为：
+
+- `Signals/*.npz`
+- `Segments_List/*.npz`
+- `Labels/*.csv`
+
+## 关键文件
+
+如果你是第一次阅读这个仓库，推荐优先看：
+
+1. `dataset_config/HYS_config.yaml`
+2. `event_mask_process/HYS_process.py`
+3. `utils/operation_tools.py`
+4. `utils/split_method.py`
+5. `dataset_builder/HYS_dataset.py`
+6. `signal_method/rule_base_event.py`
+
+## 当前状态与注意事项
+
+- 仓库目前没有依赖清单文件，常见依赖包括 `numpy`、`pandas`、`scipy`、`matplotlib`、`seaborn`、`yaml`、`tqdm`、`rich`、`polars`、`lxml`、`mne`
+- 大多数脚本没有命令行参数接口，配置文件路径直接写在脚本 `__main__` 中
+- `event_mask_process/SHHS1_process.py` 目前基本还是占位
+- `event_mask_process/HYS_PSG_process.py` 当前实现偏简化，`Resp_*` / `BCG_*` 掩码尚未真正展开
+- `output/` 里的文件更适合拿来理解格式与结果，不代表完整数据集
+
+## 相关文档
+
+- 详细项目背景：[AGENT_BACKGROUND_CN.md](./AGENT_BACKGROUND_CN.md)
--- a/dataset_builder/HYS_PSG_dataset.py
+++ b/dataset_builder/HYS_PSG_dataset.py
@ -0,0 +1,395 @@
+import multiprocessing
+import sys
+from pathlib import Path
+
+import os
+import numpy as np
+
+from utils import N2Chn
+
+os.environ['DISPLAY'] = "localhost:10.0"
+
+sys.path.append(str(Path(__file__).resolve().parent.parent))
+project_root_path = Path(__file__).resolve().parent.parent
+
+import  utils
+import signal_method
+import draw_tools
+import shutil
+import gc
+
+def build_HYS_dataset_segment(samp_id, show=False, draw_segment=False, verbose=True, multi_p=None, multi_task_id=None):
+    psg_data = utils.read_psg_channel(psg_signal_root_path / f"{samp_id}", [1, 2, 3, 4, 5, 6, 7, 8], verbose=verbose)
+
+    total_seconds = min(
+        psg_data[i]["second"] for i in N2Chn.values() if i != "Rpeak"
+    )
+    for i in N2Chn.values():
+        if i == "Rpeak":
+            continue
+        length = int(total_seconds * psg_data[i]["fs"])
+        psg_data[i]["data"] = psg_data[i]["data"][:length]
+        psg_data[i]["length"] = length
+        psg_data[i]["second"] = total_seconds
+
+    psg_data["HR"] = {
+        "name": "HR",
+        "data": signal_method.rpeak2hr(psg_data["Rpeak"]["data"], psg_data["ECG_Sync"]["length"],
+                                       psg_data["Rpeak"]["fs"]),
+        "fs": psg_data["ECG_Sync"]["fs"],
+        "length": psg_data["ECG_Sync"]["length"],
+        "second": psg_data["ECG_Sync"]["second"]
+    }
+    # 预处理与滤波
+    tho_data, tho_data_filt, tho_fs = signal_method.psg_effort_filter(conf=conf, effort_data_raw=psg_data["Effort Tho"]["data"], effort_fs=psg_data["Effort Tho"]["fs"])
+    abd_data, abd_data_filt, abd_fs = signal_method.psg_effort_filter(conf=conf, effort_data_raw=psg_data["Effort Abd"]["data"], effort_fs=psg_data["Effort Abd"]["fs"])
+    flowp_data, flowp_data_filt, flowp_fs = signal_method.psg_effort_filter(conf=conf, effort_data_raw=psg_data["Flow P"]["data"], effort_fs=psg_data["Flow P"]["fs"])
+    flowt_data, flowt_data_filt, flowt_fs = signal_method.psg_effort_filter(conf=conf, effort_data_raw=psg_data["Flow T"]["data"], effort_fs=psg_data["Flow T"]["fs"])
+
+    rri, rri_fs = signal_method.rpeak2rri_interpolation(rpeak_indices=psg_data["Rpeak"]["data"], ecg_fs=psg_data["ECG_Sync"]["fs"], rri_fs=100)
+
+
+    mask_excel_path = Path(mask_path, f"{samp_id}", f"{samp_id}_Processed_Labels.csv")
+    if verbose:
+        print(f"mask_excel_path: {mask_excel_path}")
+
+    event_mask, event_list = utils.read_mask_execl(mask_excel_path)
+
+    enable_list = [[0, psg_data["Effort Tho"]["second"]]]
+    normalized_tho_signal = signal_method.normalize_resp_signal_by_segment(tho_data_filt, tho_fs, np.zeros(psg_data["Effort Tho"]["second"]), enable_list)
+    normalized_abd_signal = signal_method.normalize_resp_signal_by_segment(abd_data_filt, abd_fs, np.zeros(psg_data["Effort Abd"]["second"]), enable_list)
+    normalized_flowp_signal = signal_method.normalize_resp_signal_by_segment(flowp_data_filt, flowp_fs, np.zeros(psg_data["Flow P"]["second"]), enable_list)
+    normalized_flowt_signal = signal_method.normalize_resp_signal_by_segment(flowt_data_filt, flowt_fs, np.zeros(psg_data["Flow T"]["second"]), enable_list)
+
+
+    # 都调整至100Hz采样率
+    target_fs = conf["target_fs"]
+    normalized_tho_signal = utils.adjust_sample_rate(normalized_tho_signal, tho_fs, target_fs)
+    normalized_abd_signal = utils.adjust_sample_rate(normalized_abd_signal, abd_fs, target_fs)
+    normalized_flowp_signal = utils.adjust_sample_rate(normalized_flowp_signal, flowp_fs, target_fs)
+    normalized_flowt_signal = utils.adjust_sample_rate(normalized_flowt_signal, flowt_fs, target_fs)
+    spo2_data_filt = utils.adjust_sample_rate(psg_data["SpO2"]["data"], psg_data["SpO2"]["fs"], target_fs)
+    normalized_effort_signal = (normalized_tho_signal + normalized_abd_signal) / 2
+    rri = utils.adjust_sample_rate(rri, rri_fs, target_fs)
+
+    # 调整至相同长度
+    min_length = min(len(normalized_tho_signal), len(normalized_abd_signal), len(normalized_flowp_signal), len(normalized_flowt_signal), len(spo2_data_filt), len(normalized_effort_signal)
+                     ,len(rri))
+    min_length = min_length - min_length % target_fs  # 保证是整数秒
+    normalized_tho_signal = normalized_tho_signal[:min_length]
+    normalized_abd_signal = normalized_abd_signal[:min_length]
+    normalized_flowp_signal = normalized_flowp_signal[:min_length]
+    normalized_flowt_signal = normalized_flowt_signal[:min_length]
+    spo2_data_filt = spo2_data_filt[:min_length]
+    normalized_effort_signal = normalized_effort_signal[:min_length]
+    rri = rri[:min_length]
+
+    tho_second = min_length / target_fs
+    for i in event_mask.keys():
+        event_mask[i] = event_mask[i][:int(tho_second)]
+
+    spo2_data_filt_fill, spo2_disable_mask = utils.fill_spo2_anomaly(spo2_data=spo2_data_filt,
+                                                                spo2_fs=target_fs,
+                                                                max_fill_duration=conf["spo2_fill__anomaly"]["max_fill_duration"],
+                                                                min_gap_duration=conf["spo2_fill__anomaly"]["min_gap_duration"])
+
+
+    draw_tools.draw_psg_signal(
+        samp_id=samp_id,
+        tho_signal=normalized_tho_signal,
+        abd_signal=normalized_abd_signal,
+        flowp_signal=normalized_flowp_signal,
+        flowt_signal=normalized_flowt_signal,
+        spo2_signal=spo2_data_filt,
+        effort_signal=normalized_effort_signal,
+        rri_signal = rri,
+        fs=target_fs,
+        event_mask=event_mask["SA_Label"],
+        save_path= mask_path / f"{samp_id}" / f"{samp_id}_Signal_Plots.png",
+        show=show
+    )
+
+    draw_tools.draw_psg_signal(
+        samp_id=samp_id,
+        tho_signal=normalized_tho_signal,
+        abd_signal=normalized_abd_signal,
+        flowp_signal=normalized_flowp_signal,
+        flowt_signal=normalized_flowt_signal,
+        spo2_signal=spo2_data_filt_fill,
+        effort_signal=normalized_effort_signal,
+        rri_signal = rri,
+        fs=target_fs,
+        event_mask=event_mask["SA_Label"],
+        save_path= mask_path / f"{samp_id}" / f"{samp_id}_Signal_Plots_fill.png",
+        show=show
+    )
+
+    spo2_disable_mask = spo2_disable_mask[::target_fs]
+    min_len = min(len(event_mask["Disable_Label"]), len(spo2_disable_mask))
+
+    if len(event_mask["Disable_Label"]) != len(spo2_disable_mask):
+        print(f"Warning: Data length mismatch! Truncating to {min_len}.")
+    event_mask["Disable_Label"] = event_mask["Disable_Label"][:min_len] & spo2_disable_mask[:min_len]
+
+    event_list = {
+                  "EnableSegment": utils.event_mask_2_list(1 - event_mask["Disable_Label"]),
+                  "DisableSegment": utils.event_mask_2_list(event_mask["Disable_Label"])}
+
+    spo2_data_filt_fill = np.nan_to_num(spo2_data_filt_fill, nan=conf["spo2_fill__anomaly"]["nan_to_num_value"])
+
+    segment_list, disable_segment_list = utils.resp_split(dataset_config, event_mask, event_list, verbose=verbose)
+    if verbose:
+        print(f"Total segments extracted for sample ID {samp_id}: {len(segment_list)}")
+
+
+    # 复制mask到processed_Labels文件夹
+    save_mask_excel_path = save_processed_label_path / f"{samp_id}_Processed_Labels.csv"
+    shutil.copyfile(mask_excel_path, save_mask_excel_path)
+
+    # 复制SA Label_corrected.csv到processed_Labels文件夹
+    sa_label_corrected_path = Path(mask_path, f"{samp_id}", f"{samp_id}_SA Label_Sync.csv")
+    if sa_label_corrected_path.exists():
+        save_sa_label_corrected_path = save_processed_label_path / f"{samp_id}_SA Label_corrected.csv"
+        shutil.copyfile(sa_label_corrected_path, save_sa_label_corrected_path)
+    else:
+        if verbose:
+            print(f"Warning: {sa_label_corrected_path} does not exist.")
+
+    # 保存处理后的信号和截取的片段列表
+    save_signal_path = save_processed_signal_path / f"{samp_id}_Processed_Signals.npz"
+    save_segment_path = save_segment_list_path / f"{samp_id}_Segment_List.npz"
+
+    # psg_data更新为处理后的信号
+    # 用下划线替换键里面的空格
+    psg_data = {
+        "Effort Tho": {
+            "name": "Effort_Tho",
+            "data": normalized_tho_signal,
+            "fs": target_fs,
+            "length": len(normalized_tho_signal),
+            "second": len(normalized_tho_signal) / target_fs
+        },
+        "Effort Abd": {
+            "name": "Effort_Abd",
+            "data": normalized_abd_signal,
+            "fs": target_fs,
+            "length": len(normalized_abd_signal),
+            "second": len(normalized_abd_signal) / target_fs
+        },
+        "Effort": {
+            "name": "Effort",
+            "data": normalized_effort_signal,
+            "fs": target_fs,
+            "length": len(normalized_effort_signal),
+            "second": len(normalized_effort_signal) / target_fs
+        },
+        "Flow P": {
+            "name": "Flow_P",
+            "data": normalized_flowp_signal,
+            "fs": target_fs,
+            "length": len(normalized_flowp_signal),
+            "second": len(normalized_flowp_signal) / target_fs
+        },
+        "Flow T": {
+            "name": "Flow_T",
+            "data": normalized_flowt_signal,
+            "fs": target_fs,
+            "length": len(normalized_flowt_signal),
+            "second": len(normalized_flowt_signal) / target_fs
+        },
+        "SpO2": {
+            "name": "SpO2",
+            "data": spo2_data_filt_fill,
+            "fs": target_fs,
+            "length": len(spo2_data_filt_fill),
+            "second": len(spo2_data_filt_fill) / target_fs
+        },
+        "HR": {
+            "name": "HR",
+            "data": psg_data["HR"]["data"],
+            "fs": psg_data["HR"]["fs"],
+            "length": psg_data["HR"]["length"],
+            "second": psg_data["HR"]["second"]
+        },
+        "RRI": {
+            "name": "RRI",
+            "data": rri,
+            "fs": target_fs,
+            "length": len(rri),
+            "second": len(rri) / target_fs
+        },
+        "5_class": {
+            "name": "Stage",
+            "data": psg_data["5_class"]["data"],
+            "fs": psg_data["5_class"]["fs"],
+            "length": psg_data["5_class"]["length"],
+            "second": psg_data["5_class"]["second"]
+        }
+    }
+
+    np.savez_compressed(save_signal_path, **psg_data)
+    np.savez_compressed(save_segment_path,
+                        segment_list=segment_list,
+                        disable_segment_list=disable_segment_list)
+    if verbose:
+        print(f"Saved processed signals to: {save_signal_path}")
+        print(f"Saved segment list to: {save_segment_path}")
+
+    if draw_segment:
+        total_len = len(segment_list) + len(disable_segment_list)
+        if verbose:
+            print(f"Drawing segments for sample ID {samp_id}, total segments (enable + disable): {total_len}")
+
+
+        draw_tools.draw_psg_label(
+            psg_data=psg_data,
+            psg_label=event_mask["SA_Label"],
+            segment_list=segment_list,
+            save_path=visual_path / f"{samp_id}" / "enable",
+            verbose=verbose,
+            multi_p=multi_p,
+            multi_task_id=multi_task_id
+        )
+
+        draw_tools.draw_psg_label(
+            psg_data=psg_data,
+            psg_label=event_mask["SA_Label"],
+            segment_list=disable_segment_list,
+            save_path=visual_path / f"{samp_id}" / "disable",
+            verbose=verbose,
+            multi_p=multi_p,
+            multi_task_id=multi_task_id
+        )
+
+    # 显式删除大型对象
+    try:
+        del psg_data
+        del normalized_tho_signal, normalized_abd_signal
+        del normalized_flowp_signal, normalized_flowt_signal
+        del normalized_effort_signal
+        del spo2_data_filt, spo2_data_filt_fill
+        del rri
+        del event_mask, event_list
+        del segment_list, disable_segment_list
+    except:
+        pass
+
+    # 强制垃圾回收
+    gc.collect()
+
+
+
+def multiprocess_entry(_progress, task_id, _id):
+    build_HYS_dataset_segment(samp_id=_id, show=False, draw_segment=True, verbose=False, multi_p=_progress, multi_task_id=task_id)
+
+
+def multiprocess_with_tqdm(args_list, n_processes):
+    from concurrent.futures import ProcessPoolExecutor
+    from rich import progress
+
+    with progress.Progress(
+            "[progress.description]{task.description}",
+            progress.BarColumn(),
+            "[progress.percentage]{task.percentage:>3.0f}%",
+            progress.MofNCompleteColumn(),
+            progress.TimeRemainingColumn(),
+            progress.TimeElapsedColumn(),
+            refresh_per_second=1,  # bit slower updates
+            transient=False
+    ) as progress:
+        futures = []
+        with multiprocessing.Manager() as manager:
+            _progress = manager.dict()
+            overall_progress_task = progress.add_task("[green]All jobs progress:")
+            with ProcessPoolExecutor(max_workers=n_processes, mp_context=multiprocessing.get_context("spawn")) as executor:
+                for i_args in range(len(args_list)):
+                    args = args_list[i_args]
+                    task_id = progress.add_task(f"task {i_args}", visible=True)
+                    futures.append(executor.submit(multiprocess_entry, _progress, task_id, args_list[i_args]))
+                # monitor the progress:
+                while (n_finished := sum([future.done() for future in futures])) < len(
+                        futures
+                ):
+                    progress.update(
+                        overall_progress_task, completed=n_finished, total=len(futures)
+                    )
+                    for task_id, update_data in _progress.items():
+                        desc = update_data.get("desc", "")
+                        # update the progress bar for this task:
+                        progress.update(
+                            task_id,
+                            completed=update_data.get("progress", 0),
+                            total=update_data.get("total", 0),
+                            description=desc
+                        )
+
+                # raise any errors:
+                for future in futures:
+                    future.result()
+
+
+def multiprocess_with_pool(args_list, n_processes):
+    """使用Pool，每个worker处理固定数量任务后重启"""
+    from multiprocessing import Pool
+
+    # maxtasksperchild 设置每个worker处理多少任务后重启（释放内存）
+    with Pool(processes=n_processes, maxtasksperchild=2) as pool:
+        results = []
+        for samp_id in args_list:
+            result = pool.apply_async(
+                build_HYS_dataset_segment,
+                args=(samp_id, False, True, False, None, None)
+            )
+            results.append(result)
+
+        # 等待所有任务完成
+        for i, result in enumerate(results):
+            try:
+                result.get()
+                print(f"Completed {i + 1}/{len(args_list)}")
+            except Exception as e:
+                print(f"Error processing task {i}:  {e}")
+
+        pool.close()
+        pool.join()
+
+
+if __name__ == '__main__':
+    yaml_path =  project_root_path / "dataset_config/HYS_PSG_config.yaml"
+
+    conf = utils.load_dataset_conf(yaml_path)
+    select_ids = conf["select_ids"]
+    root_path = Path(conf["root_path"])
+    mask_path = Path(conf["mask_save_path"])
+    save_path = Path(conf["dataset_config"]["dataset_save_path"])
+    visual_path = Path(conf["dataset_config"]["dataset_visual_path"])
+    dataset_config = conf["dataset_config"]
+
+    visual_path.mkdir(parents=True, exist_ok=True)
+
+    save_processed_signal_path = save_path / "Signals"
+    save_processed_signal_path.mkdir(parents=True, exist_ok=True)
+
+    save_segment_list_path = save_path / "Segments_List"
+    save_segment_list_path.mkdir(parents=True, exist_ok=True)
+
+    save_processed_label_path = save_path / "Labels"
+    save_processed_label_path.mkdir(parents=True, exist_ok=True)
+
+    # print(f"select_ids: {select_ids}")
+    # print(f"root_path: {root_path}")
+    # print(f"save_path: {save_path}")
+    # print(f"visual_path: {visual_path}")
+
+    org_signal_root_path = root_path / "OrgBCG_Aligned"
+    psg_signal_root_path = root_path / "PSG_Aligned"
+    print(select_ids)
+
+    # build_HYS_dataset_segment(select_ids[3], show=False, draw_segment=True)
+
+    # for samp_id in select_ids:
+    #     print(f"Processing sample ID: {samp_id}")
+    #     build_HYS_dataset_segment(samp_id, show=False, draw_segment=True)
+
+    # multiprocess_with_tqdm(args_list=select_ids, n_processes=8)
+    multiprocess_with_pool(args_list=select_ids, n_processes=8)
--- a/dataset_builder/HYS_dataset.py
+++ b/dataset_builder/HYS_dataset.py
@ -0,0 +1,233 @@
+import multiprocessing
+import sys
+from pathlib import Path
+
+import os
+import numpy as np
+
+os.environ['DISPLAY'] = "localhost:10.0"
+
+sys.path.append(str(Path(__file__).resolve().parent.parent))
+project_root_path = Path(__file__).resolve().parent.parent
+
+import  utils
+import signal_method
+import draw_tools
+import shutil
+
+
+def build_HYS_dataset_segment(samp_id, show=False, draw_segment=False, verbose=True, multi_p=None, multi_task_id=None):
+    signal_path = list((org_signal_root_path / f"{samp_id}").glob("OrgBCG_Sync_*.txt"))
+    if not signal_path:
+        raise FileNotFoundError(f"OrgBCG_Sync file not found for sample ID: {samp_id}")
+    signal_path = signal_path[0]
+    if verbose:
+        print(f"Processing OrgBCG_Sync signal file: {signal_path}")
+
+    mask_excel_path = Path(mask_path, f"{samp_id}", f"{samp_id}_Processed_Labels.csv")
+    if verbose:
+        print(f"mask_excel_path: {mask_excel_path}")
+
+    event_mask, event_list = utils.read_mask_execl(mask_excel_path)
+
+    bcg_signal_raw, signal_length, signal_fs, signal_second = utils.read_signal_txt(signal_path, dtype=float, verbose=verbose)
+
+    bcg_signal_notch, resp_signal, resp_fs, bcg_signal, bcg_fs = signal_method.signal_filter_split(conf, bcg_signal_raw, signal_fs, verbose=verbose)
+    normalized_resp_signal = signal_method.normalize_resp_signal_by_segment(resp_signal, resp_fs, event_mask["Resp_Movement_Label"], event_list["RespAmpChangeSegment"])
+
+
+    # 如果signal_data采样率过，进行降采样
+    if signal_fs == 1000:
+        bcg_signal_notch = utils.downsample_signal_fast(original_signal=bcg_signal_notch, original_fs=signal_fs, target_fs=100)
+        bcg_signal_raw = utils.downsample_signal_fast(original_signal=bcg_signal_raw, original_fs=signal_fs,
+                                                       target_fs=100)
+        signal_fs = 100
+
+    if bcg_fs == 1000:
+        bcg_signal = utils.downsample_signal_fast(original_signal=bcg_signal, original_fs=bcg_fs, target_fs=100)
+        bcg_fs = 100
+
+    # draw_tools.draw_signal_with_mask(samp_id=samp_id,
+    #                                  signal_data=resp_signal,
+    #                                  signal_fs=resp_fs,
+    #                                  resp_data=normalized_resp_signal,
+    #                                  resp_fs=resp_fs,
+    #                                  bcg_data=bcg_signal,
+    #                                  bcg_fs=bcg_fs,
+    #                                  signal_disable_mask=event_mask["Disable_Label"],
+    #                                  resp_low_amp_mask=event_mask["Resp_LowAmp_Label"],
+    #                                  resp_movement_mask=event_mask["Resp_Movement_Label"],
+    #                                  resp_change_mask=event_mask["Resp_AmpChange_Label"],
+    #                                  resp_sa_mask=event_mask["SA_Label"],
+    #                                  bcg_low_amp_mask=event_mask["BCG_LowAmp_Label"],
+    #                                  bcg_movement_mask=event_mask["BCG_Movement_Label"],
+    #                                  bcg_change_mask=event_mask["BCG_AmpChange_Label"],
+    #                                  show=show,
+    #                                  save_path=None)
+
+    segment_list, disable_segment_list = utils.resp_split(dataset_config, event_mask, event_list, verbose=verbose)
+    if verbose:
+        print(f"Total segments extracted for sample ID {samp_id}: {len(segment_list)}")
+
+
+    # 复制mask到processed_Labels文件夹
+    save_mask_excel_path = save_processed_label_path / f"{samp_id}_Processed_Labels.csv"
+    shutil.copyfile(mask_excel_path, save_mask_excel_path)
+
+    # 复制SA Label_corrected.csv到processed_Labels文件夹
+    sa_label_corrected_path = Path(mask_path, f"{samp_id}", f"{samp_id}_SA Label_corrected.csv")
+    if sa_label_corrected_path.exists():
+        save_sa_label_corrected_path = save_processed_label_path / f"{samp_id}_SA Label_corrected.csv"
+        shutil.copyfile(sa_label_corrected_path, save_sa_label_corrected_path)
+    else:
+        if verbose:
+            print(f"Warning: {sa_label_corrected_path} does not exist.")
+
+    # 保存处理后的信号和截取的片段列表
+    save_signal_path = save_processed_signal_path / f"{samp_id}_Processed_Signals.npz"
+    save_segment_path = save_segment_list_path / f"{samp_id}_Segment_List.npz"
+
+    bcg_data = {
+        "bcg_signal_notch": {
+            "name": "BCG_Signal_Notch",
+            "data": bcg_signal_notch,
+            "fs": signal_fs,
+            "length": len(bcg_signal_notch),
+            "second": len(bcg_signal_notch) // signal_fs
+        },
+        "bcg_signal":{
+            "name": "BCG_Signal_Raw",
+            "data": bcg_signal,
+            "fs": bcg_fs,
+            "length": len(bcg_signal),
+            "second": len(bcg_signal) // bcg_fs
+        },
+        "resp_signal": {
+            "name": "Resp_Signal",
+            "data": normalized_resp_signal,
+            "fs": resp_fs,
+            "length": len(normalized_resp_signal),
+            "second": len(normalized_resp_signal) // resp_fs
+        }
+    }
+
+    np.savez_compressed(save_signal_path, **bcg_data)
+    np.savez_compressed(save_segment_path,
+                        segment_list=segment_list,
+                        disable_segment_list=disable_segment_list)
+    if verbose:
+        print(f"Saved processed signals to: {save_signal_path}")
+        print(f"Saved segment list to: {save_segment_path}")
+
+    if draw_segment:
+        psg_data = utils.read_psg_channel(psg_signal_root_path / f"{samp_id}", [1, 2, 3, 4, 5, 6, 7, 8], verbose=verbose)
+        psg_data["HR"] = {
+            "name": "HR",
+            "data":  signal_method.rpeak2hr(psg_data["Rpeak"]["data"], psg_data["ECG_Sync"]["length"], psg_data["Rpeak"]["fs"]),
+            "fs": psg_data["ECG_Sync"]["fs"],
+            "length": psg_data["ECG_Sync"]["length"],
+            "second": psg_data["ECG_Sync"]["second"]
+        }
+
+
+        psg_label = utils.read_psg_label(sa_label_corrected_path, verbose=verbose)
+        psg_event_mask, _ = utils.generate_event_mask(event_df=psg_label, signal_second=psg_data["ECG_Sync"]["second"], use_correct=False)
+        total_len = len(segment_list) + len(disable_segment_list)
+        if verbose:
+            print(f"Drawing segments for sample ID {samp_id}, total segments (enable + disable): {total_len}")
+
+        # draw_tools.draw_psg_bcg_label(psg_data=psg_data,
+        #                               psg_label=psg_event_mask,
+        #                               bcg_data=bcg_data,
+        #                               event_mask=event_mask,
+        #                               segment_list=segment_list,
+        #                               save_path=visual_path / f"{samp_id}" / "enable",
+        #                               verbose=verbose,
+        #                               multi_p=multi_p,
+        #                               multi_task_id=multi_task_id
+        #                               )
+        #
+        # draw_tools.draw_psg_bcg_label(
+        #     psg_data=psg_data,
+        #     psg_label=psg_event_mask,
+        #     bcg_data=bcg_data,
+        #     event_mask=event_mask,
+        #     segment_list=disable_segment_list,
+        #     save_path=visual_path / f"{samp_id}" / "disable",
+        #     verbose=verbose,
+        #     multi_p=multi_p,
+        #     multi_task_id=multi_task_id
+        # )
+
+
+
+def multiprocess_entry(_progress, task_id, _id):
+    build_HYS_dataset_segment(samp_id=_id, show=False, draw_segment=True, verbose=False, multi_p=_progress, multi_task_id=task_id)
+
+
+def multiprocess_with_pool(args_list, n_processes):
+    """使用Pool，每个worker处理固定数量任务后重启"""
+    from multiprocessing import Pool
+
+    # maxtasksperchild 设置每个worker处理多少任务后重启（释放内存）
+    with Pool(processes=n_processes, maxtasksperchild=2) as pool:
+        results = []
+        for samp_id in args_list:
+            result = pool.apply_async(
+                build_HYS_dataset_segment,
+                args=(samp_id, False, True, False, None, None)
+            )
+            results.append(result)
+
+        # 等待所有任务完成
+        for i, result in enumerate(results):
+            try:
+                result.get()
+                print(f"Completed {i + 1}/{len(args_list)}")
+            except Exception as e:
+                print(f"Error processing task {i}:  {e}")
+
+        pool.close()
+        pool.join()
+
+
+
+if __name__ == '__main__':
+    yaml_path =  project_root_path / "dataset_config/HYS_config.yaml"
+
+    conf = utils.load_dataset_conf(yaml_path)
+    select_ids = conf["select_ids"]
+    root_path = Path(conf["root_path"])
+    mask_path = Path(conf["mask_save_path"])
+    save_path = Path(conf["dataset_config"]["dataset_save_path"])
+    visual_path = Path(conf["dataset_config"]["dataset_visual_path"])
+    dataset_config = conf["dataset_config"]
+
+    visual_path.mkdir(parents=True, exist_ok=True)
+
+    save_processed_signal_path = save_path / "Signals"
+    save_processed_signal_path.mkdir(parents=True, exist_ok=True)
+
+    save_segment_list_path = save_path / "Segments_List"
+    save_segment_list_path.mkdir(parents=True, exist_ok=True)
+
+    save_processed_label_path = save_path / "Labels"
+    save_processed_label_path.mkdir(parents=True, exist_ok=True)
+
+    # print(f"select_ids: {select_ids}")
+    # print(f"root_path: {root_path}")
+    # print(f"save_path: {save_path}")
+    # print(f"visual_path: {visual_path}")
+
+    org_signal_root_path = root_path / "OrgBCG_Aligned"
+    psg_signal_root_path = root_path / "PSG_Aligned"
+    print(select_ids)
+
+    # build_HYS_dataset_segment(select_ids[3], show=False, draw_segment=True)
+
+    # for samp_id in select_ids:
+    #     print(f"Processing sample ID: {samp_id}")
+    #     build_HYS_dataset_segment(samp_id, show=False, draw_segment=True)
+
+    # multiprocess_with_tqdm(args_list=select_ids, n_processes=16)
+    multiprocess_with_pool(args_list=select_ids, n_processes=16)
--- a/dataset_builder/init.py
+++ b/dataset_builder/init.py
--- a/dataset_config/HYS_PSG_config.yaml
+++ b/dataset_config/HYS_PSG_config.yaml
@ -0,0 +1,139 @@
+select_ids:
+  - 54
+  - 88
+  - 220
+  - 221
+  - 229
+  - 282
+  - 286
+  - 541
+  - 579
+  - 582
+  - 670
+  - 671
+  - 683
+  - 684
+  - 735
+  - 933
+  - 935
+  - 950
+  - 952
+  - 960
+  - 962
+  - 967
+  - 1302
+
+root_path: /mnt/disk_wd/marques_dataset/DataCombine2023/HYS
+mask_save_path: /mnt/disk_code/marques/dataprepare/output/HYS_PSG
+
+target_fs: 100
+
+dataset_config:
+  window_sec: 180
+  stride_sec: 60
+  dataset_save_path: /mnt/disk_wd/marques_dataset/SA_dataset/HYS_PSG_dataset
+  dataset_visual_path: /mnt/disk_wd/marques_dataset/SA_dataset/HYS_PSG_dataset/visualization
+
+
+effort:
+  downsample_fs: 10
+
+effort_filter:
+    filter_type: bandpass
+    low_cut: 0.05
+    high_cut: 0.5
+    order: 3
+
+average_filter:
+  window_size_sec: 20
+
+flow:
+  downsample_fs: 10
+
+flow_filter:
+    filter_type: bandpass
+    low_cut: 0.05
+    high_cut: 0.5
+    order: 3
+
+spo2_fill__anomaly:
+  max_fill_duration: 30
+  min_gap_duration: 10
+  nan_to_num_value: 95
+
+#ecg:
+#  downsample_fs: 100
+#
+#ecg_filter:
+#    filter_type: bandpass
+#    low_cut: 0.5
+#    high_cut: 40
+#    order: 5
+
+
+#resp:
+#  downsample_fs_1: None
+#  downsample_fs_2: 10
+#
+#resp_filter:
+#  filter_type: bandpass
+#  low_cut: 0.05
+#  high_cut: 0.5
+#  order: 3
+#
+#resp_low_amp:
+#  window_size_sec: 30
+#  stride_sec:
+#  amplitude_threshold: 3
+#  merge_gap_sec: 60
+#  min_duration_sec: 60
+#
+#resp_movement:
+#  window_size_sec: 20
+#  stride_sec: 1
+#  std_median_multiplier: 4
+#  compare_intervals_sec:
+#    - 60
+#    - 120
+##    - 180
+#  interval_multiplier: 3
+#  merge_gap_sec: 30
+#  min_duration_sec: 1
+#
+#resp_movement_revise:
+#  up_interval_multiplier: 3
+#  down_interval_multiplier: 2
+#  compare_intervals_sec: 30
+#  merge_gap_sec: 10
+#  min_duration_sec: 1
+#
+#resp_amp_change:
+#  mav_calc_window_sec: 4
+#  threshold_amplitude: 0.25
+#  threshold_energy: 0.4
+#
+#
+#bcg:
+#  downsample_fs: 100
+#
+#bcg_filter:
+#  filter_type: bandpass
+#  low_cut: 1
+#  high_cut: 10
+#  order: 10
+#
+#bcg_low_amp:
+#  window_size_sec: 1
+#  stride_sec:
+#  amplitude_threshold: 8
+#  merge_gap_sec: 20
+#  min_duration_sec: 3
+#
+#
+#bcg_movement:
+#  window_size_sec: 2
+#  stride_sec:
+#  merge_gap_sec: 20
+#  min_duration_sec: 4
+
+
--- a/dataset_config/HYS_config.yaml
+++ b/dataset_config/HYS_config.yaml
@ -0,0 +1,86 @@
+select_ids:
+  - 1302
+  - 286
+  - 950
+  - 220
+  - 229
+  - 541
+  - 582
+  - 670
+  - 684
+  - 960
+
+root_path: /mnt/disk_wd/marques_dataset/DataCombine2023/HYS
+mask_save_path: /mnt/disk_code/marques/dataprepare/output/HYS
+
+resp:
+  downsample_fs_1: 100
+  downsample_fs_2: 10
+
+resp_filter:
+  filter_type: bandpass
+  low_cut: 0.05
+  high_cut: 0.6
+  order: 3
+
+resp_low_amp:
+  window_size_sec: 30
+  stride_sec:
+  amplitude_threshold: 3
+  merge_gap_sec: 60
+  min_duration_sec: 60
+
+resp_movement:
+  window_size_sec: 20
+  stride_sec: 1
+  std_median_multiplier: 4
+  compare_intervals_sec:
+    - 60
+    - 120
+#    - 180
+  interval_multiplier: 3
+  merge_gap_sec: 30
+  min_duration_sec: 1
+
+resp_movement_revise:
+  up_interval_multiplier: 3
+  down_interval_multiplier: 2
+  compare_intervals_sec: 30
+  merge_gap_sec: 10
+  min_duration_sec: 1
+
+resp_amp_change:
+  mav_calc_window_sec: 4
+  threshold_amplitude: 0.25
+  threshold_energy: 0.4
+
+
+bcg:
+  downsample_fs: 100
+
+bcg_filter:
+  filter_type: bandpass
+  low_cut: 1
+  high_cut: 10
+  order: 10
+
+bcg_low_amp:
+  window_size_sec: 1
+  stride_sec:
+  amplitude_threshold: 8
+  merge_gap_sec: 20
+  min_duration_sec: 3
+
+
+bcg_movement:
+  window_size_sec: 2
+  stride_sec:
+  merge_gap_sec: 20
+  min_duration_sec: 4
+
+
+dataset_config:
+  window_sec: 180
+  stride_sec: 60
+  dataset_save_path: /mnt/disk_wd/marques_dataset/SA_dataset/HYS_dataset
+  dataset_visual_path: /mnt/disk_wd/marques_dataset/SA_dataset/HYS_dataset/visualization
--- a/dataset_config/RESP_PAIR_HYS_config.yaml
+++ b/dataset_config/RESP_PAIR_HYS_config.yaml
@ -0,0 +1,56 @@
+select_ids:
+  - 1000
+  - 1004
+  - 1006
+  - 1009
+  - 1010
+  - 1300
+  - 1301
+  - 1302
+  - 1308
+  - 1314
+  - 1354
+  - 1374
+  - 1378
+  - 1478
+  - 220
+  - 221
+  - 229
+  - 282
+  - 285
+  - 286
+  - 54
+  - 541
+  - 579
+  - 582
+  - 670
+  - 671
+  - 683
+  - 684
+  - 686
+  - 703
+  - 704
+  - 726
+  - 735
+  - 736
+  - 88
+  - 893
+  - 933
+  - 935
+  - 939
+  - 950
+  - 952
+  - 954
+  - 955
+  - 956
+  - 960
+  - 961
+  - 962
+  - 967
+  - 969
+  - 971
+  - 972
+
+
+root_path: /mnt/disk_wd/marques_dataset/DataCombine2023/HYS
+pair_file_path: /mnt/disk_wd/marques_dataset/Resp_Pair_Dataset/HYS/Raw
--- a/dataset_config/RESP_PAIR_ZD5Y_config.yaml
+++ b/dataset_config/RESP_PAIR_ZD5Y_config.yaml
@ -0,0 +1,32 @@
+select_ids:
+  - 3103
+  - 3105
+  - 3106
+  - 3107
+  - 3108
+  - 3110
+  - 3203
+  - 3204
+  - 3205
+  - 3209
+  - 3211
+  - 3301
+  - 3303
+  - 3304
+  - 3307
+  - 3308
+  - 3309
+  - 3403
+  - 3405
+  - 3406
+  - 3407
+  - 3408
+  - 3504
+
+
+root_path: /mnt/disk_wd/marques_dataset/DataCombine2023/ZD5Y
+pair_file_path: /mnt/disk_wd/marques_dataset/Resp_Pair_Dataset/ZD5Y/Raw
+
+
+
+
--- a/dataset_config/SHHS1_config.yaml
+++ b/dataset_config/SHHS1_config.yaml
@ -0,0 +1,41 @@
+root_path: /mnt/disk_wd/marques_dataset/shhs/polysomnography/shhs1
+mask_save_path: /mnt/disk_code/marques/dataprepare/output/shhs1
+
+effort_target_fs: 10
+ecg_target_fs: 100
+
+
+dataset_config:
+  window_sec: 180
+  stride_sec: 60
+  dataset_save_path: /mnt/disk_wd/marques_dataset/SA_dataset/SHHS1_dataset
+  dataset_visual_path: /mnt/disk_wd/marques_dataset/SA_dataset/SHHS1_dataset/visualization
+
+
+effort:
+  downsample_fs: 10
+
+effort_filter:
+    filter_type: bandpass
+    low_cut: 0.05
+    high_cut: 0.5
+    order: 3
+
+average_filter:
+  window_size_sec: 20
+
+flow:
+  downsample_fs: 10
+
+flow_filter:
+    filter_type: bandpass
+    low_cut: 0.05
+    high_cut: 0.5
+    order: 3
+
+spo2_fill__anomaly:
+  max_fill_duration: 30
+  min_gap_duration: 10
+  nan_to_num_value: 95
+
+
--- a/dataset_config/ZD5Y_config.yaml
+++ b/dataset_config/ZD5Y_config.yaml
@ -0,0 +1,88 @@
+select_ids:
+  - 3103
+  - 3105
+  - 3106
+  - 3107
+  - 3108
+  - 3110
+  - 3203
+  - 3204
+  - 3205
+  - 3207
+  - 3208
+  - 3209
+  - 3212
+  - 3301
+  - 3303
+  - 3307
+  - 3403
+  - 3504
+
+root_path: /mnt/disk_wd/marques_dataset/DataCombine2023/ZD5Y
+save_path: /mnt/disk_code/marques/dataprepare/output/ZD5Y
+
+resp:
+  downsample_fs_1: 100
+  downsample_fs_2: 10
+
+resp_filter:
+  filter_type: bandpass
+  low_cut: 0.01
+  high_cut: 0.7
+  order: 3
+
+resp_low_amp:
+  window_size_sec: 30
+  stride_sec:
+  amplitude_threshold: 3
+  merge_gap_sec: 60
+  min_duration_sec: 60
+
+resp_movement:
+  window_size_sec: 20
+  stride_sec: 1
+  std_median_multiplier: 5
+  compare_intervals_sec:
+    - 60
+    - 120
+#    - 180
+  interval_multiplier: 3.5
+  merge_gap_sec: 30
+  min_duration_sec: 1
+
+resp_movement_revise:
+    up_interval_multiplier: 3
+    down_interval_multiplier: 2
+    compare_intervals_sec: 30
+    merge_gap_sec: 10
+    min_duration_sec: 1
+
+resp_amp_change:
+  mav_calc_window_sec: 1
+  threshold_amplitude: 0.25
+  threshold_energy: 0.4
+
+
+bcg:
+    downsample_fs: 100
+
+bcg_filter:
+  filter_type: bandpass
+  low_cut: 1
+  high_cut: 10
+  order: 10
+
+bcg_low_amp:
+  window_size_sec: 1
+  stride_sec:
+  amplitude_threshold: 8
+  merge_gap_sec: 20
+  min_duration_sec: 3
+
+
+bcg_movement:
+    window_size_sec: 2
+    stride_sec:
+    merge_gap_sec: 20
+    min_duration_sec: 4
+
--- a/dataset_tools/resp_pair_copy.py
+++ b/dataset_tools/resp_pair_copy.py
@ -0,0 +1,67 @@
+from pathlib import Path
+import sys
+sys.path.append(str(Path(__file__).resolve().parent.parent))
+project_root_path = Path(__file__).resolve().parent.parent
+import utils
+import shutil
+
+def copy_one_resp_pair(one_id):
+    sync_type = "Sync"
+
+    org_bcg_file_path = sync_bcg_path / f"{one_id}"
+    dest_bcg_file_path = pair_file_path / f"{one_id}"
+    dest_bcg_file_path.mkdir(parents=True, exist_ok=True)
+    if not list(org_bcg_file_path.glob("OrgBCG_Sync_*.txt")):
+        if not list(org_bcg_file_path.glob("OrgBCG_RoughCut_*.txt")):
+            print(f"No OrgBCG files found for ID {one_id}.")
+            return
+        else:
+            sync_type = "RoughCut"
+            print(f"Using RoughCut files for ID {one_id}.")
+
+
+    for file in org_bcg_file_path.glob(f"OrgBCG_{sync_type}_*.txt"):
+        shutil.copyfile(file, dest_bcg_file_path / f"{one_id}_{file.name}".replace("_RoughCut", "").replace("_Sync", ""))
+    psg_file_path = sync_psg_path / f"{one_id}"
+    dest_psg_file_path = pair_file_path / f"{one_id}"
+    dest_psg_file_path.mkdir(parents=True, exist_ok=True)
+
+    # 检查上面的文件是否存在
+    psg_file_patterns = [
+        f"5_class_{sync_type}_*.txt",
+        f"Effort Abd_{sync_type}_*.txt",
+        f"Effort Tho_{sync_type}_*.txt",
+        f"Flow P_{sync_type}_*.txt",
+        f"Flow T_{sync_type}_*.txt",
+        f"SA Label_Sync.csv",
+        f"SpO2_{sync_type}_*.txt"
+    ]
+    for pattern in psg_file_patterns:
+        if not list(psg_file_path.glob(pattern)):
+            print(f"No PSG files found for ID {one_id} with pattern {pattern}.")
+            return
+    for pattern in psg_file_patterns:
+        for file in psg_file_path.glob(pattern):
+            shutil.copyfile(file, dest_psg_file_path / f"{one_id}_{file.name.replace('_RoughCut', '').replace('_Sync', '')}")
+
+
+
+
+
+
+
+if __name__ == '__main__':
+    yaml_path =  project_root_path / "dataset_config/RESP_PAIR_ZD5Y_config.yaml"
+
+    conf = utils.load_dataset_conf(yaml_path)
+    select_ids = conf["select_ids"]
+    root_path = Path(conf["root_path"])
+    sync_bcg_path = root_path / "OrgBCG_Aligned"
+    sync_psg_path = root_path / "PSG_Aligned"
+    pair_file_path = Path(conf["pair_file_path"])
+
+    # copy_one_resp_pair(961)
+
+    for samp_id in select_ids:
+        print(f"Processing {samp_id}...")
+        copy_one_resp_pair(samp_id)
--- a/dataset_tools/shhs_annotations_check.py
+++ b/dataset_tools/shhs_annotations_check.py
@ -0,0 +1,100 @@
+
+import argparse
+from pathlib import Path
+from lxml import etree
+from tqdm import tqdm
+from collections import Counter
+
+
+def main():
+    # 设定目标文件夹路径，你可以修改这里的路径，或者运行脚本时手动输入
+    # 默认为当前目录 '.'
+    # target_dir =  "/mnt/disk_wd/marques_dataset/shhs/polysomnography/annotations-events-nsrr/shhs1"
+    target_dir =  "/mnt/disk_wd/marques_dataset/shhs/polysomnography/annotations-events-nsrr/shhs2"
+
+    folder_path = Path(target_dir)
+
+    if not folder_path.exists():
+        print(f"错误: 路径 '{folder_path}' 不存在。")
+        return
+
+    # 1. 获取所有 XML 文件 (扁平结构，不递归子目录)
+    xml_files = list(folder_path.glob("*.xml"))
+    total_files = len(xml_files)
+
+    if total_files == 0:
+        print(f"在 '{folder_path}' 中没有找到 XML 文件。")
+        return
+
+    print(f"找到 {total_files} 个 XML 文件，准备开始处理...")
+
+    # 用于统计 (EventType, EventConcept) 组合的计数器
+    stats_counter = Counter()
+
+    # 2. 遍历文件，使用 tqdm 显示进度条
+    for xml_file in tqdm(xml_files, desc="Processing XMLs", unit="file"):
+        try:
+            # 使用 lxml 解析
+            tree = etree.parse(str(xml_file))
+            root = tree.getroot()
+
+            # 3. 定位到 ScoredEvent 节点
+            # SHHS XML 结构通常是: PSGAnnotation -> ScoredEvents -> ScoredEvent
+            # 我们直接查找所有的 ScoredEvent 节点
+            events = root.findall(".//ScoredEvent")
+
+            for event in events:
+                # 提取 EventType
+                type_node = event.find("EventType")
+                # 处理节点不存在或文本为空的情况
+                e_type = type_node.text.strip() if (type_node is not None and type_node.text) else "N/A"
+
+                # 提取 EventConcept
+                concept_node = event.find("EventConcept")
+                e_concept = concept_node.text.strip() if (concept_node is not None and concept_node.text) else "N/A"
+
+                # 4. 组合并计数
+                # 组合键为元组 (EventType, EventConcept)
+                key = (e_type, e_concept)
+                stats_counter[key] += 1
+
+        except etree.XMLSyntaxError:
+            print(f"\n[警告] 文件格式错误，跳过: {xml_file.name}")
+        except Exception as e:
+            print(f"\n[错误] 处理文件 {xml_file.name} 时出错: {e}")
+
+    # 5. 打印结果到终端
+    if stats_counter:
+        # --- 动态计算列宽 ---
+        # 获取所有 EventType 的最大长度，默认长度 9
+        max_type_width = max((len(k[0]) for k in stats_counter.keys()), default=9)
+        max_type_width = max(max_type_width, 9)
+
+        # 获取所有 EventConcept 的最大长度，默认长度 12
+        max_conc_width = max((len(k[1]) for k in stats_counter.keys()), default=12)
+        max_conc_width = max(max_conc_width, 12)
+
+        # 计算表格总宽度
+        total_line_width = max_type_width + max_conc_width + 10 + 6
+
+        print("\n" + "=" * total_line_width)
+        print(f"{'EventType':<{max_type_width}} | {'EventConcept':<{max_conc_width}} | {'Count':>10}")
+        print("-" * total_line_width)
+
+        # --- 修改处：按名称排序 ---
+        # sorted() 默认会对元组 (EventType, EventConcept) 进行字典序排序
+        # 即先按 EventType A-Z 排序，再按 EventConcept A-Z 排序
+        for (e_type, e_concept), count in sorted(stats_counter.items()):
+            print(f"{e_type:<{max_type_width}} | {e_concept:<{max_conc_width}} | {count:>10}")
+
+        print("=" * total_line_width)
+
+    else:
+        print("\n未提取到任何事件数据。")
+
+    print("=" * 90)
+    print(f"统计完成。共扫描 {total_files} 个文件。")
+
+
+if __name__ == "__main__":
+    main()
--- a/draw_tools/init.py
+++ b/draw_tools/init.py
@ -0,0 +1,2 @@
+from .draw_statics import draw_signal_with_mask, draw_psg_signal
+from .draw_label import draw_psg_bcg_label,draw_psg_label
--- a/draw_tools/draw_label.py
+++ b/draw_tools/draw_label.py
@ -0,0 +1,337 @@
+from matplotlib.axes import Axes
+from matplotlib.gridspec import GridSpec
+from matplotlib.colors import PowerNorm
+import matplotlib.pyplot as plt
+import matplotlib.patches as mpatches
+import seaborn as sns
+import numpy as np
+from tqdm.rich import tqdm
+import utils
+import gc
+#  添加with_prediction参数
+
+psg_bcg_chn_name2ax = {
+    "SpO2": 0,
+    "Flow T": 1,
+    "Flow P": 2,
+    "Effort Tho": 3,
+    "Effort Abd": 4,
+    "HR": 5,
+    "resp": 6,
+    "bcg": 7,
+    "Stage": 8,
+    "resp_twinx": 9,
+    "bcg_twinx": 10,
+}
+
+psg_chn_name2ax = {
+    "SpO2": 0,
+    "Flow T": 1,
+    "Flow P": 2,
+    "Effort Tho": 3,
+    "Effort Abd": 4,
+    "Effort": 5,
+    "HR": 6,
+    "RRI": 7,
+    "Stage": 8,
+}
+
+
+resp_chn_name2ax = {
+    "resp": 0,
+    "bcg": 1,
+}
+
+
+def create_psg_bcg_figure():
+    fig = plt.figure(figsize=(12, 8), dpi=200)
+    gs = GridSpec(9, 1, height_ratios=[1, 1, 1, 1, 1, 1, 3, 2, 1])
+    fig.subplots_adjust(top=0.98, bottom=0.05, right=0.98, left=0.1, hspace=0, wspace=0)
+    axes = []
+    for i in range(9):
+        ax = fig.add_subplot(gs[i])
+        axes.append(ax)
+
+    axes[psg_bcg_chn_name2ax["SpO2"]].grid(True)
+    # axes[0].xaxis.set_major_formatter(Params.FORMATTER)
+    axes[psg_bcg_chn_name2ax["SpO2"]].set_ylim((85, 100))
+    axes[psg_bcg_chn_name2ax["SpO2"]].tick_params(axis='x', colors="white")
+
+    axes[psg_bcg_chn_name2ax["Flow T"]].grid(True)
+    # axes[1].xaxis.set_major_formatter(Params.FORMATTER)
+    axes[psg_bcg_chn_name2ax["Flow T"]].tick_params(axis='x', colors="white")
+
+    axes[psg_bcg_chn_name2ax["Flow P"]].grid(True)
+    # axes[2].xaxis.set_major_formatter(Params.FORMATTER)
+    axes[psg_bcg_chn_name2ax["Flow P"]].tick_params(axis='x', colors="white")
+
+    axes[psg_bcg_chn_name2ax["Effort Tho"]].grid(True)
+    # axes[3].xaxis.set_major_formatter(Params.FORMATTER)
+    axes[psg_bcg_chn_name2ax["Effort Tho"]].tick_params(axis='x', colors="white")
+
+    axes[psg_bcg_chn_name2ax["Effort Abd"]].grid(True)
+    # axes[4].xaxis.set_major_formatter(Params.FORMATTER)
+    axes[psg_bcg_chn_name2ax["Effort Abd"]].tick_params(axis='x', colors="white")
+
+    axes[psg_bcg_chn_name2ax["HR"]].grid(True)
+    axes[psg_bcg_chn_name2ax["HR"]].tick_params(axis='x', colors="white")
+
+    axes[psg_bcg_chn_name2ax["resp"]].grid(True)
+    axes[psg_bcg_chn_name2ax["resp"]].tick_params(axis='x', colors="white")
+    axes.append(axes[psg_bcg_chn_name2ax["resp"]].twinx())
+
+    axes[psg_bcg_chn_name2ax["bcg"]].grid(True)
+    # axes[5].xaxis.set_major_formatter(Params.FORMATTER)
+    axes[psg_bcg_chn_name2ax["bcg"]].tick_params(axis='x', colors="white")
+    axes.append(axes[psg_bcg_chn_name2ax["bcg"]].twinx())
+
+    axes[psg_bcg_chn_name2ax["Stage"]].grid(True)
+    # axes[7].xaxis.set_major_formatter(Params.FORMATTER)
+
+    return fig, axes
+
+
+def create_psg_figure():
+    fig = plt.figure(figsize=(12, 8), dpi=200)
+    gs = GridSpec(9, 1, height_ratios=[1, 1, 1, 1, 1, 1, 1, 1, 1])
+    fig.subplots_adjust(top=0.98, bottom=0.05, right=0.98, left=0.1, hspace=0, wspace=0)
+    axes = []
+    for i in range(9):
+        ax = fig.add_subplot(gs[i])
+        axes.append(ax)
+
+    axes[psg_chn_name2ax["SpO2"]].grid(True)
+    # axes[0].xaxis.set_major_formatter(Params.FORMATTER)
+    axes[psg_chn_name2ax["SpO2"]].set_ylim((85, 100))
+    axes[psg_chn_name2ax["SpO2"]].tick_params(axis='x', colors="white")
+
+    axes[psg_chn_name2ax["Flow T"]].grid(True)
+    # axes[1].xaxis.set_major_formatter(Params.FORMATTER)
+    axes[psg_chn_name2ax["Flow T"]].tick_params(axis='x', colors="white")
+
+    axes[psg_chn_name2ax["Flow P"]].grid(True)
+    # axes[2].xaxis.set_major_formatter(Params.FORMATTER)
+    axes[psg_chn_name2ax["Flow P"]].tick_params(axis='x', colors="white")
+
+    axes[psg_chn_name2ax["Effort Tho"]].grid(True)
+    # axes[3].xaxis.set_major_formatter(Params.FORMATTER)
+    axes[psg_chn_name2ax["Effort Tho"]].tick_params(axis='x', colors="white")
+
+    axes[psg_chn_name2ax["Effort Abd"]].grid(True)
+    # axes[4].xaxis.set_major_formatter(Params.FORMATTER)
+    axes[psg_chn_name2ax["Effort Abd"]].tick_params(axis='x', colors="white")
+
+    axes[psg_chn_name2ax["Effort"]].grid(True)
+    axes[psg_chn_name2ax["Effort"]].tick_params(axis='x', colors="white")
+
+    axes[psg_chn_name2ax["HR"]].grid(True)
+    axes[psg_chn_name2ax["HR"]].tick_params(axis='x', colors="white")
+
+    axes[psg_chn_name2ax["RRI"]].grid(True)
+    axes[psg_chn_name2ax["RRI"]].tick_params(axis='x', colors="white")
+
+
+    axes[psg_chn_name2ax["Stage"]].grid(True)
+
+
+    return fig, axes
+
+def create_resp_figure():
+    fig = plt.figure(figsize=(12, 6), dpi=100)
+    gs = GridSpec(2, 1, height_ratios=[3, 2])
+    fig.subplots_adjust(top=0.98, bottom=0.05, right=0.98, left=0.1, hspace=0, wspace=0)
+    axes = []
+    for i in range(2):
+        ax = fig.add_subplot(gs[i])
+        axes.append(ax)
+
+    axes[0].grid(True)
+    # axes[0].xaxis.set_major_formatter(Params.FORMATTER)
+    axes[0].tick_params(axis='x', colors="white")
+
+    axes[1].grid(True)
+    # axes[1].xaxis.set_major_formatter(Params.FORMATTER)
+    axes[1].tick_params(axis='x', colors="white")
+
+    return fig, axes
+
+
+def plt_signal_label_on_ax(ax: Axes, signal_data, segment_start, segment_end, event_mask=None,
+                           event_codes: list[int] = None, multi_labels=None, ax2: Axes = None):
+    signal_fs = signal_data["fs"]
+    chn_signal = signal_data["data"]
+    time_axis = np.linspace(segment_start, segment_end, (segment_end - segment_start) * signal_fs)
+    ax.plot(time_axis, chn_signal[segment_start * signal_fs:segment_end * signal_fs], color='black',
+            label=signal_data["name"])
+    if event_mask is not None:
+        if multi_labels is None and event_codes is not None:
+            for event_code in event_codes:
+                mask = event_mask[segment_start:segment_end].repeat(signal_fs) == event_code
+                y = (chn_signal[segment_start * signal_fs:segment_end * signal_fs] * mask).astype(float)
+                np.place(y, y == 0, np.nan)
+                ax.plot(time_axis, y, color=utils.ColorCycle[event_code])
+        elif multi_labels == "resp" and event_codes is not None:
+            ax.set_ylim(-6, 6)
+            # 建立第二个y轴坐标
+            ax2.cla()
+            ax2.plot(time_axis, event_mask["Resp_LowAmp_Label"][segment_start:segment_end].repeat(signal_fs) * -1,
+                     color='blue', alpha=0.8, label='Low Amplitude Mask')
+            ax2.plot(time_axis, event_mask["Resp_Movement_Label"][segment_start:segment_end].repeat(signal_fs) * -2,
+                     color='orange', alpha=0.8, label='Movement Mask')
+            ax2.plot(time_axis, event_mask["Resp_AmpChange_Label"][segment_start:segment_end].repeat(signal_fs) * -3,
+                     color='green', alpha=0.8, label='Amplitude Change Mask')
+            for event_code in event_codes:
+                sa_mask = event_mask["SA_Label"][segment_start:segment_end].repeat(signal_fs) == event_code
+                score_mask = event_mask["SA_Score"][segment_start:segment_end].repeat(signal_fs)
+                # y = (sa_mask * score_mask).astype(float)
+                y = (chn_signal[segment_start * signal_fs:segment_end * signal_fs] * sa_mask).astype(float)
+                np.place(y, y == 0, np.nan)
+                ax.plot(time_axis, y, color=utils.ColorCycle[event_code])
+                ax2.plot(time_axis, score_mask, color="orange")
+            ax2.set_ylim(-4, 5)
+        elif multi_labels == "bcg" and event_codes is not None:
+            # 建立第二个y轴坐标
+            ax2.cla()
+            ax2.plot(time_axis, event_mask["BCG_LowAmp_Label"][segment_start:segment_end].repeat(signal_fs) * -1,
+                     color='blue', alpha=0.8, label='Low Amplitude Mask')
+            ax2.plot(time_axis, event_mask["BCG_Movement_Label"][segment_start:segment_end].repeat(signal_fs) * -2,
+                     color='orange', alpha=0.8, label='Movement Mask')
+            ax2.plot(time_axis, event_mask["BCG_AmpChange_Label"][segment_start:segment_end].repeat(signal_fs) * -3,
+                     color='green', alpha=0.8, label='Amplitude Change Mask')
+
+            ax2.set_ylim(-4, 4)
+
+    ax.set_ylabel("Amplitude")
+    ax.legend(loc=1)
+
+
+def plt_stage_on_ax(ax, stage_data, segment_start, segment_end):
+    stage_signal = stage_data["data"]
+    stage_fs = stage_data["fs"]
+    time_axis = np.linspace(segment_start, segment_end, (segment_end - segment_start) * stage_fs)
+    ax.plot(time_axis, stage_signal[segment_start * stage_fs:segment_end * stage_fs], color='black', label=stage_data["name"])
+    ax.set_ylim(0, 6)
+    ax.set_yticks([1, 2, 3, 4, 5])
+    ax.set_yticklabels(["N3", "N2", "N1", "REM", "Awake"])
+    ax.set_ylabel("Stage")
+    ax.legend(loc=1)
+
+
+def plt_spo2_on_ax(ax: Axes, spo2_data, segment_start, segment_end):
+    spo2_signal = spo2_data["data"]
+    spo2_fs = spo2_data["fs"]
+    time_axis = np.linspace(segment_start, segment_end, (segment_end - segment_start) * spo2_fs)
+    ax.plot(time_axis, spo2_signal[segment_start * spo2_fs:segment_end * spo2_fs], color='black', label=spo2_data["name"])
+
+    if spo2_signal[segment_start * spo2_fs:segment_end * spo2_fs].min() < 85:
+        ax.set_ylim((spo2_signal[segment_start * spo2_fs:segment_end * spo2_fs].min() - 5, 100))
+    else:
+        ax.set_ylim((85, 100))
+    ax.set_ylabel("SpO2 (%)")
+    ax.legend(loc=1)
+
+
+def score_mask2alpha(score_mask):
+    alpha_mask = np.zeros_like(score_mask, dtype=float)
+    alpha_mask[score_mask <= 0] = 0
+    alpha_mask[score_mask == 1] = 0.9
+    alpha_mask[score_mask == 2] = 0.6
+    alpha_mask[score_mask == 3] = 0.1
+    return alpha_mask
+
+
+def draw_psg_bcg_label(psg_data, psg_label, bcg_data, event_mask, segment_list, save_path=None, verbose=True,
+                       multi_p=None, multi_task_id=None):
+    if save_path is not None:
+        save_path.mkdir(parents=True, exist_ok=True)
+
+    for mask in event_mask.keys():
+        if mask.startswith("Resp_") or mask.startswith("BCG_"):
+            event_mask[mask] = utils.none_to_nan_mask(event_mask[mask], 0)
+
+    event_mask["SA_Score"] = utils.none_to_nan_mask(event_mask["SA_Score"], 0)
+
+    # event_mask["SA_Score_Alpha"] = score_mask2alpha(event_mask["SA_Score"])
+    # event_mask["SA_Score_Alpha"] = utils.none_to_nan_mask(event_mask["SA_Score_Alpha"], 0)
+
+    fig, axes = create_psg_bcg_figure()
+    for i, (segment_start, segment_end) in enumerate(segment_list):
+        for ax in axes:
+            ax.cla()
+
+        plt_spo2_on_ax(axes[psg_bcg_chn_name2ax["SpO2"]], psg_data["SpO2"], segment_start, segment_end)
+        plt_stage_on_ax(axes[psg_bcg_chn_name2ax["Stage"]], psg_data["5_class"], segment_start, segment_end)
+        plt_signal_label_on_ax(axes[psg_bcg_chn_name2ax["Flow T"]], psg_data["Flow T"], segment_start, segment_end,
+                               psg_label, event_codes=[1, 2, 3, 4])
+        plt_signal_label_on_ax(axes[psg_bcg_chn_name2ax["Flow P"]], psg_data["Flow P"], segment_start, segment_end,
+                               psg_label, event_codes=[1, 2, 3, 4])
+        plt_signal_label_on_ax(axes[psg_bcg_chn_name2ax["Effort Tho"]], psg_data["Effort Tho"], segment_start, segment_end,
+                               psg_label, event_codes=[1, 2, 3, 4])
+        plt_signal_label_on_ax(axes[psg_bcg_chn_name2ax["Effort Abd"]], psg_data["Effort Abd"], segment_start, segment_end,
+                               psg_label, event_codes=[1, 2, 3, 4])
+        plt_signal_label_on_ax(axes[psg_bcg_chn_name2ax["HR"]], psg_data["HR"], segment_start, segment_end)
+        plt_signal_label_on_ax(axes[psg_bcg_chn_name2ax["resp"]], bcg_data["resp_signal"], segment_start, segment_end,
+                               event_mask, multi_labels="resp", event_codes=[1, 2, 3, 4],
+                               ax2=axes[psg_bcg_chn_name2ax["resp_twinx"]])
+        plt_signal_label_on_ax(axes[psg_bcg_chn_name2ax["bcg"]], bcg_data["bcg_signal"], segment_start, segment_end,
+                               event_mask, multi_labels="bcg", event_codes=[1, 2, 3, 4],
+                               ax2=axes[psg_bcg_chn_name2ax["bcg_twinx"]])
+
+
+        if save_path is not None:
+            fig.savefig(save_path / f"Segment_{segment_start}_{segment_end}.png")
+            # print(f"Saved figure to: {save_path / f'Segment_{segment_start}_{segment_end}.png'}")
+
+        if multi_p is not None:
+            multi_p[multi_task_id] = {"progress": i + 1, "total": len(segment_list), "desc": f"task_id:{multi_task_id} drawing {save_path.name}"}
+
+    plt.close(fig)
+    plt.close('all')
+    gc.collect()
+
+
+def draw_psg_label(psg_data, psg_label, segment_list, save_path=None, verbose=True,
+                       multi_p=None, multi_task_id=None):
+    if save_path is not None:
+        save_path.mkdir(parents=True, exist_ok=True)
+
+
+    if multi_p is  None:
+        # 遍历psg_data中所有数据的长度
+        for i in range(len(psg_data.keys())):
+            chn_name = list(psg_data.keys())[i]
+            print(f"{chn_name} data length: {len(psg_data[chn_name]['data'])}, fs: {psg_data[chn_name]['fs']}")
+        # psg_label的长度
+        print(f"psg_label length: {len(psg_label)}")
+
+    fig, axes = create_psg_figure()
+    for i, (segment_start, segment_end) in enumerate(segment_list):
+        for ax in axes:
+            ax.cla()
+
+        plt_spo2_on_ax(axes[psg_chn_name2ax["SpO2"]], psg_data["SpO2"], segment_start, segment_end)
+        plt_stage_on_ax(axes[psg_chn_name2ax["Stage"]], psg_data["5_class"], segment_start, segment_end)
+        plt_signal_label_on_ax(axes[psg_chn_name2ax["Flow T"]], psg_data["Flow T"], segment_start, segment_end,
+                               psg_label, event_codes=[1, 2, 3, 4])
+        plt_signal_label_on_ax(axes[psg_chn_name2ax["Flow P"]], psg_data["Flow P"], segment_start, segment_end,
+                               psg_label, event_codes=[1, 2, 3, 4])
+        plt_signal_label_on_ax(axes[psg_chn_name2ax["Effort Tho"]], psg_data["Effort Tho"], segment_start, segment_end,
+                               psg_label, event_codes=[1, 2, 3, 4])
+        plt_signal_label_on_ax(axes[psg_chn_name2ax["Effort Abd"]], psg_data["Effort Abd"], segment_start, segment_end,
+                               psg_label, event_codes=[1, 2, 3, 4])
+        plt_signal_label_on_ax(axes[psg_chn_name2ax["Effort"]], psg_data["Effort"], segment_start, segment_end,
+                               psg_label, event_codes=[1, 2, 3, 4])
+        plt_signal_label_on_ax(axes[psg_chn_name2ax["HR"]], psg_data["HR"], segment_start, segment_end)
+        plt_signal_label_on_ax(axes[psg_chn_name2ax["RRI"]], psg_data["RRI"], segment_start, segment_end)
+
+        if save_path is not None:
+            fig.savefig(save_path / f"Segment_{segment_start}_{segment_end}.png")
+            # print(f"Saved figure to: {save_path / f'Segment_{segment_start}_{segment_end}.png'}")
+
+        if multi_p is not None:
+            multi_p[multi_task_id] = {"progress": i + 1, "total": len(segment_list), "desc": f"task_id:{multi_task_id} drawing {save_path.name}"}
+    plt.close(fig)
+    plt.close('all')
+    gc.collect()
+
+
--- a/draw_tools/draw_statics.py
+++ b/draw_tools/draw_statics.py
@ -0,0 +1,371 @@
+from matplotlib.axes import Axes
+from matplotlib.gridspec import GridSpec
+from matplotlib.colors import PowerNorm
+import matplotlib.pyplot as plt
+import matplotlib.patches as mpatches
+import seaborn as sns
+import numpy as np
+
+
+def draw_ax_confusion_matrix(ax:Axes, confusion_matrix, segment_count_matrix, confusion_matrix_percent,
+                             valid_signal_length, total_duration, time_labels, amp_labels, signal_type=''):
+    # 创建用于热图注释的文本矩阵
+    text_matrix = np.empty((len(amp_labels), len(time_labels)), dtype=object)
+    percent_matrix = np.zeros((len(amp_labels), len(time_labels)))
+
+    # 填充文本矩阵和百分比矩阵
+    for i in range(len(amp_labels)):
+        for j in range(len(time_labels)):
+            val = confusion_matrix.iloc[i, j]
+            segment_count = segment_count_matrix[i, j]
+            percent = confusion_matrix_percent.iloc[i, j]
+            text_matrix[i, j] = f"[{int(segment_count)}]{int(val)}\n({percent:.2f}%)"
+            percent_matrix[i, j] = percent
+
+    # 绘制热图，调整颜色条位置
+    sns_heatmap = sns.heatmap(percent_matrix, annot=text_matrix, fmt='',
+                              xticklabels=time_labels, yticklabels=amp_labels,
+                              cmap='YlGnBu', ax=ax, vmin=0, vmax=100,
+                              norm=PowerNorm(gamma=0.5, vmin=0, vmax=100),
+                              cbar_kws={'label': '百分比 (%)', 'shrink': 0.6, 'pad': 0.15},
+                              # annot_kws={'fontsize': 12}
+                              )
+
+    # 添加行统计（右侧）
+    row_sums = confusion_matrix['总计']
+    row_percents = confusion_matrix_percent['总计']
+    ax.text(len(time_labels) + 1, -0.5, "各幅值时长\n(有效区间百分比%)", ha='center', va='center')
+    for i, (val, perc) in enumerate(zip(row_sums, row_percents)):
+        ax.text(len(time_labels) + 0.5, i + 0.5, f"{int(val)}\n({perc:.2f}%)",
+                ha='center', va='center')
+
+    # 添加列统计（底部）
+    col_sums = segment_count_matrix.sum(axis=0)
+    col_percents = confusion_matrix.sum(axis=0) / total_duration * 100
+    ax.text(-1, len(amp_labels) + 0.5, "[各时长片段数]\n(有效区间百分比%)", ha='center', va='center', rotation=0)
+    for j, (val, perc) in enumerate(zip(col_sums, col_percents)):
+        ax.text(j + 0.5, len(amp_labels) + 0.5, f"[{int(val)}]\n({perc:.2f}%)",
+                ha='center', va='center', rotation=0)
+
+    # 将x轴坐标移到顶部
+    ax.xaxis.set_label_position('top')
+    ax.xaxis.tick_top()
+
+    # 设置标题和标签
+    ax.set_title('幅值-时长统计矩阵', pad=40)
+    ax.set_xlabel('持续时间区间 (秒)', labelpad=10)
+    ax.set_ylabel('幅值区间')
+
+    # 设置坐标轴标签水平显示
+    ax.set_xticklabels(time_labels, rotation=0)
+    ax.set_yticklabels(amp_labels, rotation=0)
+
+    # 调整颜色条位置
+    cbar = sns_heatmap.collections[0].colorbar
+    cbar.ax.yaxis.set_label_position('right')
+
+    # 添加图例说明
+    ax.text(-2, -1, "热图内：\n[片段数]时长\n(有效区间百分比%)",
+            ha='left', va='top', bbox=dict(facecolor='none', edgecolor='black', alpha=0.5))
+
+    # 总计
+    # 总片段数
+    total_segments = segment_count_matrix.sum()
+    # 有效总市场占比
+    total_percent = valid_signal_length / total_duration * 100
+    ax.text(len(time_labels) + 0.5, len(amp_labels) + 0.5, f"[{int(total_segments)}]{valid_signal_length}\n({total_percent:.2f}%)",
+            ha='center', va='center')
+
+
+
+def draw_ax_amp(ax, signal_name, original_times, origin_signal, no_movement_signal, mav_values,
+                movement_position_list, low_amp_position_list, signal_second_length, aml_list=None):
+    # 绘制信号线
+    ax.plot(original_times, origin_signal, 'k-', linewidth=1, alpha=0.7)
+
+    # 添加红色和蓝色的axvspan区域
+    for start, end in movement_position_list:
+        if start < len(original_times) and end < len(original_times):
+            ax.axvspan(start, end, color='red', alpha=0.3)
+
+    for start, end in low_amp_position_list:
+        if start < len(original_times) and end < len(original_times):
+            ax.axvspan(start, end, color='blue', alpha=0.2)
+
+    # 如果存在AML列表，绘制水平线
+    if aml_list is not None:
+        color_map = ['red', 'orange', 'green']
+        for i, aml in enumerate(aml_list):
+            ax.hlines(aml, 0, signal_second_length, color=color_map[min(i, 2)], linestyle='dashed', linewidth=2, alpha=0.5, label=f'{aml} aml')
+
+
+    ax.plot(np.linspace(0, len(mav_values), len(mav_values)), mav_values, color='blue', linewidth=2, alpha=0.4, label='2sMAV')
+
+    # 设置Y轴范围
+    ax.set_ylim((-2000, 2000))
+
+    # 创建表示不同颜色区域的图例
+    red_patch = mpatches.Patch(color='red', alpha=0.2, label='Movement Area')
+    blue_patch = mpatches.Patch(color='blue', alpha=0.2, label='Low Amplitude Area')
+
+    # 添加新的图例项，并保留原来的图例项
+    handles, labels = ax.get_legend_handles_labels()  # 获取原有图例
+    ax.legend(handles=[red_patch, blue_patch] + handles,
+              labels=['Movement Area', 'Low Amplitude Area'] + labels,
+              loc='upper right',
+              bbox_to_anchor=(1, 1.4),
+              framealpha=0.5)
+
+    # 设置标题和标签
+    ax.set_title(f'{signal_name} Signal')
+    ax.set_ylabel('Amplitude')
+    ax.set_xlabel('Time (s)')
+
+    # 启用网格
+    ax.grid(True, linestyle='--', alpha=0.7)
+
+
+def draw_signal_metrics(bcg_origin_signal, resp_origin_signal, bcg_no_movement_signal, resp_no_movement_signal,
+                        bcg_sampling_rate, resp_sampling_rate, bcg_movement_position_list, bcg_low_amp_position_list,
+                        resp_movement_position_list, resp_low_amp_position_list,
+                        bcg_mav_values, resp_mav_values, bcg_statistic_info, resp_statistic_info,
+                        signal_info, show=False, save_path=None):
+
+    # 创建图像
+    fig = plt.figure(figsize=(18, 10))
+
+    gs = GridSpec(2, 2, height_ratios=[2, 2], width_ratios=[4, 2], hspace=0.5)
+
+    signal_second_length = len(bcg_origin_signal) // bcg_sampling_rate
+    bcg_origin_times = np.linspace(0, signal_second_length, len(bcg_origin_signal))
+    resp_origin_times = np.linspace(0, signal_second_length, len(resp_origin_signal))
+    # 子图 1：原始信号
+    ax1 = fig.add_subplot(gs[0])
+    draw_ax_amp(ax=ax1, signal_name='BCG', original_times=bcg_origin_times, origin_signal=bcg_origin_signal,
+                no_movement_signal=bcg_no_movement_signal, mav_values=bcg_mav_values,
+                movement_position_list=bcg_movement_position_list, low_amp_position_list=bcg_low_amp_position_list,
+                signal_second_length=signal_second_length, aml_list=[200, 500, 1000])
+
+    ax2 = fig.add_subplot(gs[1])
+    param_names = ['confusion_matrix', 'segment_count_matrix', 'confusion_matrix_percent',
+                   'valid_signal_length', 'total_duration', 'time_labels', 'amp_labels']
+    params = dict(zip(param_names, bcg_statistic_info))
+    params['ax'] = ax2
+    draw_ax_confusion_matrix(**params)
+
+    ax3 = fig.add_subplot(gs[2], sharex=ax1)
+    draw_ax_amp(ax=ax3, signal_name='RSEP', original_times=resp_origin_times, origin_signal=resp_origin_signal,
+                no_movement_signal=resp_no_movement_signal, mav_values=resp_mav_values,
+                movement_position_list=resp_movement_position_list, low_amp_position_list=resp_low_amp_position_list,
+                signal_second_length=signal_second_length, aml_list=[100, 300, 500])
+    ax4 = fig.add_subplot(gs[3])
+    params = dict(zip(param_names, resp_statistic_info))
+    params['ax'] = ax4
+    draw_ax_confusion_matrix(**params)
+
+    # 全局标题
+    fig.suptitle(f'{signal_info} Signal Metrics', fontsize=16, x=0.35, y=0.95)
+
+    if save_path is not None:
+        # 保存图像
+        plt.savefig(save_path, dpi=300)
+
+    if show:
+        plt.show()
+
+    plt.close()
+
+
+def draw_signal_with_mask(samp_id, signal_data, resp_data, bcg_data, signal_fs, resp_fs, bcg_fs,
+                          signal_disable_mask, resp_low_amp_mask, resp_movement_mask, resp_change_mask,
+                          resp_sa_mask, bcg_low_amp_mask, bcg_movement_mask, bcg_change_mask, show=False, save_path=None
+                          ):
+    # 第一行绘制去工频噪声的原始信号，右侧为不可用区间标记，左侧为信号幅值纵坐标
+    # 第二行绘制呼吸分量，右侧低幅值、高幅值、幅值变换标记、SA标签，左侧为呼吸幅值纵坐标
+    # 第三行绘制心冲击分量，右侧为低幅值、高幅值、幅值变换标记、，左侧为心冲击幅值纵坐标
+    # mask为None，则生成全Nan掩码
+    def _none_to_nan_mask(mask, ref):
+        if mask is None:
+            return np.full_like(ref, np.nan)
+        else:
+            # 将mask中的0替换为nan，其他的保持
+            mask = np.where(mask == 0, np.nan, mask)
+            return mask
+
+    signal_disable_mask = _none_to_nan_mask(signal_disable_mask, signal_data)
+    resp_low_amp_mask   = _none_to_nan_mask(resp_low_amp_mask, resp_data)
+    resp_movement_mask  = _none_to_nan_mask(resp_movement_mask, resp_data)
+    resp_change_mask    = _none_to_nan_mask(resp_change_mask, resp_data)
+    resp_sa_mask        = _none_to_nan_mask(resp_sa_mask, resp_data)
+    bcg_low_amp_mask    = _none_to_nan_mask(bcg_low_amp_mask, bcg_data)
+    bcg_movement_mask   = _none_to_nan_mask(bcg_movement_mask, bcg_data)
+    bcg_change_mask     = _none_to_nan_mask(bcg_change_mask, bcg_data)
+
+
+    fig = plt.figure(figsize=(18, 10))
+    ax0 = fig.add_subplot(3, 1, 1)
+    ax0.plot(np.linspace(0, len(signal_data) // signal_fs, len(signal_data)), signal_data, color='blue')
+    ax0.set_title(f'Sample {samp_id} - Raw Signal Data')
+    ax0.set_ylabel('Amplitude')
+    # ax0.set_xticklabels([])
+
+    ax0_twin = ax0.twinx()
+    ax0_twin.plot(np.linspace(0, len(signal_disable_mask), len(signal_disable_mask)), signal_disable_mask,
+                  color='red', alpha=0.5)
+    ax0_twin.autoscale(enable=False, axis='y', tight=True)
+    ax0_twin.set_ylim((-2, 2))
+    ax0_twin.set_ylabel('Disable Mask')
+    ax0_twin.set_yticks([0, 1])
+    ax0_twin.set_yticklabels(['Enabled', 'Disabled'])
+    ax0_twin.grid(False)
+    ax0_twin.legend(['Disable Mask'], loc='upper right')
+
+
+    ax1 = fig.add_subplot(3, 1, 2, sharex=ax0)
+    ax1.plot(np.linspace(0, len(resp_data) // resp_fs, len(resp_data)), resp_data, color='gray', alpha=0.5)
+    resp_data_no_movement = resp_data.copy()
+    resp_data_no_movement[resp_movement_mask.repeat(int(resp_fs)) == 1] = np.nan
+    ax1.plot(np.linspace(0, len(resp_data_no_movement) // resp_fs, len(resp_data_no_movement)), resp_data_no_movement, color='orange')
+    ax1.set_ylabel('Amplitude')
+    # ax1.set_xticklabels([])
+    ax1_twin = ax1.twinx()
+    ax1_twin.plot(np.linspace(0, len(resp_low_amp_mask), len(resp_low_amp_mask)), resp_low_amp_mask*-1,
+                  color='blue', alpha=0.5, label='Low Amplitude Mask')
+    ax1_twin.plot(np.linspace(0, len(resp_movement_mask), len(resp_movement_mask)), resp_movement_mask*-2,
+                  color='red', alpha=0.5, label='Movement Mask')
+    ax1_twin.plot(np.linspace(0, len(resp_change_mask), len(resp_change_mask)), resp_change_mask*-3,
+                  color='green', alpha=0.5, label='Amplitude Change Mask')
+    ax1_twin.plot(np.linspace(0, len(resp_sa_mask), len(resp_sa_mask)), resp_sa_mask,
+                  color='purple', alpha=0.5, label='SA Mask')
+    ax1_twin.autoscale(enable=False, axis='y', tight=True)
+    ax1_twin.set_ylim((-4, 5))
+    # ax1_twin.set_ylabel('Resp Masks')
+    # ax1_twin.set_yticks([0, 1])
+    # ax1_twin.set_yticklabels(['No', 'Yes'])
+    ax1_twin.grid(False)
+
+    ax1_twin.legend(['Low Amplitude Mask', 'Movement Mask', 'Amplitude Change Mask', 'SA Mask'], loc='upper right')
+    ax1.set_title(f'Sample {samp_id} - Respiration Component')
+
+
+
+    ax2 = fig.add_subplot(3, 1, 3, sharex=ax0)
+    ax2.plot(np.linspace(0, len(bcg_data) // bcg_fs, len(bcg_data)), bcg_data, color='green')
+    ax2.set_ylabel('Amplitude')
+    ax2.set_xlabel('Time (s)')
+    ax2_twin = ax2.twinx()
+    ax2_twin.plot(np.linspace(0, len(bcg_low_amp_mask), len(bcg_low_amp_mask)), bcg_low_amp_mask*-1,
+                  color='blue', alpha=0.5, label='Low Amplitude Mask')
+    ax2_twin.plot(np.linspace(0, len(bcg_movement_mask), len(bcg_movement_mask)), bcg_movement_mask*-2,
+                  color='red', alpha=0.5, label='Movement Mask')
+    ax2_twin.plot(np.linspace(0, len(bcg_change_mask), len(bcg_change_mask)), bcg_change_mask*-3,
+                    color='green', alpha=0.5, label='Amplitude Change Mask')
+    ax2_twin.autoscale(enable=False, axis='y', tight=True)
+    ax2_twin.set_ylim((-4, 2))
+    ax2_twin.set_ylabel('BCG Masks')
+    # ax2_twin.set_yticks([0, 1])
+    # ax2_twin.set_yticklabels(['No', 'Yes'])
+    ax2_twin.grid(False)
+    ax2_twin.legend(['Low Amplitude Mask', 'Movement Mask', 'Amplitude Change Mask'], loc='upper right')
+    # ax2.set_title(f'Sample {samp_id} - BCG Component')
+
+    ax0_twin._lim_lock = False
+    ax1_twin._lim_lock = False
+    ax2_twin._lim_lock = False
+
+    def on_lims_change(event_ax):
+        if getattr(event_ax, '_lim_lock', False):
+            return
+        try:
+            event_ax._lim_lock = True
+
+            if event_ax == ax0_twin:
+                # 重新锁定 ax1 的 Y 轴范围
+                ax0_twin.set_ylim(-2, 2)
+            elif event_ax == ax1_twin:
+                ax1_twin.set_ylim(-4, 5)
+            elif event_ax == ax2_twin:
+                ax2_twin.set_ylim(-4, 2)
+
+        finally:
+            event_ax._lim_lock = False
+
+
+    ax0_twin.callbacks.connect('ylim_changed', on_lims_change)
+    ax1_twin.callbacks.connect('ylim_changed', on_lims_change)
+    ax2_twin.callbacks.connect('ylim_changed', on_lims_change)
+    plt.tight_layout()
+
+    if save_path is not None:
+        plt.savefig(save_path, dpi=300)
+    if show:
+        plt.show()
+
+
+def draw_psg_signal(samp_id, tho_signal, abd_signal, flowp_signal, flowt_signal, spo2_signal, effort_signal, rri_signal, event_mask, fs,
+                    show=False, save_path=None):
+    sa_mask = event_mask.repeat(fs)
+    fig, axs = plt.subplots(7, 1, figsize=(18, 12), sharex=True)
+    time_axis = np.linspace(0, len(tho_signal) / fs, len(tho_signal))
+    axs[0].plot(time_axis, tho_signal, label='THO', color='black')
+    axs[0].set_title(f'Sample {samp_id} - PSG Signal Data')
+    axs[0].set_ylabel('THO Amplitude')
+    axs[0].legend(loc='upper right')
+
+    ax0_twin = axs[0].twinx()
+    ax0_twin.plot(time_axis, sa_mask, color='purple', alpha=0.5, label='SA Mask')
+    ax0_twin.autoscale(enable=False, axis='y', tight=True)
+    ax0_twin.set_ylim((-4, 5))
+
+    axs[1].plot(time_axis, abd_signal, label='ABD', color='black')
+    axs[1].set_ylabel('ABD Amplitude')
+    axs[1].legend(loc='upper right')
+
+    ax1_twin = axs[1].twinx()
+    ax1_twin.plot(time_axis, sa_mask, color='purple', alpha=0.5, label='SA Mask')
+    ax1_twin.autoscale(enable=False, axis='y', tight=True)
+    ax1_twin.set_ylim((-4, 5))
+
+    axs[2].plot(time_axis, effort_signal, label='EFFO', color='black')
+    axs[2].set_ylabel('EFFO Amplitude')
+    axs[2].legend(loc='upper right')
+
+    ax2_twin = axs[2].twinx()
+    ax2_twin.plot(time_axis, sa_mask, color='purple', alpha=0.5, label='SA Mask')
+    ax2_twin.autoscale(enable=False, axis='y', tight=True)
+    ax2_twin.set_ylim((-4, 5))
+
+    axs[3].plot(time_axis, flowp_signal, label='FLOWP', color='black')
+    axs[3].set_ylabel('FLOWP Amplitude')
+    axs[3].legend(loc='upper right')
+
+    ax3_twin = axs[3].twinx()
+    ax3_twin.plot(time_axis, sa_mask, color='purple', alpha=0.5, label='SA Mask')
+    ax3_twin.autoscale(enable=False, axis='y', tight=True)
+    ax3_twin.set_ylim((-4, 5))
+
+    axs[4].plot(time_axis, flowt_signal, label='FLOWT', color='black')
+    axs[4].set_ylabel('FLOWT Amplitude')
+    axs[4].legend(loc='upper right')
+
+    ax4_twin = axs[4].twinx()
+    ax4_twin.plot(time_axis, sa_mask, color='purple', alpha=0.5, label='SA Mask')
+    ax4_twin.autoscale(enable=False, axis='y', tight=True)
+    ax4_twin.set_ylim((-4, 5))
+
+    axs[5].plot(time_axis, rri_signal, label='RRI', color='black')
+    axs[5].set_ylabel('RRI Amplitude')
+    axs[5].legend(loc='upper right')
+
+
+    axs[6].plot(time_axis, spo2_signal, label='SPO2', color='black')
+    axs[6].set_ylabel('SPO2 Amplitude')
+    axs[6].set_xlabel('Time (s)')
+    axs[6].legend(loc='upper right')
+
+
+    if save_path is not None:
+        plt.savefig(save_path, dpi=300)
+    if show:
+        plt.show()
+
--- a/event_mask_process/HYS_PSG_process.py
+++ b/event_mask_process/HYS_PSG_process.py
@ -0,0 +1,183 @@
+"""
+本脚本完成对呼研所数据的处理，包含以下功能：
+1. 数据读取与预处理
+    从传入路径中，进行数据和标签的读取，并进行初步的预处理
+    预处理包括为数据进行滤波、去噪等操作
+2. 数据清洗与异常值处理
+3. 输出清晰后的统计信息
+4. 数据保存
+    将处理后的数据保存到指定路径，便于后续使用
+    主要是保存切分后的数据位置和标签
+5. 可视化
+    提供数据处理前后的可视化对比，帮助理解数据变化
+    绘制多条可用性趋势图，展示数据的可用区间、体动区间、低幅值区间等
+
+
+
+# 低幅值区间规则标定与剔除
+# 高幅值连续体动规则标定与剔除
+# 手动标定不可用区间提剔除
+"""
+import sys
+from pathlib import Path
+
+sys.path.append(str(Path(__file__).resolve().parent.parent))
+project_root_path = Path(__file__).resolve().parent.parent
+
+import shutil
+import draw_tools
+import utils
+import numpy as np
+import signal_method
+import os
+
+
+os.environ['DISPLAY'] = "localhost:10.0"
+
+
+def process_one_signal(samp_id, show=False):
+    pass
+
+    tho_signal_path = list((org_signal_root_path / f"{samp_id}").glob("Effort Tho_Sync_*.txt"))
+    abd_signal_path = list((org_signal_root_path / f"{samp_id}").glob("Effort Abd_Sync_*.txt"))
+    flowp_signal_path = list((org_signal_root_path / f"{samp_id}").glob("Flow P_Sync_*.txt"))
+    flowt_signal_path = list((org_signal_root_path / f"{samp_id}").glob("Flow T_Sync_*.txt"))
+    spo2_signal_path = list((org_signal_root_path / f"{samp_id}").glob("SpO2_Sync_*.txt"))
+    stage_signal_path = list((org_signal_root_path / f"{samp_id}").glob("5_class_Sync_*.txt"))
+
+    if not tho_signal_path:
+        raise FileNotFoundError(f"Effort Tho_Sync file not found for sample ID: {samp_id}")
+    tho_signal_path = tho_signal_path[0]
+    print(f"Processing Effort Tho_Sync signal file: {tho_signal_path}")
+    if not abd_signal_path:
+        raise FileNotFoundError(f"Effort Abd_Sync file not found for sample ID: {samp_id}")
+    abd_signal_path = abd_signal_path[0]
+    print(f"Processing Effort Abd_Sync signal file: {abd_signal_path}")
+    if not flowp_signal_path:
+        raise FileNotFoundError(f"Flow P_Sync file not found for sample ID: {samp_id}")
+    flowp_signal_path = flowp_signal_path[0]
+    print(f"Processing Flow P_Sync signal file: {flowp_signal_path}")
+    if not flowt_signal_path:
+        raise FileNotFoundError(f"Flow T_Sync file not found for sample ID: {samp_id}")
+    flowt_signal_path = flowt_signal_path[0]
+    print(f"Processing Flow T_Sync signal file: {flowt_signal_path}")
+    if not spo2_signal_path:
+        raise FileNotFoundError(f"SpO2_Sync file not found for sample ID: {samp_id}")
+    spo2_signal_path = spo2_signal_path[0]
+    print(f"Processing SpO2_Sync signal file: {spo2_signal_path}")
+    if not stage_signal_path:
+        raise FileNotFoundError(f"5_class_Sync file not found for sample ID: {samp_id}")
+    stage_signal_path = stage_signal_path[0]
+    print(f"Processing 5_class_Sync signal file: {stage_signal_path}")
+
+
+
+    label_path = (label_root_path / f"{samp_id}").glob("SA Label_Sync.csv")
+    if not label_path:
+        raise FileNotFoundError(f"Label_corrected file not found for sample ID: {samp_id}")
+    label_path = list(label_path)[0]
+    print(f"Processing Label_corrected file: {label_path}")
+    #
+    # # 保存处理后的数据和标签
+    save_samp_path = save_path / f"{samp_id}"
+    save_samp_path.mkdir(parents=True, exist_ok=True)
+
+    # # # 读取信号数据
+    tho_data_raw, tho_length, tho_fs, tho_second = utils.read_signal_txt(tho_signal_path, dtype=float, verbose=True)
+    # abd_data_raw, abd_length, abd_fs, abd_second = utils.read_signal_txt(abd_signal_path, dtype=float, verbose=True)
+    # flowp_data_raw, flowp_length, flowp_fs, flowp_second = utils.read_signal_txt(flowp_signal_path, dtype=float, verbose=True)
+    # flowt_data_raw, flowt_length, flowt_fs, flowt_second = utils.read_signal_txt(flowt_signal_path, dtype=float, verbose=True)
+    # spo2_data_raw, spo2_length, spo2_fs, spo2_second = utils.read_signal_txt(spo2_signal_path, dtype=float, verbose=True)
+    stage_data_raw, stage_length, stage_fs, stage_second = utils.read_signal_txt(stage_signal_path, dtype=str, verbose=True)
+
+
+    #
+    # # 预处理与滤波
+    # tho_data, tho_data_filt, tho_fs = signal_method.psg_effort_filter(conf=conf, effort_data_raw=tho_data_raw, effort_fs=tho_fs)
+    # abd_data, abd_data_filt, abd_fs = signal_method.psg_effort_filter(conf=conf, effort_data_raw=abd_data_raw, effort_fs=abd_fs)
+    # flowp_data, flowp_data_filt, flowp_fs = signal_method.psg_effort_filter(conf=conf, effort_data_raw=flowp_data_raw, effort_fs=flowp_fs)
+    # flowt_data, flowt_data_filt, flowt_fs = signal_method.psg_effort_filter(conf=conf, effort_data_raw=flowt_data_raw, effort_fs=flowt_fs)
+
+    # 降采样
+    # old_tho_fs = tho_fs
+    # tho_fs = conf["effort"]["downsample_fs"]
+    # tho_data_filt = utils.downsample_signal_fast(original_signal=tho_data_filt, original_fs=old_tho_fs, target_fs=tho_fs)
+    # old_abd_fs = abd_fs
+    # abd_fs = conf["effort"]["downsample_fs"]
+    # abd_data_filt = utils.downsample_signal_fast(original_signal=abd_data_filt, original_fs=old_abd_fs, target_fs=abd_fs)
+    # old_flowp_fs = flowp_fs
+    # flowp_fs = conf["effort"]["downsample_fs"]
+    # flowp_data_filt = utils.downsample_signal_fast(original_signal=flowp_data_filt, original_fs=old_flowp_fs, target_fs=flowp_fs)
+    # old_flowt_fs = flowt_fs
+    # flowt_fs = conf["effort"]["downsample_fs"]
+    # flowt_data_filt = utils.downsample_signal_fast(original_signal=flowt_data_filt, original_fs=old_flowt_fs, target_fs=flowt_fs)
+
+    # spo2不降采样
+    # spo2_data_filt = spo2_data_raw
+    # spo2_fs = spo2_fs
+
+    label_data = utils.read_raw_psg_label(path=label_path)
+    event_mask, score_mask = utils.generate_event_mask(signal_second=tho_second, event_df=label_data, use_correct=False, with_score=False)
+    # event_mask > 0 的部分为1，其他为0
+    score_mask = np.where(event_mask > 0, 1, 0)
+
+    # 根据睡眠分期生成不可用区间
+    wake_mask = utils.get_wake_mask(stage_data_raw)
+    # 剔除短于60秒的觉醒区间
+    wake_mask = utils.remove_short_durations(wake_mask, time_points=np.arange(len(wake_mask) * stage_fs), min_duration_sec=60)
+    # 合并短于120秒的觉醒区间
+    wake_mask = utils.merge_short_gaps(wake_mask, time_points=np.arange(len(wake_mask) * stage_fs), max_gap_sec=60)
+
+    disable_label = wake_mask
+
+    disable_label = disable_label[:tho_second]
+
+
+    # 复制事件文件 到保存路径
+    sa_label_save_name = f"{samp_id}_" + label_path.name
+    shutil.copyfile(label_path, save_samp_path / sa_label_save_name)
+    #
+    # 新建一个dataframe，分别是秒数、SA标签，
+    save_dict = {
+        "Second": np.arange(tho_second),
+        "SA_Label": event_mask,
+        "SA_Score": score_mask,
+        "Disable_Label": disable_label,
+        "Resp_LowAmp_Label": np.zeros_like(event_mask),
+        "Resp_Movement_Label": np.zeros_like(event_mask),
+        "Resp_AmpChange_Label": np.zeros_like(event_mask),
+        "BCG_LowAmp_Label": np.zeros_like(event_mask),
+        "BCG_Movement_Label": np.zeros_like(event_mask),
+        "BCG_AmpChange_Label": np.zeros_like(event_mask)
+    }
+
+    mask_label_save_name = f"{samp_id}_Processed_Labels.csv"
+    utils.save_process_label(save_path=save_samp_path / mask_label_save_name, save_dict=save_dict)
+
+
+if __name__ == '__main__':
+    yaml_path =  project_root_path / "dataset_config/HYS_PSG_config.yaml"
+    # disable_df_path = project_root_path / "排除区间.xlsx"
+    #
+    conf = utils.load_dataset_conf(yaml_path)
+
+    root_path = Path(conf["root_path"])
+    save_path = Path(conf["mask_save_path"])
+    select_ids = conf["select_ids"]
+    #
+    print(f"select_ids: {select_ids}")
+    print(f"root_path: {root_path}")
+    print(f"save_path: {save_path}")
+    #
+    org_signal_root_path = root_path / "PSG_Aligned"
+    label_root_path = root_path / "PSG_Aligned"
+    #
+    # all_samp_disable_df = utils.read_disable_excel(disable_df_path)
+    #
+    # process_one_signal(select_ids[0], show=True)
+    # #
+    for samp_id in select_ids:
+        print(f"Processing sample ID: {samp_id}")
+        process_one_signal(samp_id, show=False)
+        print(f"Finished processing sample ID: {samp_id}\n\n")
+    pass
--- a/event_mask_process/HYS_process.py
+++ b/event_mask_process/HYS_process.py
@ -0,0 +1,246 @@
+"""
+本脚本完成对呼研所数据的处理，包含以下功能：
+1. 数据读取与预处理
+    从传入路径中，进行数据和标签的读取，并进行初步的预处理
+    预处理包括为数据进行滤波、去噪等操作
+2. 数据清洗与异常值处理
+3. 输出清晰后的统计信息
+4. 数据保存
+    将处理后的数据保存到指定路径，便于后续使用
+    主要是保存切分后的数据位置和标签
+5. 可视化
+    提供数据处理前后的可视化对比，帮助理解数据变化
+    绘制多条可用性趋势图，展示数据的可用区间、体动区间、低幅值区间等
+
+
+
+# 低幅值区间规则标定与剔除
+# 高幅值连续体动规则标定与剔除
+# 手动标定不可用区间提剔除
+"""
+import sys
+from pathlib import Path
+
+sys.path.append(str(Path(__file__).resolve().parent.parent))
+project_root_path = Path(__file__).resolve().parent.parent
+
+import shutil
+import draw_tools
+import utils
+import numpy as np
+import signal_method
+import os
+
+
+os.environ['DISPLAY'] = "localhost:10.0"
+
+
+def process_one_signal(samp_id, show=False):
+    signal_path = list((org_signal_root_path / f"{samp_id}").glob("OrgBCG_Sync_*.txt"))
+    if not signal_path:
+        raise FileNotFoundError(f"OrgBCG_Sync file not found for sample ID: {samp_id}")
+    signal_path = signal_path[0]
+    print(f"Processing OrgBCG_Sync signal file: {signal_path}")
+
+    label_path = (label_root_path / f"{samp_id}").glob("SA Label_corrected.csv")
+    if not label_path:
+        raise FileNotFoundError(f"Label_corrected file not found for sample ID: {samp_id}")
+    label_path = list(label_path)[0]
+    print(f"Processing Label_corrected file: {label_path}")
+
+    # 保存处理后的数据和标签
+    save_samp_path = save_path / f"{samp_id}"
+    save_samp_path.mkdir(parents=True, exist_ok=True)
+
+    signal_data_raw, signal_length, signal_fs, signal_second = utils.read_signal_txt(signal_path, dtype=float, verbose=True)
+
+    signal_data, resp_data, resp_fs, bcg_data, bcg_fs = signal_method.signal_filter_split(conf=conf, signal_data_raw=signal_data_raw, signal_fs=signal_fs)
+
+    # 降采样
+    old_resp_fs = resp_fs
+    resp_fs = conf["resp"]["downsample_fs_2"]
+    resp_data = utils.downsample_signal_fast(original_signal=resp_data, original_fs=old_resp_fs, target_fs=resp_fs)
+    bcg_fs = conf["bcg"]["downsample_fs"]
+    bcg_data = utils.downsample_signal_fast(original_signal=bcg_data, original_fs=signal_fs, target_fs=bcg_fs)
+
+    label_data = utils.read_label_csv(path=label_path)
+    event_mask, score_mask = utils.generate_event_mask(signal_second=signal_second, event_df=label_data)
+
+    manual_disable_mask = utils.generate_disable_mask(signal_second=signal_second, disable_df=all_samp_disable_df[
+        all_samp_disable_df["id"] == samp_id])
+    print(f"disable_mask_shape: {manual_disable_mask.shape}, num_disable: {np.sum(manual_disable_mask == 0)}")
+
+    # 分析Resp的低幅值区间
+    resp_low_amp_conf = conf.get("resp_low_amp", None)
+    if resp_low_amp_conf is not None:
+        resp_low_amp_mask, resp_low_amp_position_list = signal_method.detect_low_amplitude_signal(
+            signal_data=resp_data,
+            sampling_rate=resp_fs,
+            **resp_low_amp_conf
+        )
+        print(
+            f"resp_low_amp_mask_shape: {resp_low_amp_mask.shape}, num_low_amp: {np.sum(resp_low_amp_mask == 1)}, count_low_amp_positions: {len(resp_low_amp_position_list)}")
+    else:
+        resp_low_amp_mask, resp_low_amp_position_list = None, None
+        print("resp_low_amp_mask is None")
+
+    # 分析Resp的高幅值伪迹区间
+    resp_movement_conf = conf.get("resp_movement", None)
+    if resp_movement_conf is not None:
+        raw_resp_movement_mask, resp_movement_mask, raw_resp_movement_position_list, resp_movement_position_list = signal_method.detect_movement(
+            signal_data=resp_data,
+            sampling_rate=resp_fs,
+            **resp_movement_conf
+        )
+        print(
+            f"resp_movement_mask_shape: {resp_movement_mask.shape}, num_movement: {np.sum(resp_movement_mask == 1)}, count_movement_positions: {len(resp_movement_position_list)}")
+    else:
+        resp_movement_mask, resp_movement_position_list = None, None
+        print("resp_movement_mask is None")
+
+    resp_movement_revise_conf = conf.get("resp_movement_revise", None)
+    if resp_movement_mask is not None and resp_movement_revise_conf is not None:
+        resp_movement_mask, resp_movement_position_list = signal_method.movement_revise(
+            signal_data=resp_data,
+            movement_mask=resp_movement_mask,
+            movement_list=resp_movement_position_list,
+            sampling_rate=resp_fs,
+            **resp_movement_revise_conf,
+            verbose=False
+        )
+        print(
+            f"After revise, resp_movement_mask_shape: {resp_movement_mask.shape}, num_movement: {np.sum(resp_movement_mask == 1)}, count_movement_positions: {len(resp_movement_position_list)}")
+    else:
+        print("resp_movement_mask revise is skipped")
+
+    # 分析Resp的幅值突变区间
+    resp_amp_change_conf = conf.get("resp_amp_change", None)
+    if resp_amp_change_conf is not None and resp_movement_mask is not None:
+        resp_amp_change_mask, resp_amp_change_list = signal_method.position_based_sleep_recognition_v3(
+            signal_data=resp_data,
+            movement_mask=resp_movement_mask,
+            movement_list=resp_movement_position_list,
+            sampling_rate=resp_fs,
+            **resp_amp_change_conf,
+            verbose=False)
+        print(
+            f"amp_change_mask_shape: {resp_amp_change_mask.shape}, num_amp_change: {np.sum(resp_amp_change_mask == 1)}, count_amp_change_positions: {len(resp_amp_change_list)}")
+    else:
+        resp_amp_change_mask = None
+        print("amp_change_mask is None")
+
+    # 分析Bcg的低幅值区间
+    bcg_low_amp_conf = conf.get("bcg_low_amp", None)
+    if bcg_low_amp_conf is not None:
+        bcg_low_amp_mask, bcg_low_amp_position_list = signal_method.detect_low_amplitude_signal(
+            signal_data=bcg_data,
+            sampling_rate=bcg_fs,
+            **bcg_low_amp_conf
+        )
+        print(
+            f"bcg_low_amp_mask_shape: {bcg_low_amp_mask.shape}, num_low_amp: {np.sum(bcg_low_amp_mask == 1)}, count_low_amp_positions: {len(bcg_low_amp_position_list)}")
+    else:
+        bcg_low_amp_mask, bcg_low_amp_position_list = None, None
+        print("bcg_low_amp_mask is None")
+
+    # 分析Bcg的高幅值伪迹区间
+    bcg_movement_conf = conf.get("bcg_movement", None)
+    if bcg_movement_conf is not None:
+        raw_bcg_movement_mask, bcg_movement_mask, raw_bcg_movement_position_list, bcg_movement_position_list = signal_method.detect_movement(
+            signal_data=bcg_data,
+            sampling_rate=bcg_fs,
+            **bcg_movement_conf
+        )
+        print(
+            f"bcg_movement_mask_shape: {bcg_movement_mask.shape}, num_movement: {np.sum(bcg_movement_mask == 1)}, count_movement_positions: {len(bcg_movement_position_list)}")
+    else:
+        bcg_movement_mask = None
+        print("bcg_movement_mask is None")
+
+    # 分析Bcg的幅值突变区间
+    if bcg_movement_mask is not None:
+        bcg_amp_change_mask, bcg_amp_change_list = signal_method.position_based_sleep_recognition_v2(
+            signal_data=bcg_data,
+            movement_mask=bcg_movement_mask,
+            sampling_rate=bcg_fs)
+        print(
+            f"bcg_amp_change_mask_shape: {bcg_amp_change_mask.shape}, num_amp_change: {np.sum(bcg_amp_change_mask == 1)}, count_amp_change_positions: {len(bcg_amp_change_list)}")
+    else:
+        bcg_amp_change_mask = None
+        print("bcg_amp_change_mask is None")
+
+    # 如果signal_data采样率过，进行降采样
+    if signal_fs == 1000:
+        signal_data = utils.downsample_signal_fast(original_signal=signal_data, original_fs=signal_fs, target_fs=100)
+        signal_data_raw = utils.downsample_signal_fast(original_signal=signal_data_raw, original_fs=signal_fs,
+                                                       target_fs=100)
+        signal_fs = 100
+
+    draw_tools.draw_signal_with_mask(samp_id=samp_id,
+                                     signal_data=signal_data,
+                                     signal_fs=signal_fs,
+                                     resp_data=resp_data,
+                                     resp_fs=resp_fs,
+                                     bcg_data=bcg_data,
+                                     bcg_fs=bcg_fs,
+                                     signal_disable_mask=manual_disable_mask,
+                                     resp_low_amp_mask=resp_low_amp_mask,
+                                     resp_movement_mask=resp_movement_mask,
+                                     resp_change_mask=resp_amp_change_mask,
+                                     resp_sa_mask=event_mask,
+                                     bcg_low_amp_mask=bcg_low_amp_mask,
+                                     bcg_movement_mask=bcg_movement_mask,
+                                     bcg_change_mask=bcg_amp_change_mask,
+                                     show=show,
+                                     save_path=save_samp_path / f"{samp_id}_Signal_Plots.png")
+
+    # 复制事件文件 到保存路径
+    sa_label_save_name = f"{samp_id}_" + label_path.name
+    shutil.copyfile(label_path, save_samp_path / sa_label_save_name)
+
+    # 新建一个dataframe，分别是秒数、SA标签，SA质量标签，禁用标签，Resp低幅值标签，Resp体动标签，Resp幅值突变标签，Bcg低幅值标签，Bcg体动标签，Bcg幅值突变标签
+    save_dict = {
+        "Second": np.arange(signal_second),
+        "SA_Label": event_mask,
+        "SA_Score": score_mask,
+        "Disable_Label": manual_disable_mask,
+        "Resp_LowAmp_Label": resp_low_amp_mask if resp_low_amp_mask is not None else np.zeros(signal_second, dtype=int),
+        "Resp_Movement_Label": resp_movement_mask if resp_movement_mask is not None else np.zeros(signal_second,
+                                                                                                  dtype=int),
+        "Resp_AmpChange_Label": resp_amp_change_mask if resp_amp_change_mask is not None else np.zeros(signal_second,
+                                                                                                       dtype=int),
+        "BCG_LowAmp_Label": bcg_low_amp_mask if bcg_low_amp_mask is not None else np.zeros(signal_second, dtype=int),
+        "BCG_Movement_Label": bcg_movement_mask if bcg_movement_mask is not None else np.zeros(signal_second,
+                                                                                               dtype=int),
+        "BCG_AmpChange_Label": bcg_amp_change_mask if bcg_amp_change_mask is not None else np.zeros(signal_second,
+                                                                                                    dtype=int)
+    }
+
+    mask_label_save_name = f"{samp_id}_Processed_Labels.csv"
+    utils.save_process_label(save_path=save_samp_path / mask_label_save_name, save_dict=save_dict)
+
+
+if __name__ == '__main__':
+    yaml_path =  project_root_path / "dataset_config/HYS_config.yaml"
+    disable_df_path = project_root_path / "排除区间.xlsx"
+
+    conf = utils.load_dataset_conf(yaml_path)
+    select_ids = conf["select_ids"]
+    root_path = Path(conf["root_path"])
+    save_path = Path(conf["mask_save_path"])
+
+    print(f"select_ids: {select_ids}")
+    print(f"root_path: {root_path}")
+    print(f"save_path: {save_path}")
+
+    org_signal_root_path = root_path / "OrgBCG_Aligned"
+    label_root_path = root_path / "Label"
+
+    all_samp_disable_df = utils.read_disable_excel(disable_df_path)
+
+    # process_one_signal(select_ids[6], show=True)
+    #
+    for samp_id in select_ids:
+        print(f"Processing sample ID: {samp_id}")
+        process_one_signal(samp_id, show=False)
+        print(f"Finished processing sample ID: {samp_id}\n\n")
--- a/event_mask_process/SHHS1_process.py
+++ b/event_mask_process/SHHS1_process.py
@ -0,0 +1,42 @@
+import sys
+from pathlib import Path
+
+sys.path.append(str(Path(__file__).resolve().parent.parent))
+project_root_path = Path(__file__).resolve().parent.parent
+
+import shutil
+import draw_tools
+import utils
+import numpy as np
+import signal_method
+import os
+import mne
+from tqdm import tqdm
+import xml.etree.ElementTree as ET
+import re
+
+# 获取分期和事件标签，以及不可用区间
+
+def process_one_signal(samp_id, show=False):
+
+
+
+
+
+
+
+
+if __name__ == '__main__':
+    yaml_path =  project_root_path / "dataset_config/SHHS1_config.yaml"
+
+    conf = utils.load_dataset_conf(yaml_path)
+
+    root_path = Path(conf["root_path"])
+    save_path = Path(conf["mask_save_path"])
+
+    print(f"root_path: {root_path}")
+    print(f"save_path: {save_path}")
+
+    org_signal_root_path = root_path / "OrgBCG_Aligned"
+    label_root_path = root_path / "Label"
+
--- a/event_mask_process/init.py
+++ b/event_mask_process/init.py
--- a/signal_method/init.py
+++ b/signal_method/init.py
@ -0,0 +1,6 @@
+from .rule_base_event import detect_low_amplitude_signal, detect_movement
+from .rule_base_event import position_based_sleep_recognition_v2, position_based_sleep_recognition_v3
+from .rule_base_event import movement_revise
+from .time_metrics import calc_mav_by_slide_windows
+from .signal_process import signal_filter_split, rpeak2hr, psg_effort_filter, rpeak2rri_interpolation
+from .normalize_method import normalize_resp_signal_by_segment
--- a/signal_method/normalize_method.py
+++ b/signal_method/normalize_method.py
@ -0,0 +1,52 @@
+import utils
+import pandas as pd
+import numpy as np
+from scipy import signal
+
+def normalize_resp_signal_by_segment(resp_signal: np.ndarray, resp_fs, movement_mask, enable_list):
+    # 根据呼吸信号的幅值改变区间，对每段进行Z-Score标准化
+    normalized_resp_signal = np.zeros_like(resp_signal)
+    # 全部填成nan
+    normalized_resp_signal[:] = np.nan
+
+    resp_signal_no_movement = resp_signal.copy()
+
+
+    resp_signal_no_movement[np.array(movement_mask == 1).repeat(resp_fs)] = np.nan
+
+
+    for i in range(len(enable_list)):
+        enable_start = enable_list[i][0] * resp_fs
+        enable_end = enable_list[i][1] * resp_fs
+        segment = resp_signal_no_movement[enable_start:enable_end]
+
+        # print(f"Normalizing segment {i+1}/{len(enable_list)}: start={enable_start}, end={enable_end}, length={len(segment)}")
+
+        segment_mean = np.nanmean(segment)
+        segment_std = np.nanstd(segment)
+        if segment_std == 0:
+            raise ValueError(f"segment_std is zero! segment_start: {enable_start}, segment_end: {enable_end}")
+
+        # 同下一个enable区间的体动一起进行标准化
+        if i <= len(enable_list) - 2:
+            enable_end = enable_list[i + 1][0] * resp_fs
+        raw_segment = resp_signal[enable_start:enable_end]
+        normalized_resp_signal[enable_start:enable_end] = (raw_segment - segment_mean) / segment_std
+
+
+    #如果enable区间不从0开始，则将前面的部分也进行标准化
+    if enable_list[0][0] > 0:
+        new_enable_start = 0
+        enable_start = enable_list[0][0] * resp_fs
+        enable_end = enable_list[0][1] * resp_fs
+        segment = resp_signal_no_movement[enable_start:enable_end]
+
+        segment_mean = np.nanmean(segment)
+        segment_std = np.nanstd(segment)
+        if segment_std == 0:
+            raise ValueError(f"segment_std is zero! segment_start: {enable_start}, segment_end: {enable_end}")
+
+        raw_segment = resp_signal[new_enable_start:enable_start]
+        normalized_resp_signal[new_enable_start:enable_start] = (raw_segment - segment_mean) / segment_std
+
+    return normalized_resp_signal
--- a/signal_method/rule_base_event.py
+++ b/signal_method/rule_base_event.py
@ -0,0 +1,688 @@
+import utils
+from utils.operation_tools import timing_decorator
+import numpy as np
+from utils import merge_short_gaps, remove_short_durations, event_mask_2_list, collect_values
+from signal_method.time_metrics import calc_mav_by_slide_windows
+
+
+@timing_decorator()
+def detect_movement(signal_data, sampling_rate, window_size_sec=2, stride_sec=None,
+                    std_median_multiplier=4.5, compare_intervals_sec=[30, 60],
+                    interval_multiplier=2.5,
+                    merge_gap_sec=10, min_duration_sec=5,
+                    low_amplitude_periods=None):
+    """
+    检测信号中的体动状态，结合两种方法：标准差比较和前后窗口幅值对比。
+    使用反射填充处理信号边界。
+
+    参数：
+    - signal_data: numpy array，输入的信号数据
+    - sampling_rate: int，信号的采样率（Hz）
+    - window_size_sec: float，分析窗口的时长（秒），默认值为 2 秒
+    - stride_sec: float，窗口滑动步长（秒），默认值为None（等于window_size_sec，无重叠）
+    - std_median_multiplier: float，标准差中位数的乘数阈值，默认值为 4.5
+    - compare_intervals_sec: list，用于比较的时间间隔列表（秒），默认为 [30, 60]
+    - interval_multiplier: float，间隔中位数的上限乘数，默认值为 2.5
+    - merge_gap_sec: float，要合并的体动状态之间的最大间隔（秒），默认值为 10 秒
+    - min_duration_sec: float，要保留的体动状态的最小持续时间（秒），默认值为 5 秒
+    - low_amplitude_periods: numpy array，低幅值期间的掩码（1表示低幅值期间），默认为None
+
+    返回：
+    - movement_mask: numpy array，体动状态的掩码（1表示体动，0表示睡眠）
+    """
+    # 计算窗口大小（样本数）
+    window_samples = int(window_size_sec * sampling_rate)
+
+    # 如果未指定步长，设置为窗口大小（无重叠）
+    if stride_sec is None:
+        stride_sec = window_size_sec
+
+    # 计算步长（样本数）
+    stride_samples = int(stride_sec * sampling_rate)
+
+    # 确保步长至少为1
+    stride_samples = max(1, stride_samples)
+
+    # 计算需要的最大填充大小（基于比较间隔）
+    max_interval_samples = int(max(compare_intervals_sec) * sampling_rate)
+
+    # 应用反射填充以正确处理边界
+    # 填充大小为最大比较间隔的一半，以确保边界有足够的上下文
+    pad_size = max_interval_samples
+    padded_signal = np.pad(signal_data, pad_size, mode='reflect')
+
+    # 计算填充后的窗口数量
+    num_windows = max(1, (len(padded_signal) - window_samples) // stride_samples + 1)
+
+    # 初始化窗口标准差数组
+    window_std = np.zeros(num_windows)
+    # 计算每个窗口的标准差
+    # 分窗计算标准差
+    for i in range(num_windows):
+        start_idx = i * stride_samples
+        end_idx = min(start_idx + window_samples, len(padded_signal))
+
+        # 处理窗口，包括可能不完整的最后一个窗口
+        window_data = padded_signal[start_idx:end_idx]
+        if len(window_data) > 0:
+            window_std[i] = np.std(window_data, ddof=1)
+        else:
+            window_std[i] = 0
+
+    # 计算原始信号对应的窗口索引范围
+    # 填充后，原始信号从pad_size开始
+    orig_start_window = pad_size // stride_samples
+    if stride_sec == 1:
+        orig_end_window = orig_start_window + (len(signal_data) // stride_samples)
+    else:
+        orig_end_window = orig_start_window + (len(signal_data) // stride_samples) + 1
+
+    # 只保留原始信号对应的窗口标准差
+    original_window_std = window_std[orig_start_window:orig_end_window]
+    num_original_windows = len(original_window_std)
+
+    # 创建时间点数组（秒）
+    time_points = np.arange(num_original_windows) * stride_sec
+
+    # # 如果提供了低幅值期间的掩码，则在计算全局中位数时排除这些期间
+    # if low_amplitude_periods is not None and len(low_amplitude_periods) == num_original_windows:
+    #     valid_std = original_window_std[low_amplitude_periods == 0]
+    #     if len(valid_std) == 0:  # 如果所有窗口都在低幅值期间
+    #         valid_std = original_window_std  # 使用全部窗口
+    # else:
+    #     valid_std = original_window_std
+
+    valid_std = original_window_std  ##20250418新修改
+
+    # ---------------------- 方法一：基于STD的体动判定 ----------------------#
+    # 计算所有有效窗口标准差的中位数
+    median_std = np.median(valid_std)
+
+    # 当窗口标准差大于中位数的倍数，判定为体动状态
+    std_movement = np.where((original_window_std > (median_std * std_median_multiplier)), 1, 0)
+
+    # ------------------ 方法二：基于前后信号幅值变化的体动判定 ------------------#
+    amplitude_movement = np.zeros(num_original_windows, dtype=int)
+
+    # 定义基于时间粒度的比较间隔索引
+    compare_intervals_idx = [int(interval // stride_sec) for interval in compare_intervals_sec]
+
+    # 逐窗口判断
+    for win_idx in range(num_original_windows):
+        # 全局索引（在填充后的窗口数组中）
+        global_win_idx = win_idx + orig_start_window
+
+        # 对每个比较间隔进行检查
+        for interval_idx in compare_intervals_idx:
+            # 确定比较范围的结束索引（在填充后的窗口数组中）
+            end_idx = min(global_win_idx + interval_idx, len(window_std))
+
+            # 提取相应时间范围内的标准差值
+            if global_win_idx < end_idx:
+                interval_std = window_std[global_win_idx:end_idx]
+
+                # 计算该间隔的中位数
+                interval_median = np.median(interval_std)
+
+                # 计算上下阈值
+                upper_threshold = interval_median * interval_multiplier
+
+                # 检查当前窗口是否超出阈值范围，如果超出则直接标记为体动
+                if window_std[global_win_idx] > upper_threshold:
+                    amplitude_movement[win_idx] = 1
+                    break  # 一旦确定为体动就不需要继续检查其他间隔
+
+    # 将两种方法的结果合并：只要其中一种方法判定为体动，最终结果就是体动
+    movement_mask = np.logical_or(std_movement, amplitude_movement).astype(int)
+    raw_movement_mask = movement_mask
+
+    # 如果需要合并间隔小的体动状态
+    if merge_gap_sec > 0:
+        movement_mask = merge_short_gaps(movement_mask, time_points, merge_gap_sec)
+
+    # 如果需要移除短时体动状态
+    if min_duration_sec > 0:
+        movement_mask = remove_short_durations(movement_mask, time_points, min_duration_sec)
+
+    # raw_movement_mask， movement_mask恢复对应秒数，而不是点数
+    raw_movement_mask = raw_movement_mask.repeat(stride_sec)[:len(signal_data) // sampling_rate]
+    movement_mask = movement_mask.repeat(stride_sec)[:len(signal_data) // sampling_rate]
+
+    # 比较剔除的体动，如果被剔除的体动所在区域有高于3std的幅值，则不剔除
+    removed_movement_mask = (raw_movement_mask - movement_mask) > 0
+    removed_movement_start = np.where(np.diff(np.concatenate([[0], removed_movement_mask])) == 1)[0]
+    removed_movement_end = np.where(np.diff(np.concatenate([removed_movement_mask, [0]])) == -1)[0]
+
+    for start, end in zip(removed_movement_start, removed_movement_end):
+        # print(start ,end)
+        # 计算剔除的体动区域的幅值
+        if np.nanmax(signal_data[start * sampling_rate:(end + 1) * sampling_rate]) > median_std * std_median_multiplier:
+            movement_mask[start:end + 1] = 1
+
+    # raw体动起止位置 [[start, end], [start, end], ...]
+    raw_movement_position_list = event_mask_2_list(raw_movement_mask)
+
+    # merge体动起止位置 [[start, end], [start, end], ...]
+    movement_position_list = event_mask_2_list(movement_mask)
+
+    return raw_movement_mask, movement_mask, raw_movement_position_list, movement_position_list
+
+
+def movement_revise(signal_data, sampling_rate, movement_mask, movement_list, up_interval_multiplier: float,
+                    down_interval_multiplier: float, compare_intervals_sec, merge_gap_sec, min_duration_sec, verbose=False):
+    """
+    基于标准差对已有体动掩码进行修正。 用于大尺度的体动检测后的位置精细修正
+
+    参数：
+    - signal_data: numpy array，输入的信号数据
+    - sampling_rate: int，信号的采样率（Hz）
+    - movement_mask: numpy array，已有的体动掩码（1表示体动，0表示睡眠）
+
+    返回：
+    - revised_movement_mask: numpy array，修正后的体动掩码
+    """
+    window_size = sampling_rate
+    stride_size = sampling_rate
+
+    time_points = np.arange(len(signal_data))
+
+    compare_size = int(compare_intervals_sec // (stride_size / sampling_rate))
+
+    _, mav = calc_mav_by_slide_windows(signal_data, movement_mask=None, low_amp_mask=None, sampling_rate=sampling_rate,
+                                       window_second=4, step_second=1,
+                                       inner_window_second=4)
+
+    # 往左右两边取compare_size个点的mav，取平均值
+    for start, end in movement_list:
+        left_points = start - 20
+        right_points = end + 20
+
+        left_values = collect_values(arr=mav, index=left_points, step=-1, limit=compare_size, mask=movement_mask)
+        right_values = collect_values(arr=mav, index=right_points, step=1, limit=compare_size, mask=movement_mask)
+
+        left_value_metrics = np.median(left_values) if len(left_values) > 0 else 0
+        right_value_metrics = np.median(right_values) if len(right_values) > 0 else 0
+
+        # if left_value_metrics == 0:
+        #     value_metrics = right_value_metrics
+        # elif right_value_metrics == 0:
+        #     value_metrics = left_value_metrics
+        # else:
+        #     value_metrics = np.mean([left_value_metrics, right_value_metrics])
+
+        if left_value_metrics == 0:
+            left_value_metrics = right_value_metrics
+        elif right_value_metrics == 0:
+            right_value_metrics = left_value_metrics
+
+        if verbose:
+            print(f"Revising movement from index {start} to {end}, left_metric: {left_value_metrics:.2f}, right_metric: {right_value_metrics:.2f}")
+
+        for i in range(left_points, right_points):
+            if i < 0 or i >= len(mav):
+                continue
+            if i < start:
+                value_metrics = left_value_metrics
+            elif i > end:
+                value_metrics = right_value_metrics
+            else:
+                value_metrics = (left_value_metrics + right_value_metrics) / 2
+
+            if mav[i] > (value_metrics * up_interval_multiplier) and movement_mask[i] == 0:
+                movement_mask[i] = 1
+                if verbose:
+                    print(f"Normal revised at index {i}, mav: {mav[i]:.2f}, threshold: {value_metrics * up_interval_multiplier:.2f}")
+            elif mav[i] < (value_metrics * down_interval_multiplier) and movement_mask[i] == 1:
+                movement_mask[i] = 0
+                if verbose:
+                    print(f"Movement revised at index {i}, mav: {mav[i]:.2f}, threshold: {value_metrics * down_interval_multiplier:.2f}")
+            else:
+                if verbose:
+                    print(f"No revision at index {i}, mav: {mav[i]:.2f}, up_threshold: {value_metrics * up_interval_multiplier:.2f}, down_threshold: {value_metrics * down_interval_multiplier:.2f}")
+        #
+        # 逐秒遍历mav，判断是否需要修正
+        # print(f"Revising movement from index {start} to {end}, left_mean: {left_value_mean:.2f}, right_mean: {right_value_mean:.2f}, mean: {value_mean:.2f}")
+        # for i in range(left_points, right_points):
+        #     if i < 0 or i >= len(mav):
+        #         continue
+        #     # print(f"Index {i}, mav: {mav[i]:.2f}, left_mean: {left_value_mean:.2f}, right_mean: {right_value_mean:.2f}, mean: {value_mean:.2f}")
+        #     if mav[i] > (value_metrics * up_interval_multiplier):
+        #         movement_mask[i] = 1
+        #         # print(f"Movement revised at index {i}, mav: {mav[i]:.2f}, threshold: {value_mean * up_interval_multiplier:.2f}")
+        #     elif mav[i] < (value_metrics * down_interval_multiplier):
+        #         movement_mask[i] = 0
+        #         # print(f"Movement revised at index {i}, mav: {mav[i]:.2f}, threshold: {value_mean * down_interval_multiplier:.2f}")
+        #     # else:
+        #     #     print(f"No revision at index {i}, mav: {mav[i]:.2f}, up_threshold: {value_mean * up_interval_multiplier:.2f}, down_threshold: {value_mean * down_interval_multiplier:.2f}")
+        #     #
+    # 如果需要合并间隔小的体动状态
+    if merge_gap_sec > 0:
+        movement_mask = merge_short_gaps(movement_mask, time_points, merge_gap_sec)
+
+    # 如果需要移除短时体动状态
+    if min_duration_sec > 0:
+        movement_mask = remove_short_durations(movement_mask, time_points, min_duration_sec)
+
+    movement_list = event_mask_2_list(movement_mask)
+    return movement_mask, movement_list
+
+
+@timing_decorator()
+def detect_low_amplitude_signal(signal_data, sampling_rate, window_size_sec=1, stride_sec=None,
+                                amplitude_threshold=50, merge_gap_sec=10, min_duration_sec=5):
+    """
+    检测信号中的低幅值状态，通过计算RMS值判断信号强度是否低于设定阈值。
+
+    参数：
+    - signal_data: numpy array，输入的信号数据
+    - sampling_rate: int，信号的采样率（Hz）
+    - window_size_sec: float，分析窗口的时长（秒），默认值为 1 秒
+    - stride_sec: float，窗口滑动步长（秒），默认值为None（等于window_size_sec，无重叠）
+    - amplitude_threshold: float，RMS阈值，低于此值表示低幅值状态，默认值为 50
+    - merge_gap_sec: float，要合并的状态之间的最大间隔（秒），默认值为 10 秒
+    - min_duration_sec: float，要保留的状态的最小持续时间（秒），默认值为 5 秒
+
+    返回：
+    - low_amplitude_mask: numpy array，低幅值状态的掩码（1表示低幅值，0表示正常幅值）
+    """
+    # 计算窗口大小（样本数）
+    window_samples = int(window_size_sec * sampling_rate)
+
+    # 如果未指定步长，设置为窗口大小（无重叠）
+    if stride_sec is None:
+        stride_sec = window_size_sec
+
+    # 计算步长（样本数）
+    stride_samples = int(stride_sec * sampling_rate)
+
+    # 确保步长至少为1
+    stride_samples = max(sampling_rate, stride_samples)
+
+    # 处理信号边界，使用反射填充
+    pad_size = window_samples // 2
+    padded_signal = np.pad(signal_data, pad_size, mode='reflect')
+
+    # 计算填充后的窗口数量
+    num_windows = max(1, (len(padded_signal) - window_samples) // stride_samples + 1)
+
+    # 初始化RMS值数组
+    rms_values = np.zeros(num_windows)
+
+    # 计算每个窗口的RMS值
+    for i in range(num_windows):
+        start_idx = i * stride_samples
+        end_idx = min(start_idx + window_samples, len(signal_data))
+
+        # 处理窗口，包括可能不完整的最后一个窗口
+        window_data = signal_data[start_idx:end_idx]
+        if len(window_data) > 0:
+            rms_values[i] = np.sqrt(np.mean(window_data ** 2))
+        else:
+            rms_values[i] = 0
+
+    # 生成初始低幅值掩码：RMS低于阈值的窗口标记为1（低幅值），其他为0
+    low_amplitude_mask = np.where(rms_values <= amplitude_threshold, 1, 0)
+
+    # 计算原始信号对应的窗口索引范围
+    orig_start_window = pad_size // stride_samples
+    if stride_sec == 1:
+        orig_end_window = orig_start_window + (len(signal_data) // stride_samples)
+    else:
+        orig_end_window = orig_start_window + (len(signal_data) // stride_samples) + 1
+
+    # 只保留原始信号对应的窗口低幅值掩码
+    low_amplitude_mask = low_amplitude_mask[orig_start_window:orig_end_window]
+    # print("low_amplitude_mask_length: ", len(low_amplitude_mask))
+    num_original_windows = len(low_amplitude_mask)
+
+    # 转换为时间轴上的状态序列
+    # 计算每个窗口对应的时间点（秒）
+    time_points = np.arange(num_original_windows) * stride_sec
+
+    # 如果需要合并间隔小的状态
+    if merge_gap_sec > 0:
+        low_amplitude_mask = merge_short_gaps(low_amplitude_mask, time_points, merge_gap_sec)
+
+    # 如果需要移除短时状态
+    if min_duration_sec > 0:
+        low_amplitude_mask = remove_short_durations(low_amplitude_mask, time_points, min_duration_sec)
+
+    low_amplitude_mask = low_amplitude_mask.repeat(stride_sec)[:len(signal_data) // sampling_rate]
+
+    # 低幅值状态起止位置 [[start, end], [start, end], ...]
+    low_amplitude_position_list = event_mask_2_list(low_amplitude_mask)
+
+    return low_amplitude_mask, low_amplitude_position_list
+
+
+def get_typical_segment_for_continues_signal(signal_data, sampling_rate=100, window_size=30, step_size=1):
+    """
+    获取十个片段
+    :param signal_data: 信号数据
+    :param sampling_rate: 采样率
+    :param window_size: 窗口大小（秒）
+    :param step_size: 步长（秒）
+    :return: 典型片段列表
+    """
+    pass
+
+
+# 基于体动位置和幅值的睡姿识别
+# 主要是依靠体动mask，将整夜分割成多个有效片段，然后每个片段计算幅值指标，判断两个片段的幅值指标是否存在显著差异，如果存在显著差异，则认为存在睡姿变化
+# 考虑到每个片段长度为10s，所以每个片段的幅值指标计算时间长度为10s，然后计算每个片段的幅值指标
+# 仅对比相邻片段的幅值指标，如果存在显著差异，则认为存在睡姿变化，即每个体动相邻的30秒内存在睡姿变化，如果片段不足30秒，则按实际长度对比
+
+@timing_decorator()
+def position_based_sleep_recognition_v1(signal_data, movement_mask, sampling_rate=100, window_size_sec=30,
+                                        interval_to_movement=10):
+    mav_calc_window_sec = 2  # 计算mav的窗口大小，单位秒
+    # 获取有效片段起止位置
+    valid_mask = 1 - movement_mask
+    valid_starts = np.where(np.diff(np.concatenate([[0], valid_mask])) == 1)[0]
+    valid_ends = np.where(np.diff(np.concatenate([valid_mask, [0]])) == -1)[0]
+
+    movement_start = np.where(np.diff(np.concatenate([[0], movement_mask])) == 1)[0]
+    movement_end = np.where(np.diff(np.concatenate([movement_mask, [0]])) == -1)[0]
+
+    segment_left_average_amplitude = []
+    segment_right_average_amplitude = []
+    segment_left_average_energy = []
+    segment_right_average_energy = []
+
+    # window_samples = int(window_size_sec * sampling_rate)
+    # pad_size = window_samples // 2
+    # padded_signal = np.pad(signal_data, pad_size, mode='reflect')
+
+    for start, end in zip(valid_starts, valid_ends):
+        start *= sampling_rate
+        end *= sampling_rate
+        # 避免过短的片段
+        if end - start <= sampling_rate:  # 小于1秒的片段不考虑
+            continue
+            # 获取当前片段数据
+
+
+        elif end - start < (window_size_sec * interval_to_movement + 1) * sampling_rate:
+            left_start = start
+            left_end = min(end, left_start + window_size_sec * sampling_rate)
+            right_start = max(start, end - window_size_sec * sampling_rate)
+            right_end = end
+        else:
+            left_start = start + interval_to_movement * sampling_rate
+            left_end = left_start + window_size_sec * sampling_rate
+            right_start = end - interval_to_movement * sampling_rate - window_size_sec * sampling_rate
+            right_end = end
+
+        # 新的end - start确保为200的整数倍
+        if (left_end - left_start) % (mav_calc_window_sec * sampling_rate) != 0:
+            left_end = left_start + ((left_end - left_start) // (mav_calc_window_sec * sampling_rate)) * (
+                    mav_calc_window_sec * sampling_rate)
+        if (right_end - right_start) % (mav_calc_window_sec * sampling_rate) != 0:
+            right_end = right_start + ((right_end - right_start) // (mav_calc_window_sec * sampling_rate)) * (
+                    mav_calc_window_sec * sampling_rate)
+
+        # 计算每个片段的幅值指标
+        left_mav = np.mean(np.max(signal_data[left_start:left_end].reshape(-1, mav_calc_window_sec * sampling_rate),
+                                  axis=0)) - np.mean(
+            np.min(signal_data[left_start:left_end].reshape(-1, mav_calc_window_sec * sampling_rate), axis=0))
+        right_mav = np.mean(
+            np.max(signal_data[right_start:right_end].reshape(-1, mav_calc_window_sec * sampling_rate),
+                   axis=0)) - np.mean(
+            np.min(signal_data[right_start:right_end].reshape(-1, mav_calc_window_sec * sampling_rate), axis=0))
+        segment_left_average_amplitude.append(left_mav)
+        segment_right_average_amplitude.append(right_mav)
+
+        left_energy = np.sum(np.abs(signal_data[left_start:left_end] ** 2))
+        right_energy = np.sum(np.abs(signal_data[right_start:right_end] ** 2))
+        segment_left_average_energy.append(left_energy)
+        segment_right_average_energy.append(right_energy)
+
+    position_changes = []
+    position_change_times = []
+    # 判断是否存在显著变化 (可根据实际情况调整阈值)
+    threshold_amplitude = 0.1  # 幅值变化阈值
+    threshold_energy = 0.1  # 能量变化阈值
+
+    for i in range(1, len(segment_left_average_amplitude)):
+        # 计算幅值指标的变化率
+        left_amplitude_change = abs(segment_left_average_amplitude[i] - segment_left_average_amplitude[i - 1]) / max(
+            segment_left_average_amplitude[i - 1], 1e-6)
+        right_amplitude_change = abs(segment_right_average_amplitude[i] - segment_right_average_amplitude[i - 1]) / max(
+            segment_right_average_amplitude[i - 1], 1e-6)
+
+        # 计算能量指标的变化率
+        left_energy_change = abs(segment_left_average_energy[i] - segment_left_average_energy[i - 1]) / max(
+            segment_left_average_energy[i - 1], 1e-6)
+        right_energy_change = abs(segment_right_average_energy[i] - segment_right_average_energy[i - 1]) / max(
+            segment_right_average_energy[i - 1], 1e-6)
+
+        # 如果左右通道中的任一通道同时满足幅值和能量的变化阈值，则认为存在姿势变化
+        left_significant_change = (left_amplitude_change > threshold_amplitude) and (
+                left_energy_change > threshold_energy)
+        right_significant_change = (right_amplitude_change > threshold_amplitude) and (
+                right_energy_change > threshold_energy)
+
+        if left_significant_change or right_significant_change:
+            # 记录姿势变化发生的时间点 用当前分割的体动的起始位置和结束位置表示
+            position_changes.append(1)
+            position_change_times.append((movement_start[i - 1], movement_end[i - 1]))
+        else:
+            position_changes.append(0)  # 0表示不存在姿势变化
+
+    return position_changes, position_change_times
+
+
+def position_based_sleep_recognition_v2(signal_data, movement_mask, sampling_rate=100):
+    """
+
+    :param signal_data:
+    :param movement_mask:  mask的采样率为1Hz
+    :param sampling_rate:
+    :param window_size_sec:
+    :return:
+    """
+    mav_calc_window_sec = 2  # 计算mav的窗口大小，单位秒
+    # 获取有效片段起止位置
+    valid_mask = 1 - movement_mask
+    valid_starts = np.where(np.diff(np.concatenate([[0], valid_mask])) == 1)[0]
+    valid_ends = np.where(np.diff(np.concatenate([valid_mask, [0]])) == -1)[0]
+
+    movement_start = np.where(np.diff(np.concatenate([[0], movement_mask])) == 1)[0]
+    movement_end = np.where(np.diff(np.concatenate([movement_mask, [0]])) == -1)[0]
+
+    segment_average_amplitude = []
+    segment_average_energy = []
+
+    signal_data_no_movement = signal_data.copy()
+    for start, end in zip(movement_start, movement_end):
+        signal_data_no_movement[start * sampling_rate:end * sampling_rate] = np.nan
+
+    # from matplotlib import pyplot as plt
+    # plt.plot(signal_data, alpha=0.3, color='gray')
+    # plt.plot(signal_data_no_movement, color='blue', linewidth=1)
+    # plt.show()
+
+    for start, end in zip(valid_starts, valid_ends):
+        start *= sampling_rate
+        end *= sampling_rate
+        # 避免过短的片段
+        if end - start <= sampling_rate:  # 小于1秒的片段不考虑
+            continue
+
+        # 新的end - start确保为200的整数倍
+        if (end - start) % (mav_calc_window_sec * sampling_rate) != 0:
+            end = start + ((end - start) // (mav_calc_window_sec * sampling_rate)) * (
+                    mav_calc_window_sec * sampling_rate)
+
+        # 计算每个片段的幅值指标
+        mav = np.nanmean(
+            np.nanmax(signal_data_no_movement[start:end].reshape(-1, mav_calc_window_sec * sampling_rate),
+                      axis=0)) - np.nanmean(
+            np.nanmin(signal_data_no_movement[start:end].reshape(-1, mav_calc_window_sec * sampling_rate), axis=0))
+        segment_average_amplitude.append(mav)
+
+        energy = np.nansum(np.abs(signal_data_no_movement[start:end] ** 2))
+        segment_average_energy.append(energy)
+
+    position_changes = np.zeros(len(signal_data) // sampling_rate, dtype=int)
+    position_change_times = []
+    # 判断是否存在显著变化 (可根据实际情况调整阈值)
+    threshold_amplitude = 0.1  # 幅值变化阈值
+    threshold_energy = 0.1  # 能量变化阈值
+
+    for i in range(1, len(segment_average_amplitude)):
+        # 计算幅值指标的变化率
+        amplitude_change = abs(segment_average_amplitude[i] - segment_average_amplitude[i - 1]) / max(
+            segment_average_amplitude[i - 1], 1e-6)
+
+        # 计算能量指标的变化率
+        energy_change = abs(segment_average_energy[i] - segment_average_energy[i - 1]) / max(
+            segment_average_energy[i - 1], 1e-6)
+
+        significant_change = (amplitude_change > threshold_amplitude) and (energy_change > threshold_energy)
+
+        if significant_change:
+            # 记录姿势变化发生的时间点 用当前分割的体动的起始位置和结束位置表示
+            position_changes[movement_start[i - 1]:movement_end[i - 1]] = 1
+            position_change_times.append((movement_start[i - 1], movement_end[i - 1]))
+
+    return position_changes, position_change_times
+
+
+def position_based_sleep_recognition_v3(signal_data, movement_mask, movement_list, sampling_rate, mav_calc_window_sec,
+                                        threshold_amplitude, threshold_energy, verbose=False):
+    """
+
+    :param threshold_energy:
+    :param threshold_amplitude:
+    :param mav_calc_window_sec:
+    :param movement_list:
+    :param signal_data:
+    :param movement_mask:  mask的采样率为1Hz
+    :param sampling_rate:
+    :param window_size_sec:
+    :return:
+    """
+    # 获取有效片段起止位置
+
+    valid_list = utils.event_mask_2_list(movement_mask, event_true=False)
+
+    segment_average_amplitude = []
+    segment_average_energy = []
+
+    signal_data_no_movement = signal_data.copy()
+    for start, end in movement_list:
+        signal_data_no_movement[start * sampling_rate:end * sampling_rate] = np.nan
+
+    # from matplotlib import pyplot as plt
+    # plt.plot(signal_data, alpha=0.3, color='gray')
+    # plt.plot(signal_data_no_movement, color='blue', linewidth=1)
+    # plt.show()
+
+    if len(valid_list) < 2:
+        return [], []
+
+    def clac_mav(data_segment):
+        # 确定data_segment长度为mav_calc_window_sec的整数倍
+        if len(data_segment) % (mav_calc_window_sec * sampling_rate) != 0:
+            data_segment = data_segment[:-(len(data_segment) % (mav_calc_window_sec * sampling_rate))]
+
+        mav = np.nanstd(
+            np.nanmax(data_segment.reshape(-1, mav_calc_window_sec * sampling_rate),
+                      axis=0) -
+            np.nanmin(data_segment.reshape(-1, mav_calc_window_sec * sampling_rate), axis=0))
+        return mav
+
+    def clac_energy(data_segment):
+        energy = np.nansum(np.abs(data_segment ** 2)) // (len(data_segment) // sampling_rate)
+        return energy
+
+    def calc_mav_by_quantiles(data_segment):
+        # 先计算所有的mav值
+        if len(data_segment) % (mav_calc_window_sec * sampling_rate) != 0:
+            data_segment = data_segment[:-(len(data_segment) % (mav_calc_window_sec * sampling_rate))]
+
+        mav_values = np.nanmax(data_segment.reshape(-1, mav_calc_window_sec * sampling_rate), axis=1) - np.nanmin(
+            data_segment.reshape(-1, mav_calc_window_sec * sampling_rate), axis=1)
+        # 计算分位数
+        q20 = np.nanpercentile(mav_values, 20)
+        q80 = np.nanpercentile(mav_values, 80)
+
+        mav_values = mav_values[(mav_values >= q20) & (mav_values <= q80)]
+        mav = np.nanmean(mav_values)
+        return mav
+
+    position_changes = np.zeros(len(signal_data) // sampling_rate, dtype=int)
+    position_change_list = []
+
+    pre_valid_start = valid_list[0][0] * sampling_rate
+    pre_valid_end = valid_list[0][1] * sampling_rate
+
+    if verbose:
+        print(f"Total movement segments to analyze: {len(movement_list)}")
+        print(f"Total valid segments available: {len(valid_list)}")
+
+    for i in range(len(movement_list)):
+        if verbose:
+            print(f"Analyzing movement segment {i + 1}/{len(movement_list)}")
+
+        if i + 1 >= len(valid_list):
+            if verbose:
+                print("No more valid segments to compare. Ending analysis.")
+            break
+
+        next_valid_start = valid_list[i + 1][0] * sampling_rate
+        next_valid_end = valid_list[i + 1][1] * sampling_rate
+
+        movement_start = movement_list[i][0]
+        movement_end = movement_list[i][1]
+
+        # 避免过短的片段
+        if movement_end - movement_start <= 1:  # 小于1秒的片段不考虑
+            if verbose:
+                print(
+                    f"Skipping movement segment {i + 1} due to insufficient length. movement start: {movement_start}, movement end: {movement_end}")
+                pre_valid_start = pre_valid_start
+                pre_valid_end = next_valid_end
+            continue
+
+        # 计算前后片段的幅值和能量
+        # left_mav = clac_mav(signal_data_no_movement[pre_valid_start:pre_valid_end])
+        # right_mav = clac_mav(signal_data_no_movement[next_valid_start:next_valid_end])
+        # left_energy = clac_energy(signal_data_no_movement[pre_valid_start:pre_valid_end])
+        # right_energy = clac_energy(signal_data_no_movement[next_valid_start:next_valid_end])
+
+        left_mav = calc_mav_by_quantiles(signal_data_no_movement[pre_valid_start:pre_valid_end])
+        right_mav = calc_mav_by_quantiles(signal_data_no_movement[next_valid_start:next_valid_end])
+
+
+        # 计算幅值指标的变化率
+        amplitude_change = abs(right_mav - left_mav) / max(left_mav, 1e-6)
+        # # 计算能量指标的变化率
+        # energy_change = abs(right_energy - left_energy) / max(left_energy, 1e-6)
+
+        # significant_change = (amplitude_change > threshold_amplitude) and (energy_change > threshold_energy)
+        significant_change = (amplitude_change > threshold_amplitude)
+        if significant_change:
+            # print(
+            #     f"Significant position change detected between segments {movement_start} and {movement_end}:  left:{pre_valid_start}to{pre_valid_end} left_mav={left_mav:.2f}, right_mav={right_mav:.2f},  amplitude_change={amplitude_change:.2f}, left_energy={left_energy:.2f}, right_energy={right_energy:.2f}, energy_change={energy_change:.2f}")
+            if verbose:
+                print(f"Significant position change detected between segments {movement_start}s and {movement_end}:s left:{pre_valid_start}to{pre_valid_end}  left_mav={left_mav:.2f}, right:{next_valid_start}to{next_valid_end} right_mav={right_mav:.2f},  amplitude_change={amplitude_change:.2f}")
+            # 记录姿势变化发生的时间点 用当前分割的体动的起始位置和结束位置表示
+            position_changes[movement_start:movement_end] = 1
+            position_change_list.append(movement_list[i])
+            # 更新前后片段
+            pre_valid_start = next_valid_start
+            pre_valid_end = next_valid_end
+
+        else:
+            # print(
+            #     f"No significant position change between segments {movement_start} and {movement_end}: left:{pre_valid_start}to{pre_valid_end}  left_mav={left_mav:.2f}, right_mav={right_mav:.2f},  amplitude_change={amplitude_change:.2f}, left_energy={left_energy:.2f}, right_energy={right_energy:.2f}, energy_change={energy_change:.2f}")
+            if verbose:
+                print(f"No significant position change between segments {movement_start} and {movement_end}: left:{pre_valid_start}to{pre_valid_end}  left_mav={left_mav:.2f}, right_mav={right_mav:.2f},  amplitude_change={amplitude_change:.2f}")
+
+            # 仅更新前片段
+            pre_valid_start = pre_valid_start
+            pre_valid_end = next_valid_end
+
+    return position_changes, position_change_list
--- a/signal_method/shhs_tools.py
+++ b/signal_method/shhs_tools.py
@ -0,0 +1,62 @@
+import xml.etree.ElementTree as ET
+ANNOTATION_MAP = {
+    "Wake|0": 0,
+    "Stage 1 sleep|1": 1,
+    "Stage 2 sleep|2": 2,
+    "Stage 3 sleep|3": 3,
+    "Stage 4 sleep|4": 4,
+    "REM sleep|5": 5,
+    "Unscored|9": 9,
+    "Movement|6": 6
+}
+
+SA_EVENTS = ['Central apnea', 'Hypopnea', 'Obstructive apnea']
+
+def parse_sleep_annotations(annotation_path):
+    """解析睡眠分期注释"""
+    try:
+        tree = ET.parse(annotation_path)
+        root = tree.getroot()
+        events = []
+        for scored_event in root.findall('.//ScoredEvent'):
+            event_type = scored_event.find('EventType').text
+            if event_type != "Stages|Stages":
+                continue
+            description = scored_event.find('EventConcept').text
+            start = float(scored_event.find('Start').text)
+            duration = float(scored_event.find('Duration').text)
+            if description not in ANNOTATION_MAP:
+                continue
+            events.append({
+                'onset': start,
+                'duration': duration,
+                'description': description,
+                'stage': ANNOTATION_MAP[description]
+            })
+        return events
+    except Exception as e:
+        return None
+
+
+def extract_osa_events(annotation_path):
+    """提取睡眠呼吸暂停事件"""
+    try:
+        tree = ET.parse(annotation_path)
+        root = tree.getroot()
+        events = []
+        for scored_event in root.findall('.//ScoredEvent'):
+            event_concept = scored_event.find('EventConcept').text
+            event_type = event_concept.split('|')[0].strip()
+            if event_type in SA_EVENTS:
+                start = float(scored_event.find('Start').text)
+                duration = float(scored_event.find('Duration').text)
+                if duration >= 10:
+                    events.append({
+                        'start': start,
+                        'duration': duration,
+                        'end': start + duration,
+                        'type': event_type
+                    })
+        return events
+    except Exception as e:
+        return []
--- a/signal_method/signal_process.py
+++ b/signal_method/signal_process.py
@ -0,0 +1,107 @@
+import numpy as np
+from scipy.interpolate import interp1d
+
+import utils
+
+def signal_filter_split(conf, signal_data_raw, signal_fs, verbose=True):
+    # 滤波
+    # 50Hz陷波滤波器
+    # signal_data = utils.butterworth(data=signal_data, _type="bandpass", low_cut=0.5, high_cut=45, order=10, sample_rate=signal_fs)
+    if verbose:
+        print("Applying 50Hz notch filter...")
+    signal_data = utils.notch_filter(data=signal_data_raw, notch_freq=50.0, quality_factor=30.0, sample_rate=signal_fs)
+
+    resp_data_0 = utils.butterworth(data=signal_data, _type="lowpass", low_cut=50, order=10, sample_rate=signal_fs)
+    resp_fs = conf["resp"]["downsample_fs_1"]
+    resp_data_1 = utils.downsample_signal_fast(original_signal=resp_data_0, original_fs=signal_fs, target_fs=resp_fs)
+    resp_data_1 = utils.average_filter(raw_data=resp_data_1, sample_rate=resp_fs, window_size_sec=20)
+    resp_data_2 = utils.butterworth(data=resp_data_1, _type=conf["resp_filter"]["filter_type"],
+                                    low_cut=conf["resp_filter"]["low_cut"],
+                                    high_cut=conf["resp_filter"]["high_cut"], order=conf["resp_filter"]["order"],
+                                    sample_rate=resp_fs)
+    if verbose:
+        print("Begin plotting signal data...")
+
+    # fig = plt.figure(figsize=(12, 8))
+    # # 绘制三个图raw_data、resp_data_1、resp_data_2
+    # ax0 = fig.add_subplot(3, 1, 1)
+    # ax0.plot(np.linspace(0, len(signal_data) // signal_fs, len(signal_data)), signal_data, color='blue')
+    # ax0.set_title('Raw Signal Data')
+    # ax1 = fig.add_subplot(3, 1, 2, sharex=ax0)
+    # ax1.plot(np.linspace(0, len(resp_data_1) // resp_fs, len(resp_data_1)), resp_data_1, color='orange')
+    # ax1.set_title('Resp Data after Average Filtering')
+    # ax2 = fig.add_subplot(3, 1, 3, sharex=ax0)
+    # ax2.plot(np.linspace(0, len(resp_data_1) // resp_fs, len(resp_data_2)), resp_data_2, color='green')
+    # ax2.set_title('Resp Data after Butterworth Filtering')
+    # plt.tight_layout()
+    # plt.show()
+
+    bcg_data = utils.butterworth(data=signal_data, _type=conf["bcg_filter"]["filter_type"],
+                                 low_cut=conf["bcg_filter"]["low_cut"],
+                                 high_cut=conf["bcg_filter"]["high_cut"], order=conf["bcg_filter"]["order"],
+                                 sample_rate=signal_fs)
+
+
+    return signal_data, resp_data_2, resp_fs, bcg_data, signal_fs
+
+
+def psg_effort_filter(conf, effort_data_raw, effort_fs):
+    # 滤波
+    effort_data_1 = utils.bessel(data=effort_data_raw, _type=conf["effort_filter"]["filter_type"],
+                                 low_cut=conf["effort_filter"]["low_cut"],
+                                 high_cut=conf["effort_filter"]["high_cut"], order=conf["effort_filter"]["order"],
+                                 sample_rate=effort_fs)
+    # 移动平均
+    effort_data_2 = utils.average_filter(raw_data=effort_data_1, sample_rate=effort_fs, window_size_sec=conf["average_filter"]["window_size_sec"])
+    return effort_data_raw, effort_data_2, effort_fs
+
+
+def rpeak2hr(rpeak_indices, signal_length, ecg_fs):
+    hr_signal = np.zeros(signal_length)
+    for i in range(1, len(rpeak_indices)):
+        rri = rpeak_indices[i] - rpeak_indices[i - 1]
+        if rri == 0:
+            continue
+        hr = 60 * ecg_fs / rri  # 心率，单位：bpm
+        if hr > 120:
+            hr = 120
+        elif hr < 30:
+            hr = 30
+        hr_signal[rpeak_indices[i - 1]:rpeak_indices[i]] = hr
+    # 填充最后一个R峰之后的心率值
+    if len(rpeak_indices) > 1:
+        hr_signal[rpeak_indices[-1]:] = hr_signal[rpeak_indices[-2]]
+    return hr_signal
+
+def rpeak2rri_repeat(rpeak_indices, signal_length, ecg_fs):
+    rri_signal = np.zeros(signal_length)
+    for i in range(1, len(rpeak_indices)):
+        rri = rpeak_indices[i] - rpeak_indices[i - 1]
+        rri_signal[rpeak_indices[i - 1]:rpeak_indices[i]] = rri
+    # 填充最后一个R峰之后的RRI值
+    if len(rpeak_indices) > 1:
+        rri_signal[rpeak_indices[-1]:] = rri_signal[rpeak_indices[-2]]
+
+    # 遍历异常值
+    for i in range(1, len(rpeak_indices)):
+        rri = rpeak_indices[i] - rpeak_indices[i - 1]
+        if rri < 0.3 * ecg_fs or rri > 2 * ecg_fs:
+            rri_signal[rpeak_indices[i - 1]:rpeak_indices[i]] = 0
+
+    return rri_signal
+
+def rpeak2rri_interpolation(rpeak_indices, ecg_fs, rri_fs):
+    r_peak_time = np.asarray(rpeak_indices) / ecg_fs
+    rri = np.diff(r_peak_time)
+    t_rri = r_peak_time[1:]
+
+    mask = (rri > 0.3) & (rri < 2.0)
+    rri_clean = rri[mask]
+    t_rri_clean = t_rri[mask]
+
+    t_uniform = np.arange(t_rri_clean[0], t_rri_clean[-1], 1/rri_fs)
+    f = interp1d(t_rri_clean, rri_clean, kind='linear', fill_value="extrapolate")
+    rri_uniform = f(t_uniform)
+
+    return rri_uniform, rri_fs
+
--- a/signal_method/time_metrics.py
+++ b/signal_method/time_metrics.py
@ -0,0 +1,45 @@
+from utils.operation_tools import calculate_by_slide_windows
+from utils.operation_tools import timing_decorator
+import numpy as np
+
+
+@timing_decorator()
+def calc_mav_by_slide_windows(signal_data, movement_mask, low_amp_mask, sampling_rate=100, window_second=10, step_second=1, inner_window_second=2):
+    if movement_mask is not None:
+        assert len(movement_mask) * sampling_rate == len(signal_data), f"movement_mask 长度与 signal_data 长度不一致, {len(movement_mask) * sampling_rate} != {len(signal_data)}"
+        # assert len(movement_mask) == len(low_amp_mask), f"movement_mask 和 low_amp_mask 长度不一致, {len(movement_mask)} != {len(low_amp_mask)}"
+        # print(f"movement_mask_length: {len(movement_mask)}, signal_data_length: {len(signal_data)}")
+        processed_mask = movement_mask.copy()
+    else:
+        processed_mask = None
+
+    def mav_func(x):
+        return np.mean(np.nanmax(x.reshape(-1, inner_window_second*sampling_rate), axis=1) - np.nanmin(x.reshape(-1, inner_window_second*sampling_rate), axis=1)) / 2
+    mav_nan, mav = calculate_by_slide_windows(mav_func, signal_data, processed_mask, sampling_rate=sampling_rate,
+                                              window_second=window_second, step_second=step_second)
+
+    return mav_nan, mav
+
+@timing_decorator()
+def calc_wavefactor_by_slide_windows(signal_data, movement_mask, low_amp_mask, sampling_rate=100):
+    assert len(movement_mask) * sampling_rate == len(signal_data), f"movement_mask 长度与 signal_data 长度不一致, {len(movement_mask) * sampling_rate} != {len(signal_data)}"
+    assert len(movement_mask) == len(low_amp_mask), f"movement_mask 和 low_amp_mask 长度不一致, {len(movement_mask)} != {len(low_amp_mask)}"
+
+    processed_mask = movement_mask.copy()
+    processed_mask = processed_mask.repeat(sampling_rate)
+    wavefactor_nan, wavefactor = calculate_by_slide_windows(lambda x: np.sqrt(np.mean(x ** 2)) / np.mean(np.abs(x)),
+                               signal_data, processed_mask, sampling_rate=sampling_rate, window_second=2, step_second=1)
+
+    return wavefactor_nan, wavefactor
+
+@timing_decorator()
+def calc_peakfactor_by_slide_windows(signal_data, movement_mask, low_amp_mask, sampling_rate=100):
+    assert len(movement_mask) * sampling_rate == len(signal_data), f"movement_mask 长度与 signal_data 长度不一致, {len(movement_mask) * sampling_rate} != {len(signal_data)}"
+    assert len(movement_mask) == len(low_amp_mask), f"movement_mask 和 low_amp_mask 长度不一致, {len(movement_mask)} != {len(low_amp_mask)}"
+
+    processed_mask = movement_mask.copy()
+    processed_mask = processed_mask.repeat(sampling_rate)
+    peakfactor_nan, peakfactor = calculate_by_slide_windows(lambda x: np.max(np.abs(x)) / np.sqrt(np.mean(x ** 2)),
+                               signal_data, processed_mask, sampling_rate=sampling_rate, window_second=2, step_second=1)
+
+    return peakfactor_nan, peakfactor
--- a/utils/HYS_FileReader.py
+++ b/utils/HYS_FileReader.py
@ -0,0 +1,354 @@
+from pathlib import Path
+from typing import Union
+
+import utils
+from .event_map import N2Chn
+import numpy as np
+import pandas as pd
+from .operation_tools import event_mask_2_list
+# 尝试导入 Polars
+try:
+    import polars as pl
+
+    HAS_POLARS = True
+except ImportError:
+    HAS_POLARS = False
+
+
+def read_signal_txt(path: Union[str, Path], dtype, verbose=True, is_peak=False):
+    """
+    Read a txt file and return the first column as a numpy array.
+
+    Args:
+        :param is_peak:
+        :param path:
+        :param verbose:
+        :param dtype:
+    Returns:
+        np.ndarray: The first column of the txt file as a numpy array.
+
+    """
+    path = Path(path)
+    if not path.exists():
+        raise FileNotFoundError(f"File not found: {path}")
+
+    if HAS_POLARS:
+        df = pl.read_csv(path, has_header=False, infer_schema_length=0)
+        signal_data_raw =  df[:, 0].to_numpy().astype(dtype)
+    else:
+        df = pd.read_csv(path, header=None, dtype=dtype)
+        signal_data_raw =  df.iloc[:, 0].to_numpy()
+
+    signal_original_length = len(signal_data_raw)
+    signal_fs = int(path.stem.split("_")[-1])
+    if is_peak:
+        signal_second = None
+        signal_length = None
+    else:
+        signal_second = signal_original_length // signal_fs
+        # 根据采样率进行截断
+        signal_data_raw = signal_data_raw[:signal_second * signal_fs]
+        signal_length = len(signal_data_raw)
+
+    if verbose:
+        print(f"Signal file read from {path}")
+        print(f"signal_fs: {signal_fs}")
+        print(f"signal_original_length: {signal_original_length}")
+        print(f"signal_after_cut_off_length: {signal_length}")
+        print(f"signal_second: {signal_second}")
+
+    return signal_data_raw, signal_length, signal_fs, signal_second
+
+
+def read_label_csv(path: Union[str, Path], verbose=True) -> pd.DataFrame:
+    """
+    Read a CSV file and return it as a pandas DataFrame.
+
+    Args:
+        path (str | Path): Path to the CSV file.
+        verbose (bool):
+    Returns:
+        pd.DataFrame: The content of the CSV file as a pandas DataFrame.
+        :param path:
+        :param verbose:
+    """
+    path = Path(path)
+    if not path.exists():
+        raise FileNotFoundError(f"File not found: {path}")
+
+    # 直接用pandas读取 包含中文 故指定编码
+    df = pd.read_csv(path, encoding="gbk")
+    if verbose:
+        print(f"Label file read from {path}, number of rows: {len(df)}")
+
+    # 统计打标情况
+    # isLabeled=1 表示已打标
+    # Event type 有值的为PSG导出的事件
+    # Event type 为nan的为手动打标的事件
+    # score=1 显著事件， score=2 为受干扰事件 score=3 为非显著应删除事件
+    # 确认后的事件在correct_EventsType
+    # 输出事件信息 按照总计事件、低通气、中枢性、阻塞性、混合型按行输出 格式为 总计/来自PSG/手动/删除/未标注
+    # Columns:
+    # Index	Event type	Stage	Time	Epoch	Date	Duration	HR bef.	HR extr.	HR delta	O2 bef.	O2 min.	O2 delta	Body Position	Validation	Start	End	score	remark	correct_Start	correct_End	correct_EventsType	isLabeled
+    # Event type:
+    # Hypopnea
+    # Central apnea
+    # Obstructive apnea
+    # Mixed apnea
+
+    num_total = np.sum((df["isLabeled"] == 1) & (df["score"] != 3))
+
+    num_psg_events = np.sum(df["Event type"].notna())
+    num_manual_events = np.sum(df["Event type"].isna())
+
+    num_deleted = np.sum(df["score"] == 3)
+
+    # 统计事件
+    num_unlabeled = np.sum(df["isLabeled"] == -1)
+
+    num_psg_hyp = np.sum(df["Event type"] == "Hypopnea")
+    num_psg_csa = np.sum(df["Event type"] == "Central apnea")
+    num_psg_osa = np.sum(df["Event type"] == "Obstructive apnea")
+    num_psg_msa = np.sum(df["Event type"] == "Mixed apnea")
+
+    num_hyp = np.sum((df["correct_EventsType"] == "Hypopnea") & (df["score"] != 3))
+    num_csa = np.sum((df["correct_EventsType"] == "Central apnea") & (df["score"] != 3))
+    num_osa = np.sum((df["correct_EventsType"] == "Obstructive apnea") & (df["score"] != 3))
+    num_msa = np.sum((df["correct_EventsType"] == "Mixed apnea") & (df["score"] != 3))
+
+    num_manual_hyp = np.sum((df["Event type"].isna()) & (df["correct_EventsType"] == "Hypopnea"))
+    num_manual_csa = np.sum((df["Event type"].isna()) & (df["correct_EventsType"] == "Central apnea"))
+    num_manual_osa = np.sum((df["Event type"].isna()) & (df["correct_EventsType"] == "Obstructive apnea"))
+    num_manual_msa = np.sum((df["Event type"].isna()) & (df["correct_EventsType"] == "Mixed apnea"))
+
+    num_deleted_hyp = np.sum((df["score"] == 3) & (df["correct_EventsType"] == "Hypopnea"))
+    num_deleted_csa = np.sum((df["score"] == 3) & (df["correct_EventsType"] == "Central apnea"))
+    num_deleted_osa = np.sum((df["score"] == 3) & (df["correct_EventsType"] == "Obstructive apnea"))
+    num_deleted_msa = np.sum((df["score"] == 3) & (df["correct_EventsType"] == "Mixed apnea"))
+
+    num_unlabeled_hyp = np.sum((df["isLabeled"] == -1) & (df["Event type"] == "Hypopnea"))
+    num_unlabeled_csa = np.sum((df["isLabeled"] == -1) & (df["Event type"] == "Central apnea"))
+    num_unlabeled_osa = np.sum((df["isLabeled"] == -1) & (df["Event type"] == "Obstructive apnea"))
+    num_unlabeled_msa = np.sum((df["isLabeled"] == -1) & (df["Event type"] == "Mixed apnea"))
+
+    num_hyp_1_score = np.sum((df["correct_EventsType"] == "Hypopnea") & (df["score"] == 1))
+    num_csa_1_score = np.sum((df["correct_EventsType"] == "Central apnea") & (df["score"] == 1))
+    num_osa_1_score = np.sum((df["correct_EventsType"] == "Obstructive apnea") & (df["score"] == 1))
+    num_msa_1_score = np.sum((df["correct_EventsType"] == "Mixed apnea") & (df["score"] == 1))
+
+    num_hyp_2_score = np.sum((df["correct_EventsType"] == "Hypopnea") & (df["score"] == 2))
+    num_csa_2_score = np.sum((df["correct_EventsType"] == "Central apnea") & (df["score"] == 2))
+    num_osa_2_score = np.sum((df["correct_EventsType"] == "Obstructive apnea") & (df["score"] == 2))
+    num_msa_2_score = np.sum((df["correct_EventsType"] == "Mixed apnea") & (df["score"] == 2))
+
+    num_hyp_3_score = np.sum((df["correct_EventsType"] == "Hypopnea") & (df["score"] == 3))
+    num_csa_3_score = np.sum((df["correct_EventsType"] == "Central apnea") & (df["score"] == 3))
+    num_osa_3_score = np.sum((df["correct_EventsType"] == "Obstructive apnea") & (df["score"] == 3))
+    num_msa_3_score = np.sum((df["correct_EventsType"] == "Mixed apnea") & (df["score"] == 3))
+
+    num_1_score = np.sum(df["score"] == 1)
+    num_2_score = np.sum(df["score"] == 2)
+    num_3_score = np.sum(df["score"] == 3)
+
+    if verbose:
+        print("Event Statistics:")
+        # 格式化输出 总计/来自PSG/手动/删除/未标注 指定宽度
+        print(f"Type {'Total':^8s} / {'From PSG':^8s} / {'Manual':^8s} / {'Deleted':^8s} / {'Unlabeled':^8s}")
+        print(
+            f"Hyp: {num_hyp:^8d} / {num_psg_hyp:^8d} / {num_manual_hyp:^8d} / {num_deleted_hyp:^8d} / {num_unlabeled_hyp:^8d}")
+        print(
+            f"CSA: {num_csa:^8d} / {num_psg_csa:^8d} / {num_manual_csa:^8d} / {num_deleted_csa:^8d} / {num_unlabeled_csa:^8d}")
+        print(
+            f"OSA: {num_osa:^8d} / {num_psg_osa:^8d} / {num_manual_osa:^8d} / {num_deleted_osa:^8d} / {num_unlabeled_osa:^8d}")
+        print(
+            f"MSA: {num_msa:^8d} / {num_psg_msa:^8d} / {num_manual_msa:^8d} / {num_deleted_msa:^8d} / {num_unlabeled_msa:^8d}")
+        print(
+            f"All: {num_total:^8d} / {num_psg_events:^8d} / {num_manual_events:^8d} / {num_deleted:^8d} / {num_unlabeled:^8d}")
+
+        print("Score Statistics (only for non-deleted events and manual created events):")
+        print(f"Type {'Total':^8s} / {'Score 1':^8s} / {'Score 2':^8s} / {'Score 3':^8s}")
+        print(f"Hyp: {num_hyp:^8d} / {num_hyp_1_score:^8d} / {num_hyp_2_score:^8d} / {num_hyp_3_score:^8d}")
+        print(f"CSA: {num_csa:^8d} / {num_csa_1_score:^8d} / {num_csa_2_score:^8d} / {num_csa_3_score:^8d}")
+        print(f"OSA: {num_osa:^8d} / {num_osa_1_score:^8d} / {num_osa_2_score:^8d} / {num_osa_3_score:^8d}")
+        print(f"MSA: {num_msa:^8d} / {num_msa_1_score:^8d} / {num_msa_2_score:^8d} / {num_msa_3_score:^8d}")
+        print(f"All: {num_total:^8d} / {num_1_score:^8d} / {num_2_score:^8d} / {num_3_score:^8d}")
+
+    df["Start"] = df["Start"].astype(int)
+    df["End"] = df["End"].astype(int)
+    return df
+
+
+def read_raw_psg_label(path: Union[str, Path], verbose=True) -> pd.DataFrame:
+    """
+    Read a CSV file and return it as a pandas DataFrame.
+
+    Args:
+        path (str | Path): Path to the CSV file.
+        verbose (bool):
+    Returns:
+        pd.DataFrame: The content of the CSV file as a pandas DataFrame.
+        :param path:
+        :param verbose:
+    """
+    path = Path(path)
+    if not path.exists():
+        raise FileNotFoundError(f"File not found: {path}")
+
+    # 直接用pandas读取 包含中文 故指定编码
+    df = pd.read_csv(path, encoding="gbk")
+    if verbose:
+        print(f"Label file read from {path}, number of rows: {len(df)}")
+
+    num_psg_events = np.sum(df["Event type"].notna())
+    # 统计事件
+    num_psg_hyp = np.sum(df["Event type"] == "Hypopnea")
+    num_psg_csa = np.sum(df["Event type"] == "Central apnea")
+    num_psg_osa = np.sum(df["Event type"] == "Obstructive apnea")
+    num_psg_msa = np.sum(df["Event type"] == "Mixed apnea")
+
+
+
+    if verbose:
+        print("Event Statistics:")
+        # 格式化输出 总计/来自PSG/手动/删除/未标注 指定宽度
+        print(f"Type {'Total':^8s}")
+        print(
+            f"Hyp:   {num_psg_hyp:^8d} ")
+        print(
+            f"CSA:   {num_psg_csa:^8d} ")
+        print(
+            f"OSA:   {num_psg_osa:^8d} ")
+        print(
+            f"MSA:   {num_psg_msa:^8d} ")
+        print(
+            f"All:   {num_psg_events:^8d}")
+
+    df["Start"] = df["Start"].astype(int)
+    df["End"] = df["End"].astype(int)
+    return df
+
+def read_disable_excel(path: Union[str, Path]) -> pd.DataFrame:
+    """
+    Read an Excel file and return it as a pandas DataFrame.
+
+    Args:
+        path (str | Path): Path to the Excel file.
+    Returns:
+        pd.DataFrame: The content of the Excel file as a pandas DataFrame.
+    """
+    path = Path(path)
+    if not path.exists():
+        raise FileNotFoundError(f"File not found: {path}")
+
+    # 直接用pandas读取
+    df = pd.read_excel(path)
+    df["id"] = df["id"].astype(int)
+    df["start"] = df["start"].astype(int)
+    df["end"] = df["end"].astype(int)
+    return df
+
+
+def read_mask_execl(path: Union[str, Path]):
+    """
+    Read an Excel file and return the mask as a numpy array.
+    Args:
+        path (str | Path): Path to the Excel file.
+    Returns:
+        np.ndarray: The mask as a numpy array.
+    """
+    path = Path(path)
+    if not path.exists():
+        raise FileNotFoundError(f"File not found: {path}")
+
+    df = pd.read_csv(path)
+    event_mask = df.to_dict(orient="list")
+    for key in event_mask:
+        event_mask[key] = np.array(event_mask[key])
+
+    event_list = {"RespAmpChangeSegment": event_mask_2_list(1 - event_mask["Resp_AmpChange_Label"]),
+                  "BCGAmpChangeSegment": event_mask_2_list(1 - event_mask["BCG_AmpChange_Label"]),
+                  "EnableSegment": event_mask_2_list(1 - event_mask["Disable_Label"]),
+                  "DisableSegment": event_mask_2_list(event_mask["Disable_Label"])}
+
+
+    return event_mask, event_list
+
+
+def read_psg_mask_excel(path: Union[str, Path]):
+
+    df = pd.read_csv(path)
+    event_mask = df.to_dict(orient="list")
+    for key in event_mask:
+        event_mask[key] = np.array(event_mask[key])
+
+    return event_mask
+
+
+def read_psg_channel(path_str: Union[str, Path], channel_number: list[int], verbose=True):
+    """
+    读取PSG文件中特定通道的数据。
+
+    参数:
+        path_str (Union[str, Path]): 存放PSG文件的文件夹路径。
+        channel_name (str): 需要读取的通道名称。
+    返回:
+        np.ndarray: 指定通道的数据数组。
+    """
+    path = Path(path_str)
+    if not path.exists():
+        raise FileNotFoundError(f"PSG Dir not found: {path}")
+
+    if not path.is_dir():
+        raise NotADirectoryError(f"PSG Dir not found: {path}")
+    channel_data = {}
+    # 遍历检查通道对应的文件是否存在
+    for ch_id in channel_number:
+        ch_name = N2Chn[ch_id]
+        ch_path = list(path.glob(f"{ch_name}*.txt"))
+
+        if not any(ch_path):
+            raise FileNotFoundError(f"PSG Channel file not found: {ch_path}")
+
+        if len(ch_path) > 1:
+            print(f"Warning!!! PSG Channel file more than one: {ch_path}")
+
+        if ch_id == 8:
+            # sleep stage 特例 读取为整数
+            ch_signal, ch_length, ch_fs, ch_second = read_signal_txt(ch_path[0], dtype=str, verbose=verbose)
+            # 转换为整数数组
+            for stage_str, stage_number in utils.Stage2N.items():
+                np.place(ch_signal, ch_signal == stage_str, stage_number)
+            ch_signal = ch_signal.astype(int)
+        elif ch_id == 1:
+            # Rpeak 特例 读取为整数
+            ch_signal, ch_length, ch_fs, ch_second = read_signal_txt(ch_path[0], dtype=int, verbose=verbose, is_peak=True)
+        else:
+            ch_signal, ch_length, ch_fs, ch_second = read_signal_txt(ch_path[0], dtype=float, verbose=verbose)
+        channel_data[ch_name] = {
+            "name": ch_name,
+            "path": ch_path[0],
+            "data": ch_signal,
+            "length": ch_length,
+            "fs": ch_fs,
+            "second": ch_second
+        }
+
+    return channel_data
+
+
+def read_psg_label(path: Union[str, Path],  verbose=True):
+    path = Path(path)
+    if not path.exists():
+        raise FileNotFoundError(f"File not found: {path}")
+
+    # 直接用pandas读取 包含中文 故指定编码
+    df = pd.read_csv(path, encoding="gbk")
+    if verbose:
+        print(f"Label file read from {path}, number of rows: {len(df)}")
+
+    # 丢掉Event type为空的行
+    df = df.dropna(subset=["Event type"], how='all').reset_index(drop=True)
+
+    return df
+
+
--- a/utils/init.py
+++ b/utils/init.py
@ -0,0 +1,12 @@
+from .HYS_FileReader import read_label_csv, read_signal_txt, read_disable_excel, read_psg_label, read_raw_psg_label, read_psg_mask_excel
+from .operation_tools import load_dataset_conf, generate_disable_mask, generate_event_mask, event_mask_2_list
+from .operation_tools import merge_short_gaps, remove_short_durations
+from .operation_tools import collect_values
+from .operation_tools import save_process_label
+from .operation_tools import none_to_nan_mask
+from .operation_tools import get_wake_mask
+from .operation_tools import fill_spo2_anomaly
+from .split_method import resp_split
+from .HYS_FileReader import read_mask_execl, read_psg_channel
+from .event_map import E2N, N2Chn, Stage2N, ColorCycle
+from .filter_func import butterworth, average_filter, downsample_signal_fast, notch_filter, bessel, adjust_sample_rate
--- a/utils/event_map.py
+++ b/utils/event_map.py
@ -0,0 +1,42 @@
+# apnea event type to number mapping
+E2N = {
+    "Hypopnea": 1,
+    "Central apnea": 2,
+    "Obstructive apnea": 3,
+    "Mixed apnea": 4
+}
+
+N2Chn = {
+    1: "Rpeak",
+    2: "ECG_Sync",
+    3: "Effort Tho",
+    4: "Effort Abd",
+    5: "Flow P",
+    6: "Flow T",
+    7: "SpO2",
+    8: "5_class"
+}
+
+Stage2N = {
+    "W": 5,
+    "N1": 3,
+    "N2": 2,
+    "N3": 1,
+    "R": 4,
+}
+
+# 设定事件和其对应颜色
+# event_code  color event
+#      0      黑色    背景
+#      1      粉色    低通气
+#      2      蓝色    中枢性
+#      3      红色    阻塞型
+#      4      灰色    混合型
+#      5      绿色    血氧饱和度下降
+#      6      橙色    大体动
+#      7      橙色    小体动
+#      8      橙色    深呼吸
+#      9      橙色    脉冲体动
+#      10     橙色    无效片段
+ColorCycle = ["black", "pink", "blue", "red", "silver", "green", "orange", "orange", "orange", "orange",
+                    "orange"]
--- a/utils/filter_func.py
+++ b/utils/filter_func.py
@ -0,0 +1,155 @@
+from utils.operation_tools import timing_decorator
+import numpy as np
+from scipy import signal, ndimage
+
+
+@timing_decorator()
+def butterworth(data, _type, low_cut=0.0, high_cut=0.0, order=10, sample_rate=1000):
+    if _type == "lowpass":  # 低通滤波处理
+        sos = signal.butter(order, low_cut / (sample_rate * 0.5), btype='lowpass', output='sos')
+        return signal.sosfiltfilt(sos, np.array(data))
+    elif _type == "bandpass":  # 带通滤波处理
+        low = low_cut / (sample_rate * 0.5)
+        high = high_cut / (sample_rate * 0.5)
+        sos = signal.butter(order, [low, high], btype='bandpass', output='sos')
+        return signal.sosfiltfilt(sos, np.array(data))
+    elif _type == "highpass":  # 高通滤波处理
+        sos = signal.butter(order, high_cut / (sample_rate * 0.5), btype='highpass', output='sos')
+        return signal.sosfiltfilt(sos, np.array(data))
+    else:  # 警告,滤波器类型必须有
+        raise ValueError("Please choose a type of fliter")
+
+
+@timing_decorator()
+def bessel(data, _type, low_cut=0.0, high_cut=0.0, order=4, sample_rate=1000):
+    if _type == "lowpass":  # 低通滤波处理
+        b, a = signal.bessel(order, low_cut / (sample_rate * 0.5), btype='lowpass', analog=False, norm='mag')
+        return signal.filtfilt(b, a, np.array(data))
+    elif _type == "bandpass":  # 带通滤波处理
+        low = low_cut / (sample_rate * 0.5)
+        high = high_cut / (sample_rate * 0.5)
+        b, a = signal.bessel(order, [low, high], btype='bandpass', analog=False, norm='mag')
+        return signal.filtfilt(b, a, np.array(data))
+    elif _type == "highpass":  # 高通滤波处理
+        b, a = signal.bessel(order, high_cut / (sample_rate * 0.5), btype='highpass', analog=False, norm='mag')
+        return signal.filtfilt(b, a, np.array(data))
+    else:  # 警告,滤波器类型必须有
+        raise ValueError("Please choose a type of fliter")
+
+
+@timing_decorator()
+def downsample_signal_fast(original_signal, original_fs, target_fs, chunk_size=100000):
+    """
+    高效整数倍降采样长信号，分段处理以优化内存和速度。
+
+    参数:
+    original_signal : array-like, 原始信号数组
+    original_fs : float, 原始采样率 (Hz)
+    target_fs : float, 目标采样率 (Hz)
+    chunk_size : int, 每段处理的样本数，默认100000
+
+    返回:
+    downsampled_signal : array-like, 降采样后的信号
+    """
+    # 输入验证
+    if not isinstance(original_signal, np.ndarray):
+        original_signal = np.array(original_signal)
+    if original_fs <= target_fs:
+        raise ValueError("目标采样率必须小于原始采样率")
+    if target_fs <= 0 or original_fs <= 0:
+        raise ValueError("采样率必须为正数")
+
+    # 计算降采样因子（必须为整数）
+    downsample_factor = original_fs / target_fs
+    if not downsample_factor.is_integer():
+        raise ValueError("降采样因子必须为整数倍")
+    downsample_factor = int(downsample_factor)
+
+    # 计算总输出长度
+    total_length = len(original_signal)
+    output_length = total_length // downsample_factor
+
+    # 初始化输出数组
+    downsampled_signal = np.zeros(output_length)
+
+    # 分段处理
+    for start in range(0, total_length, chunk_size):
+        end = min(start + chunk_size, total_length)
+        chunk = original_signal[start:end]
+
+        # 使用decimate进行整数倍降采样
+        chunk_downsampled = signal.decimate(chunk, downsample_factor, ftype='iir', zero_phase=True)
+
+        # 计算输出位置
+        out_start = start // downsample_factor
+        out_end = out_start + len(chunk_downsampled)
+        if out_end > output_length:
+            chunk_downsampled = chunk_downsampled[:output_length - out_start]
+
+        downsampled_signal[out_start:out_end] = chunk_downsampled
+
+    return downsampled_signal
+
+def upsample_signal(original_signal, original_fs, target_fs):
+    """
+    信号升采样
+
+    参数:
+    original_signal : array-like, 原始信号数组
+    original_fs : float, 原始采样率 (Hz)
+    target_fs : float, 目标采样率 (Hz)
+
+    返回:
+    upsampled_signal : array-like, 升采样后的信号
+    """
+    if not isinstance(original_signal, np.ndarray):
+        original_signal = np.array(original_signal)
+    if target_fs <= original_fs:
+        raise ValueError("目标采样率必须大于原始采样率")
+    if target_fs <= 0 or original_fs <= 0:
+        raise ValueError("采样率必须为正数")
+
+    upsample_factor = target_fs / original_fs
+    num_output_samples = int(len(original_signal) * upsample_factor)
+
+    upsampled_signal = signal.resample(original_signal, num_output_samples)
+
+    return upsampled_signal
+
+
+def adjust_sample_rate(signal_data, original_fs, target_fs):
+    """
+    根据信号的原始采样率和目标采样率，自动选择升采样或降采样。
+
+    参数:
+    signal_data : array-like, 原始信号数组
+    original_fs : float, 原始采样率 (Hz)
+    target_fs : float, 目标采样率 (Hz)
+
+    返回:
+    adjusted_signal : array-like, 调整采样率后的信号
+    """
+    if original_fs == target_fs:
+        return signal_data
+    elif original_fs > target_fs:
+        return downsample_signal_fast(signal_data, original_fs, target_fs)
+    else:
+        return upsample_signal(signal_data, original_fs, target_fs)
+
+
+@timing_decorator()
+def average_filter(raw_data, sample_rate, window_size_sec=20):
+    kernel = np.ones(window_size_sec * sample_rate) / (window_size_sec * sample_rate)
+    filtered = ndimage.convolve1d(raw_data, kernel, mode='reflect')
+    convolve_filter_signal = raw_data - filtered
+    return convolve_filter_signal
+
+
+# 陷波滤波器
+@timing_decorator()
+def notch_filter(data, notch_freq=50.0, quality_factor=30.0, sample_rate=1000):
+    nyquist = 0.5 * sample_rate
+    norm_notch_freq = notch_freq / nyquist
+    b, a = signal.iirnotch(norm_notch_freq, quality_factor)
+    filtered_data = signal.filtfilt(b, a, data)
+    return filtered_data
--- a/utils/operation_tools.py
+++ b/utils/operation_tools.py
@ -0,0 +1,380 @@
+import time
+
+from pathlib import Path
+import numpy as np
+import pandas as pd
+from matplotlib import pyplot as plt
+import yaml
+from numpy.ma.core import append
+from scipy.interpolate import PchipInterpolator
+
+from utils.event_map import E2N
+plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文标签
+plt.rcParams['axes.unicode_minus'] = False  # 用来正常显示负号
+
+from scipy import ndimage, signal
+from functools import wraps
+
+# 全局配置
+class Config:
+    time_verbose = False
+
+def timing_decorator():
+    def decorator(func):
+        @wraps(func)
+        def wrapper(*args, **kwargs):
+            start_time = time.time()
+            result = func(*args, **kwargs)
+            end_time = time.time()
+            elapsed_time = end_time - start_time
+            if Config.time_verbose:  # 运行时检查全局配置
+                print(f"函数 '{func.__name__}' 执行耗时: {elapsed_time:.4f} 秒")
+            return result
+        return wrapper
+    return decorator
+
+@timing_decorator()
+def read_auto(file_path):
+    # print('suffix: ', file_path.suffix)
+    if file_path.suffix == '.txt':
+        # 使用pandas read csv读取txt
+        return pd.read_csv(file_path, header=None).to_numpy().reshape(-1)
+    elif file_path.suffix == '.npy':
+        return np.load(file_path.__str__())
+    elif file_path.suffix == '.base64':
+        with open(file_path) as f:
+            files = f.readlines()
+
+        data = np.array(files, dtype=int)
+        return data
+    else:
+        raise ValueError('这个文件类型不支持，需要自己写读取程序')
+
+
+def merge_short_gaps(state_sequence, time_points, max_gap_sec):
+    """
+    合并状态序列中间隔小于指定时长的段
+
+    参数：
+    - state_sequence: numpy array，状态序列（0/1）
+    - time_points: numpy array，每个状态点对应的时间点
+    - max_gap_sec: float，要合并的最大间隔（秒）
+
+    返回：
+    - merged_sequence: numpy array，合并后的状态序列
+    """
+    if len(state_sequence) <= 1:
+        return state_sequence
+
+    merged_sequence = state_sequence.copy()
+
+    # 找出状态转换点
+    transitions = np.diff(np.concatenate([[0], merged_sequence, [0]]))
+    # 找出状态1的起始和结束位置
+    state_starts = np.where(transitions == 1)[0]
+    state_ends = np.where(transitions == -1)[0] - 1
+
+    # 检查每对连续的状态1
+    for i in range(len(state_starts) - 1):
+        if state_ends[i] < len(time_points) and state_starts[i + 1] < len(time_points):
+            # 计算间隔时长
+            gap_duration = time_points[state_starts[i + 1]] - time_points[state_ends[i]]
+            # 如果间隔小于阈值，则合并
+            if gap_duration <= max_gap_sec:
+                merged_sequence[state_ends[i]:state_starts[i + 1]] = 1
+
+    return merged_sequence
+
+
+def remove_short_durations(state_sequence, time_points, min_duration_sec):
+    """
+    移除状态序列中持续时间短于指定阈值的段
+
+    参数：
+    - state_sequence: numpy array，状态序列（0/1）
+    - time_points: numpy array，每个状态点对应的时间点
+    - min_duration_sec: float，要保留的最小持续时间（秒）
+
+    返回：
+    - filtered_sequence: numpy array，过滤后的状态序列
+    """
+    if len(state_sequence) <= 1:
+        return state_sequence
+
+    filtered_sequence = state_sequence.copy()
+
+    # 找出状态转换点
+    transitions = np.diff(np.concatenate([[0], filtered_sequence, [0]]))
+    # 找出状态1的起始和结束位置
+    state_starts = np.where(transitions == 1)[0]
+    state_ends = np.where(transitions == -1)[0] - 1
+
+    # 检查每个状态1的持续时间
+    for i in range(len(state_starts)):
+        if state_starts[i] < len(time_points) and state_ends[i] < len(time_points):
+            # 计算持续时间
+            duration = time_points[state_ends[i]] - time_points[state_starts[i]]
+            if state_ends[i] == len(time_points) - 1:
+                # 如果是最后一个窗口，加上一个窗口的长度
+                duration += time_points[1] - time_points[0] if len(time_points) > 1 else 0
+
+            # 如果持续时间短于阈值，则移除
+            if duration < min_duration_sec:
+                filtered_sequence[state_starts[i]:state_ends[i] + 1] = 0
+
+    return filtered_sequence
+
+
+@timing_decorator()
+def calculate_by_slide_windows(func, signal_data, calc_mask, sampling_rate=100, window_second=20, step_second=None):
+    # 处理标志位长度与 signal_data 对齐
+    # if calc_mask is None:
+    #     calc_mask = np.zeros(len(signal_data), dtype=bool)
+
+    if step_second is None:
+        step_second = window_second
+
+    step_length = step_second * sampling_rate
+    window_length = window_second * sampling_rate
+
+    origin_seconds = len(signal_data) // sampling_rate
+    total_samples = len(signal_data)
+
+
+    # reflect padding
+    left_pad_size = int(window_length // 2)
+    right_pad_size = window_length - left_pad_size
+    data = np.pad(signal_data, (left_pad_size, right_pad_size), mode='reflect')
+
+    num_segments = int(np.ceil(len(signal_data) / step_length))
+    values_nan = np.full(num_segments, np.nan)
+
+    # print(f"num_segments: {num_segments}, step_length: {step_length}, window_length: {window_length}")
+    for i in range(num_segments):
+        # 包含体动则仅计算不含体动部分
+        start = int(i * step_length)
+        end = start + window_length
+        segment = data[start:end]
+        values_nan[i] = func(segment)
+
+
+    values_nan = values_nan.repeat(step_second)[:origin_seconds]
+
+    if calc_mask is not None:
+        for i in range(len(values_nan)):
+            if calc_mask[i]:
+                values_nan[i] = np.nan
+
+        values = values_nan.copy()
+
+        # 插值处理体动区域的 NaN 值
+        def interpolate_nans(x, t):
+            valid_mask = ~np.isnan(x)
+            return np.interp(t, t[valid_mask], x[valid_mask])
+
+        values = interpolate_nans(values, np.arange(len(values)))
+    else:
+        values = values_nan.copy()
+
+    return values_nan, values
+
+
+def load_dataset_conf(yaml_path):
+    with open(yaml_path, 'r', encoding='utf-8') as f:
+        config = yaml.safe_load(f)
+
+    # select_ids = config.get('select_id', [])
+    # root_path = config.get('root_path', None)
+    # data_path = Path(root_path)
+    return config
+
+
+def generate_disable_mask(signal_second: int, disable_df) -> np.ndarray:
+    disable_mask = np.zeros(signal_second, dtype=int)
+
+    for _, row in disable_df.iterrows():
+        start = row["start"]
+        end = row["end"]
+        disable_mask[start:end] = 1
+    return disable_mask
+
+
+def generate_event_mask(signal_second: int, event_df, use_correct=True, with_score=True):
+    event_mask = np.zeros(signal_second, dtype=int)
+    if with_score:
+        score_mask = np.zeros(signal_second, dtype=int)
+    else:
+        score_mask = None
+    if use_correct:
+        start_name = "correct_Start"
+        end_name = "correct_End"
+        event_type_name = "correct_EventsType"
+    else:
+        start_name = "Start"
+        end_name = "End"
+        event_type_name = "Event type"
+
+    # 剔除start = -1 的行
+    event_df = event_df[event_df[start_name] >= 0]
+
+    for _, row in event_df.iterrows():
+        start = row[start_name]
+        end = row[end_name] + 1
+        event_mask[start:end] = E2N[row[event_type_name]]
+        if with_score:
+            score_mask[start:end] = row["score"]
+    return event_mask, score_mask
+
+
+def event_mask_2_list(mask, event_true=True):
+    if event_true:
+        event_2_normal = -1
+        normal_2_event = 1
+        _append = 0
+    else:
+        event_2_normal = 1
+        normal_2_event = -1
+        _append = 1
+    mask_start = np.where(np.diff(mask, prepend=_append, append=_append) == normal_2_event)[0]
+    mask_end = np.where(np.diff(mask, prepend=_append, append=_append) == event_2_normal)[0]
+    event_list =[[start, end] for start, end in zip(mask_start, mask_end)]
+    return event_list
+
+
+def collect_values(arr: np.ndarray, index: int, step: int, limit: int, mask=None) -> list:
+    """收集非 NaN 值，直到达到指定数量或边界"""
+    values = []
+    count = 0
+    mask = mask if mask is not None else arr
+    while count < limit and 0 <= index < len(mask):
+        if not np.isnan(mask[index]):
+            values.append(arr[index])
+            count += 1
+        index += step
+    return values
+
+
+def save_process_label(save_path: Path, save_dict: dict):
+    save_df = pd.DataFrame(save_dict)
+    save_df.to_csv(save_path, index=False)
+
+def none_to_nan_mask(mask, ref):
+    """将None转换为与ref形状相同的nan掩码"""
+    if mask is None:
+        return np.full_like(ref, np.nan)
+    else:
+        # 将mask中的0替换为nan，其他的保持
+        mask = np.where(mask == 0, np.nan, mask)
+        return mask
+
+def get_wake_mask(sleep_stage_mask):
+    # 将N1, N2, N3, REM视为睡眠 0，其他为清醒 1
+    # 输入是字符 分别有 'W', 'N1', 'N2', 'N3', 'R' 等
+    wake_mask = np.where(np.isin(sleep_stage_mask, ['N1', 'N2', 'N3', 'REM', 'R']), 0, 1)
+    return wake_mask
+
+def detect_spo2_anomaly(spo2, fs, diff_thresh=7):
+    anomaly = np.zeros(len(spo2), dtype=bool)
+
+    # 生理范围
+    anomaly |= (spo2 < 50) | (spo2 > 100)
+
+    # 突变
+    diff = np.abs(np.diff(spo2, prepend=spo2[0]))
+    anomaly |= diff > diff_thresh
+
+    # NaN
+    anomaly |= np.isnan(spo2)
+
+    return anomaly
+
+def merge_close_anomalies(anomaly, fs, min_gap_duration):
+    min_gap = int(min_gap_duration * fs)
+    merged = anomaly.copy()
+
+    i = 0
+    n = len(anomaly)
+
+    while i < n:
+        if not anomaly[i]:
+            i += 1
+            continue
+
+        # 当前异常段
+        start = i
+        while i < n and anomaly[i]:
+            i += 1
+        end = i
+
+        # 向后看 gap
+        j = end
+        while j < n and not anomaly[j]:
+            j += 1
+
+        if j < n and (j - end) < min_gap:
+            merged[end:j] = True
+
+    return merged
+
+def fill_spo2_anomaly(
+    spo2_data,
+    spo2_fs,
+    max_fill_duration,
+    min_gap_duration,
+):
+    spo2 = spo2_data.astype(float).copy()
+    n = len(spo2)
+
+    anomaly = detect_spo2_anomaly(spo2, spo2_fs)
+    anomaly = merge_close_anomalies(anomaly, spo2_fs, min_gap_duration)
+
+    max_len = int(max_fill_duration * spo2_fs)
+
+    valid_mask = ~anomaly
+
+    i = 0
+    while i < n:
+        if not anomaly[i]:
+            i += 1
+            continue
+
+        start = i
+        while i < n and anomaly[i]:
+            i += 1
+        end = i
+
+        seg_len = end - start
+
+        # 超长异常段
+        if seg_len > max_len:
+            spo2[start:end] = np.nan
+            valid_mask[start:end] = False
+            continue
+
+        has_left = start > 0 and valid_mask[start - 1]
+        has_right = end < n and valid_mask[end]
+
+        # 开头异常：单侧填充
+        if not has_left and has_right:
+            spo2[start:end] = spo2[end]
+            continue
+
+        # 结尾异常：单侧填充
+        if has_left and not has_right:
+            spo2[start:end] = spo2[start - 1]
+            continue
+
+        # 两侧都有 → PCHIP
+        if has_left and has_right:
+            x = np.array([start - 1, end])
+            y = np.array([spo2[start - 1], spo2[end]])
+
+            interp = PchipInterpolator(x, y)
+            spo2[start:end] = interp(np.arange(start, end))
+            continue
+
+        # 两侧都没有（极端情况）
+        spo2[start:end] = np.nan
+        valid_mask[start:end] = False
+
+    return spo2, valid_mask
--- a/utils/signal_process.py
+++ b/utils/signal_process.py
@ -0,0 +1,108 @@
+from utils.operation_tools import timing_decorator
+import numpy as np
+from scipy import signal, ndimage
+
+
+@timing_decorator()
+def butterworth(data, _type, low_cut=0.0, high_cut=0.0, order=10, sample_rate=1000):
+    if _type == "lowpass":  # 低通滤波处理
+        sos = signal.butter(order, low_cut / (sample_rate * 0.5), btype='lowpass', output='sos')
+        return signal.sosfiltfilt(sos, np.array(data))
+    elif _type == "bandpass":  # 带通滤波处理
+        low = low_cut / (sample_rate * 0.5)
+        high = high_cut / (sample_rate * 0.5)
+        sos = signal.butter(order, [low, high], btype='bandpass', output='sos')
+        return signal.sosfiltfilt(sos, np.array(data))
+    elif _type == "highpass":  # 高通滤波处理
+        sos = signal.butter(order, high_cut / (sample_rate * 0.5), btype='highpass', output='sos')
+        return signal.sosfiltfilt(sos, np.array(data))
+    else:  # 警告,滤波器类型必须有
+        raise ValueError("Please choose a type of fliter")
+
+
+def bessel(data, _type, low_cut=0.0, high_cut=0.0, order=4, sample_rate=1000):
+    if _type == "lowpass":  # 低通滤波处理
+        b, a = signal.bessel(order, low_cut / (sample_rate * 0.5), btype='lowpass', analog=False, norm='mag')
+        return signal.filtfilt(b, a, np.array(data))
+    elif _type == "bandpass":  # 带通滤波处理
+        low = low_cut / (sample_rate * 0.5)
+        high = high_cut / (sample_rate * 0.5)
+        b, a = signal.bessel(order, [low, high], btype='bandpass', analog=False, norm='mag')
+        return signal.filtfilt(b, a, np.array(data))
+    elif _type == "highpass":  # 高通滤波处理
+        b, a = signal.bessel(order, high_cut / (sample_rate * 0.5), btype='highpass', analog=False, norm='mag')
+        return signal.filtfilt(b, a, np.array(data))
+    else:  # 警告,滤波器类型必须有
+        raise ValueError("Please choose a type of fliter")
+
+
+@timing_decorator()
+def downsample_signal_fast(original_signal, original_fs, target_fs, chunk_size=100000):
+    """
+    高效整数倍降采样长信号，分段处理以优化内存和速度。
+
+    参数:
+    original_signal : array-like, 原始信号数组
+    original_fs : float, 原始采样率 (Hz)
+    target_fs : float, 目标采样率 (Hz)
+    chunk_size : int, 每段处理的样本数，默认100000
+
+    返回:
+    downsampled_signal : array-like, 降采样后的信号
+    """
+    # 输入验证
+    if not isinstance(original_signal, np.ndarray):
+        original_signal = np.array(original_signal)
+    if original_fs <= target_fs:
+        raise ValueError("目标采样率必须小于原始采样率")
+    if target_fs <= 0 or original_fs <= 0:
+        raise ValueError("采样率必须为正数")
+
+    # 计算降采样因子（必须为整数）
+    downsample_factor = original_fs / target_fs
+    if not downsample_factor.is_integer():
+        raise ValueError("降采样因子必须为整数倍")
+    downsample_factor = int(downsample_factor)
+
+    # 计算总输出长度
+    total_length = len(original_signal)
+    output_length = total_length // downsample_factor
+
+    # 初始化输出数组
+    downsampled_signal = np.zeros(output_length)
+
+    # 分段处理
+    for start in range(0, total_length, chunk_size):
+        end = min(start + chunk_size, total_length)
+        chunk = original_signal[start:end]
+
+        # 使用decimate进行整数倍降采样
+        chunk_downsampled = signal.decimate(chunk, downsample_factor, ftype='iir', zero_phase=True)
+
+        # 计算输出位置
+        out_start = start // downsample_factor
+        out_end = out_start + len(chunk_downsampled)
+        if out_end > output_length:
+            chunk_downsampled = chunk_downsampled[:output_length - out_start]
+
+        downsampled_signal[out_start:out_end] = chunk_downsampled
+
+    return downsampled_signal
+
+
+@timing_decorator()
+def average_filter(raw_data, sample_rate, window_size_sec=20):
+    kernel = np.ones(window_size_sec * sample_rate) / (window_size_sec * sample_rate)
+    filtered = ndimage.convolve1d(raw_data, kernel, mode='reflect')
+    convolve_filter_signal = raw_data - filtered
+    return convolve_filter_signal
+
+
+# 陷波滤波器
+@timing_decorator()
+def notch_filter(data, notch_freq=50.0, quality_factor=30.0, sample_rate=1000):
+    nyquist = 0.5 * sample_rate
+    norm_notch_freq = notch_freq / nyquist
+    b, a = signal.iirnotch(norm_notch_freq, quality_factor)
+    filtered_data = signal.filtfilt(b, a, data)
+    return filtered_data
--- a/utils/split_method.py
+++ b/utils/split_method.py
@ -0,0 +1,56 @@
+
+def check_split(event_mask,  current_start, window_sec, verbose=False):
+    # 检查当前窗口是否包含在禁用区间或低幅值区间内
+    resp_movement_mask = event_mask["Resp_Movement_Label"][current_start : current_start + window_sec]
+    resp_low_amp_mask = event_mask["Resp_LowAmp_Label"][current_start : current_start + window_sec]
+
+    # 体动与低幅值进行与计算
+    low_move_mask = resp_movement_mask  | resp_low_amp_mask
+    if low_move_mask.sum() > 2/3 * window_sec:
+        if verbose:
+            print(f"{current_start}-{current_start + window_sec} rejected due to movement/low amplitude mask more than 2/3")
+        return False
+    return True
+
+
+def resp_split(dataset_config, event_mask, event_list, verbose=False):
+    # 提取体动区间和呼吸低幅值区间
+    enable_list = event_list["EnableSegment"]
+    disable_list = event_list["DisableSegment"]
+
+    # 读取数据集配置
+    window_sec = dataset_config["window_sec"]
+    stride_sec = dataset_config["stride_sec"]
+
+    segment_list = []
+    disable_segment_list = []
+
+    # 遍历每个enable区间, 如果最后一个窗口不足stride的1/2，则舍弃，否则以enable_end为结尾截取一个窗口
+    for enable_start, enable_end in enable_list:
+        current_start = enable_start
+        while current_start + window_sec <= enable_end:
+            if check_split(event_mask, current_start, window_sec, verbose):
+                segment_list.append((current_start, current_start + window_sec))
+            else:
+                disable_segment_list.append((current_start, current_start + window_sec))
+            current_start += stride_sec
+        # 检查最后一个窗口是否需要添加
+        if (enable_end - current_start >= stride_sec / 2) and (enable_end - current_start >= window_sec):
+            if check_split(event_mask, enable_end - window_sec, window_sec, verbose):
+                segment_list.append((enable_end - window_sec, enable_end))
+            else:
+                disable_segment_list.append((enable_end - window_sec, enable_end))
+
+    # 遍历每个disable区间, 如果最后一个窗口不足stride的1/2，则舍弃，否则以disable_end为结尾截取一个窗口
+    for disable_start, disable_end in disable_list:
+        current_start = disable_start
+        while current_start + window_sec <= disable_end:
+            disable_segment_list.append((current_start, current_start + window_sec))
+            current_start += stride_sec
+        # 检查最后一个窗口是否需要添加
+        if (disable_end - current_start >= stride_sec / 2) and (disable_end - current_start >= window_sec):
+            disable_segment_list.append((disable_end - window_sec, disable_end))
+
+
+    return segment_list, disable_segment_list
+
--- a/utils/statistics_metrics.py
+++ b/utils/statistics_metrics.py
@ -0,0 +1,105 @@
+from utils.operation_tools import timing_decorator
+import numpy as np
+import pandas as pd
+
+@timing_decorator()
+def statistic_amplitude_metrics(data, aml_interval=None, time_interval=None):
+    """
+    计算不同幅值区间占比和时间，最后汇总成混淆矩阵
+
+    参数:
+    data: 采样率为1秒的一维序列，其中体动所在的区域用np.nan填充
+    aml_interval: 幅值区间的分界点列表，默认为[200, 500, 1000, 2000]
+    time_interval: 时间区间的分界点列表，单位为秒，默认为[60, 300, 1800, 3600]
+
+    返回:
+    confusion_matrix: 幅值-时长统计矩阵
+    summary: 汇总统计信息
+    """
+    if aml_interval is None:
+        aml_interval = [200, 500, 1000, 2000]
+
+    if time_interval is None:
+        time_interval = [60, 300, 1800, 3600]
+    # 检查输入
+    if not isinstance(data, np.ndarray):
+        data = np.array(data)
+
+    # 整个记录的时长（包括nan）
+    total_duration = len(data)
+
+    # 创建幅值标签和时间标签
+    amp_labels = [f"0-{aml_interval[0]}"]
+    for i in range(len(aml_interval) - 1):
+        amp_labels.append(f"{aml_interval[i]}-{aml_interval[i + 1]}")
+    amp_labels.append(f"{aml_interval[-1]}+")
+
+    time_labels = [f"0-{time_interval[0]}"]
+    for i in range(len(time_interval) - 1):
+        time_labels.append(f"{time_interval[i]}-{time_interval[i + 1]}")
+    time_labels.append(f"{time_interval[-1]}+")
+
+    # 初始化结果矩阵（时长）和片段数矩阵
+    result_matrix = np.zeros((len(amp_labels), len(time_labels)))  # 时长矩阵
+    segment_count_matrix = np.zeros((len(amp_labels), len(time_labels)))  # 片段数矩阵
+
+    # 有效信号总量（非NaN的数据点数量）
+    valid_signal_length = np.sum(~np.isnan(data))
+
+    # 添加信号开始和结束的边界条件
+    signal_padded = np.concatenate(([np.nan], data, [np.nan]))
+    diff = np.diff(np.isnan(signal_padded).astype(int))
+
+    # 连续片段的起始位置（从 nan 变为非 nan）
+    segment_starts = np.where(diff == -1)[0]
+    # 连续片段的结束位置（从非 nan 变为 nan）
+    segment_ends = np.where(diff == 1)[0]
+
+    # 计算每个片段的时长和平均幅值，并填充结果矩阵
+    for start, end in zip(segment_starts, segment_ends):
+        segment = data[start:end]
+        duration = end - start  # 时长（单位：秒）
+        mean_amplitude = np.nanmean(segment)  # 片段平均幅值
+
+        # 确定幅值区间
+        if mean_amplitude <= aml_interval[0]:
+            amp_idx = 0
+        elif mean_amplitude > aml_interval[-1]:
+            amp_idx = len(aml_interval)
+        else:
+            amp_idx = np.searchsorted(aml_interval, mean_amplitude)
+
+        # 确定时长区间
+        if duration <= time_interval[0]:
+            time_idx = 0
+        elif duration > time_interval[-1]:
+            time_idx = len(time_interval)
+        else:
+            time_idx = np.searchsorted(time_interval, duration)
+
+        # 在对应位置累加该片段的时长和片段数
+        result_matrix[amp_idx, time_idx] += duration
+        segment_count_matrix[amp_idx, time_idx] += 1  # 片段数加1
+
+    # 创建DataFrame以便于展示和后续处理
+    confusion_matrix = pd.DataFrame(result_matrix, index=amp_labels, columns=time_labels)
+
+    # 计算行和列的总和
+    confusion_matrix['总计'] = confusion_matrix.sum(axis=1)
+    row_totals = confusion_matrix['总计'].copy()
+
+    # 计算百分比（相对于有效记录时长）
+    confusion_matrix_percent = confusion_matrix.div(total_duration) * 100
+
+    # 汇总统计
+    summary = {
+        'total_duration': total_duration,
+        'total_valid_signal': valid_signal_length,
+        'amplitude_distribution': row_totals.to_dict(),
+        'amplitude_percent': row_totals.div(total_duration) * 100,
+        'time_distribution': confusion_matrix.sum(axis=0).to_dict(),
+        'time_percent': confusion_matrix.sum(axis=0).div(total_duration) * 100
+    }
+
+    return summary, (confusion_matrix, segment_count_matrix, confusion_matrix_percent, valid_signal_length,
+                     total_duration, time_labels, amp_labels)
--- a/排除区间.xlsx
+++ b/排除区间.xlsx