Refactor imports in __init__.py, enhance resp_movement handling in HYS_process.py, and update HYS_config.yaml for movement revision parameters
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@ -28,7 +28,7 @@ import numpy as np
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import signal_method
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import os
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from matplotlib import pyplot as plt
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os.environ['DISPLAY'] = "localhost:10.0"
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os.environ['DISPLAY'] = "localhost:14.0"
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def process_one_signal(samp_id):
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signal_path = list((org_signal_root_path / f"{samp_id}").glob("OrgBCG_Sync_*.txt"))
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@ -127,33 +127,23 @@ def process_one_signal(samp_id):
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)
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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)}")
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else:
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resp_movement_mask = None
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resp_movement_mask, resp_movement_position_list = None, None
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print("resp_movement_mask is None")
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if resp_movement_mask is not None:
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# 左右翻转resp_data
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reverse_resp_data = resp_data[::-1]
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_, resp_movement_mask_reverse, _, resp_movement_position_list_reverse = signal_method.detect_movement(
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signal_data=reverse_resp_data,
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resp_movement_revise_conf = conf.get("resp_movement_revise", None)
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if resp_movement_mask is not None and resp_movement_revise_conf is not None:
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resp_movement_mask, resp_movement_position_list = signal_method.movement_revise(
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signal_data=resp_data,
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movement_mask=resp_movement_mask,
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movement_list=resp_movement_position_list,
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sampling_rate=resp_fs,
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**resp_movement_conf
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**resp_movement_revise_conf
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)
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print(f"resp_movement_mask_reverse_shape: {resp_movement_mask_reverse.shape}, num_movement_reverse: {np.sum(resp_movement_mask_reverse == 1)}, count_movement_positions_reverse: {len(resp_movement_position_list_reverse)}")
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# 将resp_movement_mask_reverse翻转回来
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resp_movement_mask_reverse = resp_movement_mask_reverse[::-1]
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print(f"After revise, resp_movement_mask_shape: {resp_movement_mask.shape}, num_movement: {np.sum(resp_movement_mask == 1)}")
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else:
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resp_movement_mask_reverse = None
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print("resp_movement_mask_reverse is None")
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print("resp_movement_mask revise is skipped")
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# 取交集
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if resp_movement_mask is not None and resp_movement_mask_reverse is not None:
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combined_resp_movement_mask = np.logical_and(resp_movement_mask, resp_movement_mask_reverse).astype(int)
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resp_movement_mask = combined_resp_movement_mask
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print(f"combined_resp_movement_mask_shape: {combined_resp_movement_mask.shape}, num_combined_movement: {np.sum(combined_resp_movement_mask == 1)}")
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else:
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print("combined_resp_movement_mask is None")
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# 分析Resp的幅值突变区间
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if resp_movement_mask is not None:
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@ -246,4 +236,4 @@ if __name__ == '__main__':
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all_samp_disable_df = utils.read_disable_excel(disable_df_path)
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process_one_signal(select_ids[5])
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process_one_signal(select_ids[0])
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@ -32,13 +32,21 @@ resp_low_amp:
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resp_movement:
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window_size_sec: 20
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stride_sec: 1
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std_median_multiplier: 3.5
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std_median_multiplier: 5
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compare_intervals_sec:
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- 60
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- 90
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- 120
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- 180
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interval_multiplier: 3.5
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merge_gap_sec: 30
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min_duration_sec: 2
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min_duration_sec: 1
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resp_movement_revise:
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up_interval_multiplier: 3
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down_interval_multiplier: 1.5
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compare_intervals_sec: 30
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merge_gap_sec: 10
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min_duration_sec: 1
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bcg:
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downsample_fs: 100
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@ -1 +1 @@
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from draw_tools.draw_statics import draw_signal_with_mask
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from .draw_statics import draw_signal_with_mask
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@ -222,7 +222,10 @@ def draw_signal_with_mask(samp_id, signal_data, resp_data, bcg_data, signal_fs,
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ax1 = fig.add_subplot(3, 1, 2, sharex=ax0)
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ax1.plot(np.linspace(0, len(resp_data) // resp_fs, len(resp_data)), resp_data, color='orange')
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ax1.plot(np.linspace(0, len(resp_data) // resp_fs, len(resp_data)), resp_data, color='gray', alpha=0.5)
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resp_data_no_movement = resp_data.copy()
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resp_data_no_movement[resp_movement_mask.repeat(int(resp_fs)) == 1] = np.nan
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ax1.plot(np.linspace(0, len(resp_data_no_movement) // resp_fs, len(resp_data_no_movement)), resp_data_no_movement, color='orange')
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ax1.set_ylabel('Amplitude')
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# ax1.set_xticklabels([])
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ax1_twin = ax1.twinx()
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@ -1 +1,3 @@
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from signal_method.rule_base_event import detect_low_amplitude_signal, detect_movement, position_based_sleep_recognition_v2
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from .rule_base_event import detect_low_amplitude_signal, detect_movement, position_based_sleep_recognition_v2
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from .rule_base_event import movement_revise
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from .time_metrics import calc_mav
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@ -1,6 +1,7 @@
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from utils.operation_tools import timing_decorator
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import numpy as np
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from utils import merge_short_gaps, remove_short_durations, event_mask_2_list
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from utils import merge_short_gaps, remove_short_durations, event_mask_2_list, collect_values
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from signal_method.time_metrics import calc_mav
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@timing_decorator()
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@ -92,14 +93,14 @@ def detect_movement(signal_data, sampling_rate, window_size_sec=2, stride_sec=No
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valid_std = original_window_std ##20250418新修改
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#---------------------- 方法一：基于STD的体动判定 ----------------------#
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# ---------------------- 方法一：基于STD的体动判定 ----------------------#
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# 计算所有有效窗口标准差的中位数
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median_std = np.median(valid_std)
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# 当窗口标准差大于中位数的倍数，判定为体动状态
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std_movement = np.where(original_window_std > median_std * std_median_multiplier, 1, 0)
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std_movement = np.where((original_window_std > (median_std * std_median_multiplier)), 1, 0)
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#------------------ 方法二：基于前后信号幅值变化的体动判定 ------------------#
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# ------------------ 方法二：基于前后信号幅值变化的体动判定 ------------------#
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amplitude_movement = np.zeros(num_original_windows, dtype=int)
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# 定义基于时间粒度的比较间隔索引
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@ -146,7 +147,6 @@ def detect_movement(signal_data, sampling_rate, window_size_sec=2, stride_sec=No
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raw_movement_mask = raw_movement_mask.repeat(stride_sec)[:len(signal_data) // sampling_rate]
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movement_mask = movement_mask.repeat(stride_sec)[:len(signal_data) // sampling_rate]
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# 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
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removed_movement_mask = (raw_movement_mask - movement_mask) > 0
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removed_movement_start = np.where(np.diff(np.concatenate([[0], removed_movement_mask])) == 1)[0]
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@ -155,8 +155,8 @@ def detect_movement(signal_data, sampling_rate, window_size_sec=2, stride_sec=No
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for start, end in zip(removed_movement_start, removed_movement_end):
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# print(start ,end)
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# 计算剔除的体动区域的幅值
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if np.nanmax(signal_data[start*sampling_rate:(end+1)*sampling_rate]) > median_std * std_median_multiplier:
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movement_mask[start:end+1] = 1
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if np.nanmax(signal_data[start * sampling_rate:(end + 1) * sampling_rate]) > median_std * std_median_multiplier:
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movement_mask[start:end + 1] = 1
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# raw体动起止位置 [[start, end], [start, end], ...]
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raw_movement_position_list = event_mask_2_list(raw_movement_mask)
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@ -167,22 +167,67 @@ def detect_movement(signal_data, sampling_rate, window_size_sec=2, stride_sec=No
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return raw_movement_mask, movement_mask, raw_movement_position_list, movement_position_list
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def movement_revise(signal_data, sampling_rate, movement_mask, std_median_multiplier=4.5):
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def movement_revise(signal_data, sampling_rate, movement_mask, movement_list, up_interval_multiplier: float,
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down_interval_multiplier: float, compare_intervals_sec, merge_gap_sec, min_duration_sec):
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"""
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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修正
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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
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参数：
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- signal_data: numpy array，输入的信号数据
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- sampling_rate: int，信号的采样率（Hz）
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- movement_mask: numpy array，已有的体动掩码（1表示体动，0表示睡眠）
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- std_median_multiplier: float，标准差中位数的乘数阈值，默认值为 4.5
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返回：
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- revised_movement_mask: numpy array，修正后的体动掩码
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"""
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pass
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window_size = sampling_rate
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stride_size = sampling_rate
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time_points = np.arange(len(signal_data))
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compare_size = int(compare_intervals_sec // (stride_size / sampling_rate))
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_, mav = calc_mav(signal_data, movement_mask=None, low_amp_mask=None, sampling_rate=sampling_rate,
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window_second=2, step_second=1,
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inner_window_second=1)
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# 往左右两边取compare_size个点的mav，取平均值
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for start, end in movement_list:
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left_values = collect_values(arr=mav, index=start - 1, step=-1, limit=compare_size, mask=movement_mask)
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right_values = collect_values(arr=mav, index=end + 5, step=1, limit=compare_size, mask=movement_mask)
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left_value_metrics = np.median(left_values) if len(left_values) > 0 else 0
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right_value_metrics = np.median(right_values) if len(right_values) > 0 else 0
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if left_value_metrics == 0:
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value_metrics = right_value_metrics
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elif right_value_metrics == 0:
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value_metrics = left_value_metrics
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else:
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value_metrics = np.mean([left_value_metrics, right_value_metrics])
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# 逐秒遍历mav，判断是否需要修正
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# 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}")
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for i in range(start, end + 5):
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# print(f"Index {i}, mav: {mav[i]:.2f}, left_mean: {left_value_mean:.2f}, right_mean: {right_value_mean:.2f}, mean: {value_mean:.2f}")
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if mav[i] > (value_metrics * up_interval_multiplier):
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movement_mask[i] = 1
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# print(f"Movement revised at index {i}, mav: {mav[i]:.2f}, threshold: {value_mean * up_interval_multiplier:.2f}")
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elif mav[i] < (value_metrics * down_interval_multiplier):
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movement_mask[i] = 0
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# print(f"Movement revised at index {i}, mav: {mav[i]:.2f}, threshold: {value_mean * down_interval_multiplier:.2f}")
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# else:
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# 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}")
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#
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# 如果需要合并间隔小的体动状态
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if merge_gap_sec > 0:
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movement_mask = merge_short_gaps(movement_mask, time_points, merge_gap_sec)
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# 如果需要移除短时体动状态
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if min_duration_sec > 0:
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movement_mask = remove_short_durations(movement_mask, time_points, min_duration_sec)
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movement_list = event_mask_2_list(movement_mask)
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return movement_mask, movement_list
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@ -435,7 +480,8 @@ def position_based_sleep_recognition_v2(signal_data, movement_mask, sampling_rat
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# 计算每个片段的幅值指标
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mav = np.nanmean(
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np.nanmax(signal_data_no_movement[start:end].reshape(-1, mav_calc_window_sec * sampling_rate), axis=0)) - np.nanmean(
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np.nanmax(signal_data_no_movement[start:end].reshape(-1, mav_calc_window_sec * sampling_rate),
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axis=0)) - np.nanmean(
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np.nanmin(signal_data_no_movement[start:end].reshape(-1, mav_calc_window_sec * sampling_rate), axis=0))
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segment_average_amplitude.append(mav)
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@ -5,10 +5,14 @@ import numpy as np
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@timing_decorator()
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def calc_mav(signal_data, movement_mask, low_amp_mask, sampling_rate=100, window_second=10, step_second=1, inner_window_second=2):
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if movement_mask is not None:
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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)}"
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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)}"
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# 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)}"
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# print(f"movement_mask_length: {len(movement_mask)}, signal_data_length: {len(signal_data)}")
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processed_mask = movement_mask.copy()
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else:
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processed_mask = None
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def mav_func(x):
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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
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mav_nan, mav = calculate_by_slide_windows(mav_func, signal_data, processed_mask, sampling_rate=sampling_rate,
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@ -1,5 +1,6 @@
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from utils.HYS_FileReader import read_label_csv, read_signal_txt, read_disable_excel
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from utils.operation_tools import load_dataset_conf, generate_disable_mask, generate_event_mask, event_mask_2_list
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from utils.operation_tools import merge_short_gaps, remove_short_durations
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from utils.event_map import E2N
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from utils.signal_process import butterworth, average_filter, downsample_signal_fast, notch_filter, bessel
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from .HYS_FileReader import read_label_csv, read_signal_txt, read_disable_excel
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from .operation_tools import load_dataset_conf, generate_disable_mask, generate_event_mask, event_mask_2_list
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from .operation_tools import merge_short_gaps, remove_short_durations
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from .operation_tools import collect_values
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from .event_map import E2N
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from .signal_process import butterworth, average_filter, downsample_signal_fast, notch_filter, bessel
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@ -125,8 +125,8 @@ def remove_short_durations(state_sequence, time_points, min_duration_sec):
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@timing_decorator()
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def calculate_by_slide_windows(func, signal_data, calc_mask, sampling_rate=100, window_second=20, step_second=None):
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# 处理标志位长度与 signal_data 对齐
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if calc_mask is None:
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calc_mask = np.zeros(len(signal_data), dtype=bool)
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# if calc_mask is None:
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# calc_mask = np.zeros(len(signal_data), dtype=bool)
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if step_second is None:
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step_second = window_second
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@ -157,6 +157,7 @@ def calculate_by_slide_windows(func, signal_data, calc_mask, sampling_rate=100,
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values_nan = values_nan.repeat(step_second)[:origin_seconds]
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if calc_mask is not None:
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for i in range(len(values_nan)):
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if calc_mask[i]:
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values_nan[i] = np.nan
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@ -169,6 +170,8 @@ def calculate_by_slide_windows(func, signal_data, calc_mask, sampling_rate=100,
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return np.interp(t, t[valid_mask], x[valid_mask])
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values = interpolate_nans(values, np.arange(len(values)))
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else:
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values = values_nan.copy()
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return values_nan, values
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@ -208,7 +211,20 @@ def generate_event_mask(signal_second: int, event_df):
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def event_mask_2_list(mask):
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mask_start = np.where(np.diff(mask, append=0) == 1)[0]
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mask_end = np.where(np.diff(mask, append=0) == -1)[0] + 1
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mask_start = np.where(np.diff(mask, append=0) == -1)[0]
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mask_end = np.where(np.diff(mask, append=0) == 1)[0] + 1
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event_list =[[start, end] for start, end in zip(mask_start, mask_end)]
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return event_list
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def collect_values(arr: np.ndarray, index: int, step: int, limit: int, mask=None) -> list:
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"""收集非 NaN 值，直到达到指定数量或边界"""
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values = []
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count = 0
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mask = mask if mask is not None else arr
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while count < limit and 0 <= index < len(mask):
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if not np.isnan(mask[index]):
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values.append(arr[index])
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count += 1
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index += step
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return values
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