411 lines
17 KiB
Python
411 lines
17 KiB
Python
# -*- coding: cp936 -*-
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# 使用gbk编码才能显示
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"""
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@author:Marques
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@file:Prepare_Data.py
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@email:admin@marques22.com
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@email:2021022362@m.scnu.edu.cn
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@time:2022/03/26
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"""
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from datetime import datetime
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from typing import Union, List
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import pyedflib
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from pathlib import Path
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import numpy as np
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import pandas as pd
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from matplotlib import pyplot as plt, gridspec
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from Preprocessing import BCG_Operation
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from tqdm import tqdm
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plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签
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plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号
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# ['EEG F3-A2', 'EEG F4-A1', 'EEG C3-A2', 'EEG C4-A1', 'EEG O1-A2',
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# 'EEG O2-A1', 'EOG Right', 'EOG Left', 'EMG Chin', 'ECG I', 'RR',
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# 'ECG II', 'Effort Tho', 'Flow Patient', 'Flow Patient', 'Effort Abd',
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# 'SpO2', 'Pleth', 'Snore', 'Body', 'Pulse', 'Leg LEG1', 'Leg LEG2',
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# 'EEG A1-A2', 'Imp']
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class Prepare_Data:
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# 可选择的通道
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base_channel = ['EEG F3-A2', 'EEG F4-A1', 'EEG C3-A2', 'EEG C4-A1', 'EEG O1-A2', 'EEG O2-A1', 'EOG Right',
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'EOG Left', 'EMG Chin', 'ECG I', 'RR', 'ECG II', 'Effort Tho', 'Flow Patient', 'Flow Patient', 'HR',
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'Effort Abd', 'SpO2', 'Pleth', 'Snore', 'Body', 'Pulse', 'Leg LEG1', 'Leg LEG2', 'EEG A1-A2', 'Imp']
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# 显示事件
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base_event = ["Hypopnea", "Central apnea", "Obstructive apnea", "Mixed apnea"]
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# 设定事件和其对应颜色
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# 蓝色 背景
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# 粉色 低通气
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# 橙色 中枢性
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# 红色 阻塞型 与 混合型
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color_cycle = ["blue", "pink", "orange", "red", "red"]
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assert len(color_cycle) == len(base_event) + 1, "基础事件数量与颜色数量不一致"
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def __init__(self, sampNo: int, frequency: int = 100, bcg_frequency: int = 1000,
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channel_list: List[str] = ['Effort Tho', 'Effort Abd', 'SpO2', 'Flow Patient', 'Flow Patient'],
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focus_event_list: List[str] = ["Obstructive apnea"]):
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"""
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:param sampNo: 编号选择
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:param frequency: 显示采样率
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:param bcg_frequency: BCG信号采样率
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:param channel_list: 显示的通道
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:param focus_event_list: 关注暂停事件
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"""
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self.sampNo = sampNo
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self.channel_list = channel_list
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self.focus_event_list = focus_event_list
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self.frequency = frequency
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self.bcg_frequency = bcg_frequency
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self.ecg_start_time = None
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# 用来显示颜色时按点匹配事件
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self.ecg_event_label = None
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self.bcg_event_label = None
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# 仅包含关注暂停事件的列表
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self.ecg_event_label_filtered_df = None
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self.bcg_event_label_filtered_df = None
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# 所有事件列表
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self.ecg_event_label_df = None
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self.bcg_event_label_df = None
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# 各通道信号
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self.signal_select = {}
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self.check_channel()
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self.read_data(frequency, bcg_frequency)
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self.read_event()
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def check_channel(self):
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for i in self.channel_list:
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if i not in self.base_channel:
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print(f"{i} 不存在于常见通道名中")
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print(f"常见通道名：{self.channel_list}")
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def read_data(self, frequency: int = 100, bcg_frequency: int = 1000):
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bcg_path = Path(f"../Data/BCG/{self.sampNo}samp.npy")
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ecg_path = Path(f"../Data/ECG/A{str(self.sampNo).rjust(7, '0')}.edf")
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if not bcg_path.exists():
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raise FileNotFoundError(f"{bcg_path} 不存在！")
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if not ecg_path.exists():
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raise FileNotFoundError(f"{ecg_path} 不存在！")
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with pyedflib.EdfReader(str(ecg_path.resolve())) as file:
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signal_num = file.signals_in_file
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print(f"{self.sampNo} EDF file signal number is {signal_num}")
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signal_label = file.getSignalLabels()
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print(f"{self.sampNo} EDF file signal label : {signal_label}")
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self.ecg_start_time = file.getStartdatetime()
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# 根据PSG记录长度生成事件表
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self.ecg_event_label = np.zeros(file.getFileDuration() * self.frequency)
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# 打印PSG信息
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file.file_info_long()
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# sub_index 用于区分两个flow patient
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sub_index = 1
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for i, index in enumerate(signal_label):
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# 仅加载选中的通道
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if index in self.channel_list:
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# 重命名flow patient通道
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if index == 'Flow Patient':
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index = index + str(sub_index)
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sub_index += 1
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signal = file.readSignal(i)
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sample_frequency = file.getSampleFrequency(i)
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# 读取采样率 进行重采样
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if sample_frequency < frequency:
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signal = signal.repeat(frequency / sample_frequency)
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elif sample_frequency > frequency:
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signal = signal[::int(sample_frequency / frequency)]
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self.signal_select[index] = signal
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# 加载心晓信号
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signal = np.load(bcg_path)
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preprocessing = BCG_Operation(sample_rate=bcg_frequency)
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# 20Hz低通去噪
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signal1 = preprocessing.Butterworth(signal, 'lowpass', low_cut=20, order=3)
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# 0.7Hz 低通提取呼吸
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signal2 = preprocessing.Butterworth(signal, 'lowpass', low_cut=0.7, order=3)
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# 进行降采样
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signal1 = signal1[::int(bcg_frequency / frequency)]
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signal2 = signal2[::int(bcg_frequency / frequency)]
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# 根据心晓事件长度生成事件记录
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self.bcg_event_label = np.zeros(len(signal))
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self.signal_select['xin_xiao'] = signal1
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self.signal_select['xin_xiao_respire'] = signal2
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def read_event(self):
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bcg_label_path = Path(f"../Data/BCG_label/{self.sampNo}_label_all.csv")
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ecg_label_path = Path(f"../Data/ECG_label/export{self.sampNo}.csv")
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if not bcg_label_path.exists():
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raise FileNotFoundError(f"{bcg_label_path} 不存在！")
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if not ecg_label_path.exists():
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raise FileNotFoundError(f"{ecg_label_path} 不存在！")
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df = pd.read_csv(ecg_label_path, encoding='gbk')
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self.ecg_event_label_df = df
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# 过滤不关注的事件
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df = df[df["Event type"].isin(self.focus_event_list)]
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# 根据epoch进行排列方便索引
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df = df.sort_values(by='Epoch')
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self.ecg_event_label_filtered_df = df
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for one_data in df.index:
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one_data = df.loc[one_data]
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# 通过开始事件推算事件起始点与结束点
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event_start_time = datetime.strptime(one_data["Date"] + " " + one_data["Time"], '%Y/%m/%d %H:%M:%S')
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SP = (event_start_time - self.ecg_start_time).seconds
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# 对括号进行切分，避免Duration 20 (20) 这种带括号的问题
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EP = int(SP + float(one_data["Duration"].split("(")[0]))
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SP *= self.frequency
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EP *= self.frequency
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# 对事件重新编码并存到事件记录表中
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if one_data["Event type"] == "Hypopnea":
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self.ecg_event_label[SP:EP] = 1
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elif one_data["Event type"] == "Central apnea":
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self.ecg_event_label[SP:EP] = 2
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elif one_data["Event type"] == "Obstructive apnea":
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self.ecg_event_label[SP:EP] = 3
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elif one_data["Event type"] == "Mixed apnea":
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self.ecg_event_label[SP:EP] = 4
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# 读取心晓事件
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df = pd.read_csv(bcg_label_path, encoding='gbk')
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df["new_start"] = df["new_start"].astype("int")
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df["new_end"] = df["new_end"].astype("int")
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self.bcg_event_label_df = df
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# 过滤不关注事件
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df = df[df["Event type"].isin(self.focus_event_list)]
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df = df.sort_values(by='Epoch')
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self.bcg_event_label_filtered_df = df
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for one_data in df.index:
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one_data = df.loc[one_data]
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SP = one_data["new_start"] * self.frequency
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EP = one_data["new_end"] * self.frequency
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if one_data["Event type"] == "Hypopnea":
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self.bcg_event_label[SP:EP] = 1
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elif one_data["Event type"] == "Central apnea":
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self.bcg_event_label[SP:EP] = 2
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elif one_data["Event type"] == "Obstructive apnea":
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self.bcg_event_label[SP:EP] = 3
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elif one_data["Event type"] == "Mixed apnea":
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self.bcg_event_label[SP:EP] = 4
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# assert len(self.ecg_event_label_filtered_df) == len(self.bcg_event_label_filtered_df), \
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# f"PSG与心晓事件数量不一致, PSG事件数量{len(self.ecg_event_label_filtered_df)},
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# 心晓事件数量{len(self.bcg_event_label_filtered_df)}"
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def show_one_event(self, bcg_index: int, ecg_index: int, front_add_second: int = 60,
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back_add_second: int = 60, main_SA_visual: int = 1):
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"""
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:param bcg_index: 心晓事件在csv中行号
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:param ecg_index: PSG事件在csv中序号
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:param front_add_second: 向前延伸时间
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:param back_add_second: 向后延伸时间
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:param main_SA_visual: 1：仅当前事件上色 0：不上色 2：所有事件上色
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:return:
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"""
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one_bcg_data = self.bcg_event_label_df.loc[bcg_index]
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one_ecg_data = self.ecg_event_label_df.loc[ecg_index]
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# 获取ECG事件开始与结束时间
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event_start_time = datetime.strptime(one_ecg_data["Date"] + " " + one_ecg_data["Time"], '%Y/%m/%d %H:%M:%S')
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ecg_SP = (event_start_time - self.ecg_start_time).seconds
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ecg_duration = int(float(str(one_ecg_data["Duration"]).split("(")[0]) + 0.5)
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ecg_EP = ecg_SP + ecg_duration
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# 获取BCG事件开始与结束时间
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bcg_SP = one_bcg_data["new_start"]
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bcg_EP = one_bcg_data["new_end"]
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bcg_duration = int(float(str(one_bcg_data["Duration"]).split("(")[0]))
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print(ecg_SP, ecg_EP, bcg_SP, bcg_EP)
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# 进行向两边延展
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ecg_SP = ecg_SP - front_add_second
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ecg_EP = ecg_EP + back_add_second
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bcg_SP = bcg_SP - front_add_second - (ecg_duration - bcg_duration) // 2
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bcg_EP = bcg_EP + back_add_second + (ecg_duration - bcg_duration) // 2
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# 绘图
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plt.figure(figsize=(12, 6), dpi=150)
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gs = gridspec.GridSpec(7, 1, height_ratios=[1, 1, 1, 3, 1, 1, 1])
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plt.subplots_adjust(top=1, bottom=0, right=1, left=0, hspace=0, wspace=0)
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plt.margins(0, 0)
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plt.tight_layout()
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plt.subplot(gs[0])
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# ['Effort Tho', 'Effort Abd', 'SpO2', 'Flow Patient', 'Flow Patient']
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plt.plot(np.linspace(ecg_SP, ecg_EP, (ecg_EP - ecg_SP) * self.frequency),
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self.signal_select["Effort Tho"][ecg_SP * self.frequency:ecg_EP * self.frequency], label="Effort Tho")
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# 进行事件颜色标注
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for j in range(1, 5):
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mask = self.ecg_event_label[ecg_SP * self.frequency:ecg_EP * self.frequency] == j
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y = (self.signal_select["Effort Tho"][ecg_SP * self.frequency:ecg_EP * self.frequency] * mask).astype(
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'float')
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np.place(y, y == 0, np.nan)
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plt.plot(np.linspace(ecg_SP, ecg_EP, (ecg_EP - ecg_SP) * self.frequency), y, color=self.color_cycle[j])
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# 显示图注
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plt.legend(loc=1)
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# 隐藏部分边框
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ax = plt.gca()
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ax.spines["top"].set_visible(False)
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ax.spines["right"].set_visible(False)
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ax.spines["bottom"].set_visible(False)
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# 去掉x轴
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plt.xticks([])
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plt.subplot(gs[1])
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# ['Effort Tho', 'Effort Abd', 'SpO2', 'Flow Patient', 'Flow Patient']
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plt.plot(np.linspace(ecg_SP, ecg_EP, (ecg_EP - ecg_SP) * self.frequency),
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self.signal_select["Effort Abd"][ecg_SP * self.frequency:ecg_EP * self.frequency], label="Effort Abd")
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for j in range(1, 5):
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mask = self.ecg_event_label[ecg_SP * self.frequency:ecg_EP * self.frequency] == j
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y = (self.signal_select["Effort Abd"][ecg_SP * self.frequency:ecg_EP * self.frequency] * mask).astype(
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'float')
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np.place(y, y == 0, np.nan)
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plt.plot(np.linspace(ecg_SP, ecg_EP, (ecg_EP - ecg_SP) * self.frequency), y, color=self.color_cycle[j])
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plt.title(f"sampNo:{self.sampNo} Epoch:{one_ecg_data['Epoch']} Duration:{one_ecg_data['Duration']}")
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plt.legend(loc=1)
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ax = plt.gca()
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ax.spines["top"].set_visible(False)
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ax.spines["right"].set_visible(False)
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ax.spines["bottom"].set_visible(False)
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plt.xticks([])
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plt.subplot(gs[2])
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# ['Effort Tho', 'Effort Abd', 'SpO2', 'Flow Patient', 'Flow Patient']
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plt.plot(np.linspace(bcg_SP, bcg_EP, (bcg_EP - bcg_SP) * self.frequency),
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self.signal_select["xin_xiao_respire"][bcg_SP * self.frequency:bcg_EP * self.frequency], label="心晓 呼吸")
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min_bcg = self.signal_select["xin_xiao_respire"][bcg_SP * self.frequency:bcg_EP * self.frequency].min()
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len_bcg = bcg_EP * self.frequency - bcg_SP * self.frequency
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for j in range(1, 5):
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mask = self.bcg_event_label[bcg_SP * self.frequency:bcg_EP * self.frequency] == j
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y = (min_bcg.repeat(len_bcg) * mask).astype('float')
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np.place(y, y == 0, np.nan)
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plt.plot(np.linspace(bcg_SP, bcg_EP, (bcg_EP - bcg_SP) * self.frequency), y, color=self.color_cycle[j])
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# plt.title(f"sampNo:{self.sampNo} Epoch:{one_bcg_data['Epoch']} Duration:{one_bcg_data['Duration']}")
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plt.legend(loc=1)
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ax = plt.gca()
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ax.spines["top"].set_visible(False)
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ax.spines["right"].set_visible(False)
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ax.spines["bottom"].set_visible(False)
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plt.xticks([])
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plt.subplot(gs[3])
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# ['Effort Tho', 'Effort Abd', 'SpO2', 'Flow Patient', 'Flow Patient']
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plt.plot(np.linspace(bcg_SP, bcg_EP, (bcg_EP - bcg_SP) * self.frequency),
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self.signal_select["xin_xiao"][bcg_SP * self.frequency:bcg_EP * self.frequency], label="心晓")
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min_bcg = self.signal_select["xin_xiao"][bcg_SP * self.frequency:bcg_EP * self.frequency].min()
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len_bcg = bcg_EP * self.frequency - bcg_SP * self.frequency
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for j in range(1, 5):
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mask = self.bcg_event_label[bcg_SP * self.frequency:bcg_EP * self.frequency] == j
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y = (min_bcg.repeat(len_bcg) * mask).astype('float')
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np.place(y, y == 0, np.nan)
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plt.plot(np.linspace(bcg_SP, bcg_EP, (bcg_EP - bcg_SP) * self.frequency), y, color=self.color_cycle[j])
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plt.title(f"sampNo:{self.sampNo} Epoch:{one_bcg_data['Epoch']} Duration:{one_bcg_data['Duration']}")
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plt.legend(loc=1)
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ax = plt.gca()
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ax.spines["top"].set_visible(False)
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ax.spines["right"].set_visible(False)
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ax.spines["bottom"].set_visible(False)
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plt.xticks([]) # 去掉x轴
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plt.subplot(gs[4])
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# ['Effort Tho', 'Effort Abd', 'SpO2', 'Flow Patient', 'Flow Patient']
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plt.plot(np.linspace(ecg_SP, ecg_EP, (ecg_EP - ecg_SP) * self.frequency),
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self.signal_select["Flow Patient1"][ecg_SP * self.frequency:ecg_EP * self.frequency],
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label="Flow Patient1")
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for j in range(1, 5):
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mask = self.ecg_event_label[ecg_SP * self.frequency:ecg_EP * self.frequency] == j
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y = (self.signal_select["Flow Patient1"][ecg_SP * self.frequency:ecg_EP * self.frequency] * mask).astype(
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'float')
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np.place(y, y == 0, np.nan)
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plt.plot(np.linspace(ecg_SP, ecg_EP, (ecg_EP - ecg_SP) * self.frequency), y, color=self.color_cycle[j])
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plt.legend(loc=1)
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ax = plt.gca()
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ax.spines["top"].set_visible(False)
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ax.spines["right"].set_visible(False)
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ax.spines["bottom"].set_visible(False)
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plt.xticks([]) # 去掉x轴
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plt.subplot(gs[5])
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# ['Effort Tho', 'Effort Abd', 'SpO2', 'Flow Patient', 'Flow Patient']
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plt.plot(np.linspace(ecg_SP, ecg_EP, (ecg_EP - ecg_SP) * self.frequency),
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self.signal_select["Flow Patient2"][ecg_SP * self.frequency:ecg_EP * self.frequency],
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label="Flow Patient2")
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for j in range(1, 5):
|
||
mask = self.ecg_event_label[ecg_SP * self.frequency:ecg_EP * self.frequency] == j
|
||
y = (self.signal_select["Flow Patient2"][ecg_SP * self.frequency:ecg_EP * self.frequency] * mask).astype(
|
||
'float')
|
||
np.place(y, y == 0, np.nan)
|
||
plt.plot(np.linspace(ecg_SP, ecg_EP, (ecg_EP - ecg_SP) * self.frequency), y, color=self.color_cycle[j])
|
||
plt.legend(loc=1)
|
||
ax = plt.gca()
|
||
ax.spines["top"].set_visible(False)
|
||
ax.spines["right"].set_visible(False)
|
||
ax.spines["bottom"].set_visible(False)
|
||
plt.xticks([])
|
||
|
||
plt.subplot(gs[6])
|
||
# ['Effort Tho', 'Effort Abd', 'SpO2', 'Flow Patient', 'Flow Patient']
|
||
plt.plot(np.linspace(ecg_SP, ecg_EP, (ecg_EP - ecg_SP) * self.frequency),
|
||
self.signal_select["SpO2"][ecg_SP * self.frequency:ecg_EP * self.frequency], label="SpO2")
|
||
plt.legend(loc=1)
|
||
ax = plt.gca()
|
||
ax.spines["top"].set_visible(False)
|
||
ax.spines["right"].set_visible(False)
|
||
ax.spines["bottom"].set_visible(False)
|
||
plt.xticks([])
|
||
|
||
plt.show()
|
||
|
||
def show_all_event(self, start_index: int = 0, shifting: int = 0, front_add_second: int = 60,
|
||
back_add_second: int = 60, main_SA_visual: int = 1):
|
||
|
||
for index in range(start_index, len(self.bcg_event_label_filtered_df)):
|
||
self.show_one_event(self.bcg_event_label_filtered_df.index[index],
|
||
self.ecg_event_label_filtered_df.index[index + shifting],
|
||
front_add_second=front_add_second,
|
||
back_add_second=back_add_second,
|
||
main_SA_visual=main_SA_visual
|
||
)
|
||
|
||
def get_fft(self):
|
||
pass
|
||
|
||
|
||
if __name__ == '__main__':
|
||
prepareData = Prepare_Data(670)
|
||
prepareData.show_all_event()
|