bcg-signal-processor/Preprocess.py at main · guyuwei/bcg-signal-processor · GitHub

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# TODO：心脏带数据预处理
# - 使用环境变量/相对路径配置数据文件
#   BCG_ROW_PATH/ECG_ROW_PATH/ECG_RPEAKS_PATH，默认读取 data/ 下文件


import os
import heartpy as hp
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import wfdb
from sklearn import preprocessing

from biosppy.signals import ecg

BCG_Rowfpath = os.getenv('BCG_ROW_PATH', 'data/BCG_ROW.csv')
ECG_Rowfpath = os.getenv('ECG_ROW_PATH', 'data/ECG_RECEIVE.csv')
ECG_Rpeaks = os.getenv('ECG_RPEAKS_PATH', 'data/Rpeaks.csv')
# savepath
# BCG_Beats = "/Users/gyw/Desktop/Projec/基于睡眠检测的闭环自适应分类/BCG-ECG/BCG处理/BCG_Beats.csv"

sample_rate = 1000
R_before = 0.36
R_after = 0.5
Sam_before = R_before * sample_rate
Sam_after = R_after * sample_rate


def filter(data, sample_rate):
    filtered = hp.remove_baseline_wander(data, sample_rate)
    return filtered


def arctan_normalization(X):
    # 归一化
    min_max_scaler = preprocessing.MinMaxScaler(feature_range=[-1, 1])
    x = np.asarray(X).reshape(-1, 1)
    x_minmax = min_max_scaler.fit_transform(x)
    return np.array(x_minmax)


def moving_average(interval, windowsize):
    window = np.ones(int(windowsize)) / float(windowsize)
    re = np.convolve(interval, window, 'valid')
    return re
	#
BCGBeats = []
ECGBeats = []

if __name__ == '__main__':
    # load data
    start = 30000
    stop = start + 20000
    BCG = pd.read_csv(BCG_Rowfpath).dropna(axis=1)
    ECG = pd.read_csv(ECG_Rowfpath).dropna(axis=1)
    BCG = arctan_normalization(BCG)
    ECG = arctan_normalization(ECG)
    Rpeaks = pd.read_csv(ECG_Rpeaks).dropna(axis=1)

    plt.figure(figsize=(30, 15))
    plt.plot(BCG, color="blue", label="BCG", alpha=1)
    plt.plot(ECG, color="red", label="ECG", alpha=1)
    plt.title("Synchronous acquisition signal:WXK-1013", fontsize=50)
    plt.yticks(fontproperties='Times New Roman',
               size=20, weight='bold')  # 设置大小及加粗
    plt.xticks(fontproperties='Times New Roman', size=20, weight='bold')
    plt.legend(fontsize=20)
    plt.show()


    print(Rpeaks)
    for i in Rpeaks:
        i = int(i)
        bcg_beat = BCG[i - 360:i + 500]
        ecg_beat = ECG[i - 360:i + 500]
        BCGBeats.append(bcg_beat)
        ECGBeats.append(ecg_beat)
        plt.plot(ecg_beat)
        plt.plot(bcg_beat)
        plt.show()