1、完成了<数据精对齐>的所有代码，修复了部分内容
2、新增命名规范
This commit is contained in:
@ -51,8 +51,8 @@ class ConfigParams:
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}
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PREPROCESS_INPUT_BCG_FILENAME: str = "orgBcg_Raw_"
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PREPROCESS_INPUT_ECG_FILENAME: str = "ECG I_Raw_"
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PREPROCESS_SAVE_BCG_FILENAME: str = "DSbcg_sig_"
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PREPROCESS_SAVE_ECG_FILENAME: str = "ECG_filter_"
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PREPROCESS_SAVE_BCG_FILENAME: str = "BCG_Raw_"
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PREPROCESS_SAVE_ECG_FILENAME: str = "ECG_Raw_"
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PREPROCESS_SAVE_CHUNK_SIZE: int = 1000000
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# BCG的J峰算法定位
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@ -73,7 +73,7 @@ class ConfigParams:
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"UseCPU": False,
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"DetectMethod": ""
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}
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DETECT_JPEAK_INPUT_BCG_FILENAME: str = "DSbcg_sig_"
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DETECT_JPEAK_INPUT_BCG_FILENAME: str = "BCG_Raw_"
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DETECT_JPEAK_SAVE_FILENAME: str = "JPeak_revise"
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DETECT_JPEAK_SAVE_CHUNK_SIZE: int = 100
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@ -90,8 +90,8 @@ class ConfigParams:
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"PeaksValue": 200,
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"DetectMethod": ""
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}
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DETECT_RPEAK_INPUT_ECG_FILENAME: str = "ECG_filter_"
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DETECT_RPEAK_SAVE_FILENAME: str = "final_Rpeak"
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DETECT_RPEAK_INPUT_ECG_FILENAME: str = "ECG_Raw_"
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DETECT_RPEAK_SAVE_FILENAME: str = "Rpeak_final"
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DETECT_RPEAK_SAVE_CHUNK_SIZE: int = 100
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# 人工纠正
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@ -118,12 +118,12 @@ class ConfigParams:
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"MoveSpeed": 1000
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}
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}
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LABEL_CHECK_INPUT_BCG_FILENAME: str = "DSbcg_sig_"
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LABEL_CHECK_INPUT_BCG_FILENAME: str = "BCG_Raw_"
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LABEL_CHECK_INPUT_JPEAK_FILENAME: str = "JPeak_revise"
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LABEL_CHECK_SAVE_JPEAK_FILENAME: str = "JPeak_revise_corrected"
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LABEL_CHECK_INPUT_ECG_FILENAME: str = "ECG_filter_"
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LABEL_CHECK_INPUT_RPEAK_FILENAME: str = "final_Rpeak"
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LABEL_CHECK_SAVE_RPEAK_FILENAME: str = "final_Rpeak_corrected"
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LABEL_CHECK_INPUT_ECG_FILENAME: str = "ECG_Raw_"
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LABEL_CHECK_INPUT_RPEAK_FILENAME: str = "Rpeak_final"
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LABEL_CHECK_SAVE_RPEAK_FILENAME: str = "Rpeak_final_corrected"
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LABEL_CHECK_SAVE_CHUNK_SIZE: int = 100
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LABEL_CHECK_LABEL_TRANSPARENCY: float = 0.2
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LABEL_CHECK_ACTION_LABEL_MULTIPLE_SHORTCUT_KEY: str = "Z"
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@ -135,13 +135,21 @@ class ConfigParams:
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"ECGFreq": 1000
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}
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}
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PRECISELY_ALIGN_INPUT_BCG_FILENAME: str = "DSbcg_sig_"
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PRECISELY_ALIGN_INPUT_ORGBCG_FILENAME: str = "orgBcg_Raw_"
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PRECISELY_ALIGN_INPUT_BCG_FILENAME: str = "BCG_Raw_"
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PRECISELY_ALIGN_INPUT_JPEAK_FILENAME: str = "JPeak_revise_corrected"
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PRECISELY_ALIGN_SAVE_BCG_FILENAME: str = "Align_info"
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PRECISELY_ALIGN_INPUT_ECG_FILENAME: str = "ECG_filter_"
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PRECISELY_ALIGN_INPUT_RPEAK_FILENAME: str = "final_Rpeak_corrected"
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PRECISELY_ALIGN_SAVE_ECG_FILENAME: str = "Align_info"
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PRECISELY_ALIGN_SAVE_BCG_ALIGNINFO_FILENAME: str = "Align_info"
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PRECISELY_ALIGN_INPUT_ECG_FILENAME: str = "ECG_Raw_"
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PRECISELY_ALIGN_INPUT_RPEAK_FILENAME: str = "Rpeak_final_corrected"
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PRECISELY_ALIGN_SAVE_ECG_ALIGNINFO_FILENAME: str = "Align_info"
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PRECISELY_ALIGN_SAVE_ORGBCG_FILENAME: str = "orgBcg_Sync_"
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PRECISELY_ALIGN_SAVE_BCG_FILENAME: str = "BCG_Sync_"
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PRECISELY_ALIGN_SAVE_ECG_FILENAME: str = "ECG_Sync_"
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PRECISELY_ALIGN_SAVE_JPEAK_FILENAME: str = "JPeak_Sync"
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PRECISELY_ALIGN_SAVE_RPEAK_FILENAME: str = "Rpeak_Sync"
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PRECISELY_ALIGN_ACTION_GET_RANGE_SHORTCUT_KEY: str = "Z"
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PRECISELY_ALIGN_SAVE_CHUNK_SIZE: int = 1000000
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PRECISELY_ALIGN_SAVE_PEAK_CHUNK_SIZE: int = 100
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# 体动标注
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@ -216,6 +216,30 @@ class Constants:
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PRECISELY_ALIGN_POSTPROCESS_VIEW_FINISHED: str = "数据后处理完成"
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PRECISELY_ALIGN_POSTPROCESS_VIEW_FAILURE: str = "数据后处理失败"
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PRECISELY_ALIGN_SAVING_ALIGNINFO: str = "正在保存对齐信息"
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PRECISELY_ALIGN_SAVING_ALIGNINFO_FINISHED: str = "保存对齐信息完成"
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PRECISELY_ALIGN_SAVING_ALIGNINFO_FAILURE: str = "保存对齐信息失败"
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PRECISELY_ALIGN_SAVING_RES_ORGBCG: str = "正在保存切割后orgBcg"
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PRECISELY_ALIGN_SAVING_RES_ORGBCG_FINISHED: str = "保存切割后orgBcg完成"
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PRECISELY_ALIGN_SAVING_RES_ORGBCG_FAILURE: str = "保存切割后orgBcg失败"
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PRECISELY_ALIGN_SAVING_RES_BCG: str = "正在保存切割后BCG"
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PRECISELY_ALIGN_SAVING_RES_BCG_FINISHED: str = "保存切割后BCG完成"
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PRECISELY_ALIGN_SAVING_RES_BCG_FAILURE: str = "保存切割后BCG失败"
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PRECISELY_ALIGN_SAVING_CUT_ECG: str = "正在保存切割后ECG"
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PRECISELY_ALIGN_SAVING_CUT_ECG_FINISHED: str = "保存切割后ECG完成"
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PRECISELY_ALIGN_SAVING_CUT_ECG_FAILURE: str = "保存切割后ECG失败"
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PRECISELY_ALIGN_SAVING_CUT_JPEAK: str = "正在保存切割后J峰"
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PRECISELY_ALIGN_SAVING_CUT_JPEAK_FINISHED: str = "保存切割后J峰完成"
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PRECISELY_ALIGN_SAVING_CUT_JPEAK_FAILURE: str = "保存切割后J峰失败"
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PRECISELY_ALIGN_SAVING_CUT_RPEAK: str = "正在保存切割后R峰"
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PRECISELY_ALIGN_SAVING_CUT_RPEAK_FINISHED: str = "保存切割后R峰完成"
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PRECISELY_ALIGN_SAVING_CUT_RPEAK_FAILURE: str = "保存切割后R峰失败"
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PRECISELY_ALIGN_FAILURE_REASON = {
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"Data_Path_Not_Exist": "（数据路径不存在）",
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"Read_Data_Exception": "（读取数据异常）",
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@ -226,6 +250,11 @@ class Constants:
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"Calculate_Correlation_Exception": "（计算相关性异常）",
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"Correlation_Align_Exception": "（处理相关对齐异常）",
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"PostProcess_Align_Exception": "（数据后处理异常）",
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"res_orgBcg_Not_Exist": "（切割后orgBcg不存在）",
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"res_BCG_Not_Exist": "（切割后BCG不存在）",
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"cut_ECG_Not_Exist": "（切割后ECG不存在）",
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"cut_Jpeak_Not_Exist": "（切割后J峰不存在）",
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"cut_Rpeak_Not_Exist": "（切割后R峰不存在）",
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"Save_Exception": "（保存异常）"
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}
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@ -1,465 +0,0 @@
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import os
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import shutil
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import pandas as pd
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from resampy import resample
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from scipy import signal
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import numpy as np
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import matplotlib.pyplot as plt
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def read_data(root):
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orgBCGpath = os.path.join(root, 'raw_org.txt')
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BCGpath = os.path.join(root, 'DSbcg_sig_1000hz3.txt')
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ECGpath = os.path.join(root, 'filter_ecg.txt')
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Jpeakspath = os.path.join(root, 'Jpeak.txt')
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Rpeakspath = os.path.join(root, 'final_Rpeak.txt')
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orgBCG = np.array(pd.read_csv(orgBCGpath, header=None)).reshape(-1)
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# B, A = signal.butter(4, np.array([2,8])*2/1000, 'bandpass')
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# BCG = signal.filtfilt(B, A, orgBCG)
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BCG = np.array(pd.read_csv(BCGpath)).reshape(-1)
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ECG = np.array(pd.read_csv(ECGpath, header=None)).reshape(-1)
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Jpeaks = np.loadtxt(Jpeakspath).astype(int)
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Rpeaks = np.loadtxt(Rpeakspath).astype(int)
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return orgBCG, BCG, ECG, Jpeaks, Rpeaks
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def Align_two_way(Jpeaks, Rpeaks, fs=1000):
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def get_info(Jpeaks, Rpeaks):
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"""
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:param Jpeaks:
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:param Rpeaks:
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:return:
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"""
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RRIs = np.diff(Rpeaks)
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JJIs = np.diff(Jpeaks)
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RRIVs = np.diff(RRIs)
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JJIVs = np.diff(JJIs)
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N_JJIV = get_area(Jpeaks, JJIVs, Rpeaks, RRIVs, title='JJIV')
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N_RRIV = get_area(Rpeaks, RRIVs, Jpeaks, JJIVs, N_JJIV=N_JJIV[0], title='RRIV')
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JJIs_cut = JJIs[N_JJIV[1, 0]: N_JJIV[1, 1]]
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RRIs_cut = RRIs[N_RRIV[1, 0]: N_RRIV[1, 1]]
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Rpeaks_cut = Rpeaks[N_RRIV[1, 0]:N_RRIV[1, 1] + 2]
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Jpeaks_cut = Jpeaks[N_JJIV[1, 0]:N_JJIV[1, 1] + 2]
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RRI = np.diff(Rpeaks_cut)
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# shift = get_IIV_shift(JJIs_cut, RRIs_cut)
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shift, correlation_IIV, correlation_II, total_time_ratio = get_IIV_shift(JJIs_cut, RRIs_cut)
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offset_interval = np.sum(RRI[:shift]) - (Jpeaks_cut[0] - Rpeaks_cut[0]) # BCG 相对于ECG的偏移量
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anchor_R = Rpeaks[N_RRIV[1, 0] + shift]
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anchor_J = Jpeaks[N_JJIV[1, 0]]
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# info = {'N_JJIV':N_JJIV, 'N_RRIV':N_RRIV, 'shift':shift, 'offset_interval':offset_interval, 'anchors':[anchor_R, anchor_J]}
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info = {'N_JJIV': N_JJIV, 'N_RRIV': N_RRIV, 'correlation_IIV': correlation_IIV,
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'correlation_II': correlation_II, 'shift': shift, 'total_time_ratio': total_time_ratio,
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'offset_interval': offset_interval, 'anchors': [anchor_R, anchor_J]}
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return info
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info1 = get_info(Jpeaks, Rpeaks)
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offset_interval1 = info1['offset_interval']
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anchor1 = info1['anchors']
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offset_interval1_T = offset_interval1 / fs # 前端偏移时间量，大于0
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print("前端对齐的偏移量为：{}s".format(offset_interval1_T))
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# while True:
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info2 = get_info(Jpeaks, Rpeaks)
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anchor2 = info2['anchors']
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offset_interval2 = info2['offset_interval']
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offset_interval2_T = offset_interval2 / fs # 后端偏移时间量，大于0
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print("后端对齐的偏移量为：{}s".format(offset_interval2_T))
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orgfs = (int(anchor2[1]) - int(anchor1[1])) * fs / (int(anchor2[0]) - int(anchor1[0]))
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offset_anchor = anchor1[0] - anchor1[1]
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info = {'1': info1, '2': info2, 'offset_anchor': offset_anchor, 'orgfs': orgfs}
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return info
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################################################ Align utils #############################################################
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def get_area(peaks1_, data1, peaks2_, data2, fs=1000, N_JJIV=None, title='JJIV'):
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global N, line0, line1
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peaks1 = peaks1_[:-2] / fs
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peaks2 = peaks2_[:-2] / fs
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ylim = [-500, 500]
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'''手动选择区域'''
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N = np.array([]).astype(int)
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fig, ax = plt.subplots(nrows=2, ncols=1, sharex=True, sharey=True)
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ax0, ax1 = ax[0], ax[1]
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ax0.set_title(title)
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ax0.stem(peaks1, data1, markerfmt='C0.')
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line0, = ax0.plot(peaks1, data1, 'o', c='b', markersize=2, picker=True, pickradius=5)
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line1, = ax0.plot(peaks1[N], data1[N] + 1, 'v', c='r', picker=True, pickradius=5)
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ax0.set_ylim(ylim[0], ylim[1])
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ax0.grid()
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ax1.stem(peaks2, data2, markerfmt='C0.')
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if N_JJIV is not None:
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N_JJIV2 = np.copy(N_JJIV)
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N_JJIV2 = N_JJIV2 / fs
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ax1.plot([N_JJIV2[0], N_JJIV2[0]], [ylim[0], ylim[1]], 'k--')
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ax1.plot([N_JJIV2[1], N_JJIV2[1]], [ylim[0], ylim[1]], 'k--')
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ax1.set_ylim(ylim[0], ylim[1])
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def onpick(event):
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global line0, line1
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global N
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thisline = event.artist
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xdata = thisline.get_xdata()
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ind = event.ind[-1]
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MM = ind
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if thisline == line0:
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if len(N) < 2:
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N = np.append(N, MM)
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N.sort()
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elif thisline == line1:
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N = np.delete(N, int(ind))
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xlime = ax0.get_xlim()
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ylime = ax0.get_ylim()
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ax0.clear()
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ax0.set_title(title)
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ax0.stem(peaks1, data1, markerfmt='C0.')
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line0, = ax0.plot(peaks1, data1, 'o', c='b', markersize=2, picker=True, pickradius=5)
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line1, = ax0.plot(peaks1[N], data1[N] + 1, 'v', c='r', picker=True, pickradius=5)
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ax0.set_xlim(xlime)
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ax0.set_ylim(ylime)
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ax0.grid()
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plt.draw()
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fig.canvas.mpl_connect('pick_event', onpick)
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plt.show()
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if len(N) == 0:
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N = np.array([0, len(data1) - 1]).astype(int)
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N_peak = peaks1_[N]
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N_out = np.asarray([N_peak[:2], N[:2]]).astype(int)
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return N_out
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def get_IIV_shift(JJIs, RRIs):
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global shift, line0
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RRIVs = np.diff(RRIs)
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JJIVs = np.diff(JJIs)
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corre_ = np.correlate(RRIVs, JJIVs, 'valid')
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'''手动选择偏移'''
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corre = np.copy(corre_)
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shift = np.argmax(corre)
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RRIVs_cut = RRIVs[shift:shift + len(JJIVs)]
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RRIs_cut = RRIs[shift:shift + len(JJIs)]
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correlation = np.corrcoef(RRIVs_cut, JJIVs)
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correlation_IIV = (correlation[0, 1] + correlation[1, 0]) / 2
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correlation = np.corrcoef(RRIs_cut, JJIs)
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correlation_II = (correlation[0, 1] + correlation[1, 0]) / 2
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tmp = RRIVs_cut * JJIVs
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same_sign_rate = np.sum(tmp > 0) / len(JJIVs)
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total_time_ratio = np.sum(JJIs) / np.sum(RRIs[shift:shift + len(JJIs)])
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fig, ax = plt.subplots(2, 1)
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ax0, ax1 = ax[0], ax[1]
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ax0.set_title(
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"corre_IIV: {}, corre_II: {}\n same_sign_rate:{}, total_time_ratio: {}\n shift: {}".format(correlation_IIV,
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correlation_II,
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same_sign_rate,
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total_time_ratio,
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shift))
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ax0.stem(corre, markerfmt='C0.', label='corre(RRIV, JJIV)')
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line0, = ax0.plot(corre, 'o', c='b', markersize=2, picker=True, pickradius=5)
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ax0.plot(shift, corre[shift] + 1, 'v', c='r', picker=True, pickradius=5)
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ax0.legend()
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ax0.grid()
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ax1.stem(RRIVs, markerfmt='b.', label='RRIV')
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ax1.stem(np.arange(shift, shift + len(JJIVs)), JJIVs, markerfmt='ko', label='JJIV')
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ax1.legend()
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def onpick(event):
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global line0
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global shift
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thisline = event.artist
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xdata = thisline.get_xdata()
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ind = event.ind[-1]
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shift = int(xdata[ind])
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RRIVs_cut = RRIVs[shift:shift + len(JJIVs)]
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RRIs_cut = RRIs[shift:shift + len(JJIs)]
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correlation = np.corrcoef(RRIVs_cut, JJIVs)
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correlation_IIV = (correlation[0, 1] + correlation[1, 0]) / 2
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correlation = np.corrcoef(RRIs_cut, JJIs)
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correlation_II = (correlation[0, 1] + correlation[1, 0]) / 2
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tmp = RRIVs_cut * JJIVs
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same_sign_rate = np.sum(tmp > 0) / len(JJIVs)
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total_time_ratio = np.sum(JJIs) / np.sum(RRIs[shift:shift + len(JJIs)])
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xlime = ax0.get_xlim()
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ylime = ax0.get_ylim()
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ax0.clear()
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ax0.set_title(
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"corre_IIV: {}, corre_II: {}\n same_sign_rate:{}, total_time_ratio: {}\n shift: {}".format(correlation_IIV,
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correlation_II,
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same_sign_rate,
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total_time_ratio,
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shift))
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ax0.stem(corre, markerfmt='C0.', label='corre(RRIV, JJIV)')
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line0, = ax0.plot(corre, 'o', c='b', markersize=2, picker=True, pickradius=5)
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ax0.plot(shift, corre[shift] + 1, 'v', c='r', picker=True, pickradius=5)
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ax0.legend()
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ax0.grid()
|
||||
ax0.set_xlim(xlime)
|
||||
ax0.set_ylim(ylime)
|
||||
xlime = ax1.get_xlim()
|
||||
ylime = ax1.get_ylim()
|
||||
ax1.clear()
|
||||
ax1.stem(RRIVs, markerfmt='b.', label='RRIV')
|
||||
ax1.stem(np.arange(shift, shift + len(JJIVs)), JJIVs, markerfmt='ko', label='JJIV')
|
||||
ax1.legend()
|
||||
ax1.set_xlim(xlime)
|
||||
ax1.set_ylim(ylime)
|
||||
plt.draw()
|
||||
|
||||
fig.canvas.mpl_connect('pick_event', onpick)
|
||||
plt.show()
|
||||
|
||||
# return shift
|
||||
return shift, correlation_IIV, correlation_II, total_time_ratio
|
||||
|
||||
|
||||
def main():
|
||||
def offset_correct(BCG, ECG, anchors):
|
||||
global line0, line1, N, M
|
||||
a = np.max([np.max(ECG), np.max(BCG)])
|
||||
b = np.min([np.min(ECG), np.min(BCG)])
|
||||
peaks_ECG, _ = signal.find_peaks(ECG)
|
||||
peaks_BCG, _ = signal.find_peaks(BCG)
|
||||
N, M = np.array([]).astype(int), np.array([]).astype(int)
|
||||
fig, ax = plt.subplots()
|
||||
ax.set_title("offset correct")
|
||||
ax.plot(ECG)
|
||||
ax.plot(BCG)
|
||||
ax.plot([anchors[0], anchors[0]], [b, a], 'k--')
|
||||
ax.plot([anchors[1], anchors[1]], [b, a], 'k--')
|
||||
ax.plot(N, ECG[N] + 1, 'rv')
|
||||
ax.plot(M, BCG[M] + 1, 'yv')
|
||||
line0, = ax.plot(peaks_ECG, ECG[peaks_ECG], 'o', markersize=2, c='g', picker=True, pickradius=5)
|
||||
line1, = ax.plot(peaks_BCG, BCG[peaks_BCG], 'o', markersize=2, c='g', picker=True, pickradius=5)
|
||||
ax.grid()
|
||||
|
||||
def onpick(event):
|
||||
global line0, line1, N, M
|
||||
thisline = event.artist
|
||||
xdata = thisline.get_xdata()
|
||||
ind = event.ind[-1]
|
||||
nm = int(xdata[ind])
|
||||
if thisline == line0:
|
||||
if nm in N:
|
||||
idx = np.where(N == nm)[0]
|
||||
N = np.delete(N, idx)
|
||||
elif len(N) < 2:
|
||||
N = np.append(N, nm)
|
||||
elif thisline == line1:
|
||||
if nm in M:
|
||||
idx = np.where(M == nm)[0]
|
||||
M = np.delete(M, idx)
|
||||
elif len(M) < 2:
|
||||
M = np.append(M, nm)
|
||||
|
||||
xlime = ax.get_xlim()
|
||||
ylime = ax.get_ylim()
|
||||
ax.clear()
|
||||
ax.set_title("offset correct")
|
||||
ax.plot(ECG)
|
||||
ax.plot(BCG)
|
||||
ax.plot([anchors[0], anchors[0]], [b, a], 'k--')
|
||||
ax.plot([anchors[1], anchors[1]], [b, a], 'k--')
|
||||
ax.plot(N, ECG[N] + 1, 'rv')
|
||||
ax.plot(M, BCG[M] + 1, 'yv')
|
||||
line0, = ax.plot(peaks_ECG, ECG[peaks_ECG], 'o', markersize=2, c='g', picker=True, pickradius=5)
|
||||
line1, = ax.plot(peaks_BCG, BCG[peaks_BCG], 'o', markersize=2, c='g', picker=True, pickradius=5)
|
||||
ax.grid()
|
||||
ax.set_xlim(xlime)
|
||||
ax.set_ylim(ylime)
|
||||
plt.draw()
|
||||
|
||||
fig.canvas.mpl_connect('pick_event', onpick)
|
||||
plt.show()
|
||||
|
||||
if len(N) == 0 or len(M) == 0:
|
||||
off = 0
|
||||
else:
|
||||
M.sort()
|
||||
N.sort()
|
||||
off = round(((int(N[1]) - int(M[1])) + (int(N[0]) - int(M[0]))) / 2)
|
||||
|
||||
return off
|
||||
|
||||
'''输入信息（如数据路径、采样频率）'''
|
||||
fs = 1000
|
||||
root = r"E:\data_annotation\pratice_data\282" # E:\BCG_data\703
|
||||
|
||||
saveroot = os.path.join(root, 'Align')
|
||||
if not os.path.exists(saveroot):
|
||||
os.mkdir(saveroot)
|
||||
else:
|
||||
shutil.rmtree(saveroot)
|
||||
os.mkdir(saveroot)
|
||||
|
||||
'''获取数据（BCG、ECG、J_peak、R_peak）'''
|
||||
raw_org_, DSbcg_sig_1000hz3_, filter_ecg_, Jpeak_, final_Rpeak_ = read_data(root)
|
||||
|
||||
orgBCG = raw_org_
|
||||
BCG = DSbcg_sig_1000hz3_
|
||||
ECG = filter_ecg_
|
||||
Rpeaks = final_Rpeak_
|
||||
Jpeaks = Jpeak_
|
||||
|
||||
orgbcg_len = len(BCG)
|
||||
argmax_bcg = np.argmax(BCG)
|
||||
orgecg_len = len(ECG)
|
||||
argmax_ecg = np.argmax(ECG)
|
||||
|
||||
# plt.figure()
|
||||
# plt.subplot(2,1,1)
|
||||
# plt.plot(BCG[:3600000])
|
||||
# plt.subplot(2,1,2)
|
||||
# plt.plot(ECG)
|
||||
|
||||
info = Align_two_way(Jpeaks, Rpeaks, fs=fs)
|
||||
|
||||
print(info)
|
||||
|
||||
anchor0 = info['1']['anchors']
|
||||
anchor1 = info['2']['anchors']
|
||||
|
||||
off = info['offset_anchor']
|
||||
orgfs = info['orgfs']
|
||||
|
||||
if off > 0:
|
||||
ECG = ECG[off:]
|
||||
anchor0[0] = anchor0[0] - off
|
||||
anchor1[0] = anchor1[0] - off
|
||||
idxs = np.where(Rpeaks > off)[0]
|
||||
Rpeaks = Rpeaks[idxs] - off
|
||||
else:
|
||||
BCG = BCG[-off:]
|
||||
orgBCG = orgBCG[-off:]
|
||||
anchor0[1] = anchor0[1] + off
|
||||
anchor1[1] = anchor1[1] + off
|
||||
|
||||
BCG_res = resample(BCG, orgfs, fs)
|
||||
orgBCG_res = resample(orgBCG, orgfs, fs)
|
||||
|
||||
# ————————————————————————————————————————————————————————————————
|
||||
anchor0[1] = round(int(anchor0[1]) * fs / orgfs)
|
||||
anchor1[1] = round(int(anchor1[1]) * fs / orgfs)
|
||||
off = anchor1[0] - anchor1[1]
|
||||
if off > 0:
|
||||
ECG = ECG[off:]
|
||||
anchor0[0] = anchor0[0] - off
|
||||
anchor1[0] = anchor1[0] - off
|
||||
idxs = np.where(Rpeaks > off)[0]
|
||||
Rpeaks = Rpeaks[idxs] - off
|
||||
else:
|
||||
BCG_res = BCG_res[-off:]
|
||||
orgBCG_res = orgBCG_res[-off:]
|
||||
anchor0[1] = anchor0[1] + off
|
||||
anchor1[1] = anchor1[1] + off
|
||||
|
||||
datalen = np.min([len(ECG), len(BCG_res)])
|
||||
ECG = ECG[:datalen]
|
||||
BCG_res = BCG_res[:datalen]
|
||||
orgBCG_res = orgBCG_res[:datalen]
|
||||
|
||||
idxs = np.where(Rpeaks < datalen)[0]
|
||||
Rpeaks = Rpeaks[idxs]
|
||||
|
||||
# ——————————————————————————————————————————————————————————————————
|
||||
off = offset_correct(BCG_res, ECG, [anchor0[0], anchor1[0]])
|
||||
if off > 0:
|
||||
ECG = ECG[off:]
|
||||
anchor0[0] = anchor0[0] - off
|
||||
anchor1[0] = anchor1[0] - off
|
||||
idxs = np.where(Rpeaks > off)[0]
|
||||
Rpeaks = Rpeaks[idxs] - off
|
||||
else:
|
||||
BCG_res = BCG_res[-off:]
|
||||
orgBCG_res = orgBCG_res[-off:]
|
||||
anchor0[1] = anchor0[1] + off
|
||||
anchor1[1] = anchor1[1] + off
|
||||
|
||||
info['offset_correct'] = off
|
||||
|
||||
datalen = np.min([len(ECG), len(BCG_res)])
|
||||
ECG = ECG[:datalen]
|
||||
BCG_res = BCG_res[:datalen]
|
||||
orgBCG_res = orgBCG_res[:datalen]
|
||||
|
||||
idxs = np.where(Rpeaks < datalen)[0]
|
||||
Rpeaks = Rpeaks[idxs]
|
||||
|
||||
Jpeaks = []
|
||||
peaks, _ = signal.find_peaks(BCG_res)
|
||||
for i in Rpeaks:
|
||||
tmp = np.abs(peaks - i)
|
||||
idx = np.argmin(tmp)
|
||||
Jpeaks.append(peaks[idx])
|
||||
Jpeaks = np.asarray(Jpeaks).astype(int)
|
||||
|
||||
# ----------------2024.4.17 CYS 新增计算BCG与ECG前后两端切割的横坐标----------------
|
||||
orgfs = info['orgfs'] # 原始数据采样频率
|
||||
frontcut_index_bcg = int((argmax_bcg - np.argmax(BCG_res) / 1000 * orgfs))
|
||||
backcut_index_bcg = int((len(BCG_res) / 1000 * orgfs + argmax_bcg - np.argmax(BCG_res) / 1000 * orgfs))
|
||||
|
||||
frontcut_index_ecg = argmax_ecg - np.argmax(ECG)
|
||||
backcut_index_ecg = len(ECG) + argmax_ecg - np.argmax(ECG)
|
||||
|
||||
print("1. BCG前段被切割的坐标值为：", frontcut_index_bcg)
|
||||
print("1. BCG后段被切割的坐标值为：", backcut_index_bcg)
|
||||
print("2. ECG前段被切割的坐标值为：", frontcut_index_ecg)
|
||||
print("2. ECG后段被切割的坐标值为：", backcut_index_ecg)
|
||||
info = {'0': info, 'frontcut_index_bcg': frontcut_index_bcg, 'backcut_index_bcg': backcut_index_bcg,
|
||||
'frontcut_index_ecg': frontcut_index_ecg, 'backcut_index_ecg': backcut_index_ecg}
|
||||
# ------------------------------------------------------------------------------
|
||||
|
||||
plt.figure()
|
||||
plt.plot(ECG, label='ECG')
|
||||
plt.plot(BCG_res, label='BCG')
|
||||
plt.plot(Rpeaks, ECG[Rpeaks], 'ko', label='Rpeaks')
|
||||
plt.plot(Jpeaks, BCG_res[Jpeaks], 'rv', label='Jpeaks')
|
||||
# plt.legend()
|
||||
plt.show()
|
||||
|
||||
savepath = os.path.join(saveroot, 'Align_info.npy')
|
||||
np.save(savepath, info)
|
||||
savepath = os.path.join(saveroot, 'Align_info.txt')
|
||||
with open(savepath, 'w') as file:
|
||||
file.write(str(info))
|
||||
|
||||
savepath = os.path.join(saveroot, 'orgData_sync.txt')
|
||||
pd.DataFrame(np.round(orgBCG_res, 4)).to_csv(savepath, index=None, header=None)
|
||||
savepath = os.path.join(saveroot, 'BCG_sync.txt')
|
||||
pd.DataFrame(np.round(BCG_res, 4)).to_csv(savepath, index=None, header=None)
|
||||
savepath = os.path.join(saveroot, 'ECG_sync.txt')
|
||||
pd.DataFrame(ECG).to_csv(savepath, index=None, header=None)
|
||||
savepath = os.path.join(saveroot, 'Jpeaks_sync.txt')
|
||||
pd.DataFrame(Jpeaks).to_csv(savepath, index=None, header=None)
|
||||
savepath = os.path.join(saveroot, 'Rpeaks_sync.txt')
|
||||
pd.DataFrame(Rpeaks).to_csv(savepath, index=None, header=None)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Reference in New Issue
Block a user