PPG信号和ECG信号检测血管年龄

PAT 通常用作动脉硬度的间接测量值或心血管健康的指标。它与各种生理和病理状况有关，例如高血压、动脉硬化和内皮功能障碍。

通过脉搏到达时间进行测量，简单来说就是先从脉冲传输时间 PPG 数据集中提取数据，提取此数据集中每个对象的脉冲到达时间，再提取参考年龄，进行一些比较。
用的pulse transit time PPG数据集
22*3个数据记录了走跑坐
Bp_sys_start/end systolic blood pressure 收缩压
Bp_dia_start/end diastolic blood pressure 舒张压
Hr_1_start/end 测量开始时使用欧姆龙 HEM-7322 血压计测量的心率 (bpm)
Hr_2_start/end 测量开始时使用 iHealth Air 无线脉搏血氧仪测量的心率 (bpm)
spo2_start/end SpO2血氧饱和度

对于ecg和ppg信号，用neurokit2和wfdb库导入数据，
有一个需要注意的预处理操作是：
让ppg = -1*ppg，使ppg信号看起来更像血压信号。
再用neurokit2进行信号过滤，消除噪音
然后检测峰值：
ppg：

ecg：

对于峰值时间的检测，ppg和ecg略有区别：
ecg可以直接提取R 波的时间，它表示心室去极化的时间（促使心室收缩的电活动）。
ppg则一般不使用收缩峰时间，因为脉搏开始和收缩峰之间可能存在可变延迟，所以一般会用脉冲开始时间

def get_ppg_onsets(ppg, pks, fs):ons = np.empty(0)for i in range(len(pks) - 1):start = pks[i]stop = pks[i + 1]ibi = ppg[start:stop]aux_ons = np.argmin(ibi)ind_ons = aux_ons.astype(int)ons = np.append(ons, ind_ons + start)ons = ons.astype(int)return onsdef get_data(record_list,database_name,required_signals,required_duration,required_activity):matching_recs = {'dir': [], 'name': [], 'length': [], 'start_sbp': [], 'end_sbp': [], 'delta_sbp': [], 'age': []}for record in record_list:print('Record: {}'.format(record), end="", flush=True)record_data = wfdb.rdheader(record, pn_dir=database_name, rd_segments=True)# Check whether the required signals are present in the recordsigs_present = record_data.sig_nameif not all(x in sigs_present for x in required_signals):print('   (missing signals)')continueif not required_activity in record:print('   (not required activity)')continueseg_length = record_data.sig_len / (record_data.fs)if seg_length < required_duration:print(f' (too short at {seg_length / 60:.1f} mins)')continue# This record does meet the requirements, so extract information and data from it# Informationmatching_recs['dir'].append(database_name)matching_recs['name'].append(record_data.record_name)matching_recs['length'].append(seg_length)# Blood pressure measurementsstart_el = record_data.comments[0].index('<bp_sys_start>: ') + len('<bp_sys_start>: ')end_el = record_data.comments[0].index('<bp_sys_end>') - 1matching_recs['start_sbp'].append(int(record_data.comments[0][start_el:end_el]))start_el = record_data.comments[0].index('<bp_sys_end>: ') + len('<bp_sys_end>: ')end_el = record_data.comments[0].index('<bp_dia_start>') - 1matching_recs['end_sbp'].append(int(record_data.comments[0][start_el:end_el]))matching_recs['delta_sbp'].append(matching_recs['end_sbp'][-1] - matching_recs['start_sbp'][-1])# agesstart_el = record_data.comments[0].index('<age>: ') + len('<age>: ')end_el = record_data.comments[0].index('<bp_sys_start>') - 1matching_recs['age'].append(float(record_data.comments[0][start_el:end_el]))print('   (met requirements)')print(f"A total of {len(matching_recs['dir'])} out of {len(record_list)} records met the requirements.")return matching_recsdef get_pat(matching_recs,start_seconds,n_seconds_to_load,required_signals):subj_nos = [i for i in range(len(matching_recs['name']))]median_pats = []for subj_no in subj_nos:# specify this subject's record name and directory:record_name = matching_recs['name'][subj_no]record_dir = matching_recs['dir'][subj_no]# extract this subject's signalsrecord_data = wfdb.rdheader(record_name, pn_dir=record_dir, rd_segments=True)fs = record_data.fs# Specify timings of segment to be extractedsample_start = fs * start_secondssample_end = fs * (start_seconds + n_seconds_to_load)# Load segment datasegment_data = wfdb.rdrecord(record_name=record_name,channel_names=required_signals,sampfrom=sample_start,sampto=sample_end,pn_dir=record_dir)ppg_col = segment_data.sig_name.index("pleth_1")ppg_final = segment_data.p_signal[:, ppg_col]ecg_col = segment_data.sig_name.index("ecg")ecg_final = segment_data.p_signal[:, ecg_col]fs = segment_data.fsppg = -1 * ppg_final# filter signalsppg = nk2.ppg_clean(ppg, sampling_rate=fs)ecg = nk2.ecg_clean(ecg_final, sampling_rate=fs)# detect beats in signalsppg_clean = nk2.ppg_clean(ppg, sampling_rate=fs)ppg_peaks = nk2.ppg_findpeaks(ppg_clean, method="elgendi", show=False)['PPG_Peaks']ecg_clean = nk2.ecg_clean(ecg, sampling_rate=fs)ecg_signals, ecg_info = nk2.ecg_peaks(ecg_clean, method="neurokit", show=False)ecg_peaks = ecg_info["ECG_R_Peaks"]# obtain timingsecg_timings = ecg_peaksppg_timings = get_ppg_onsets(ppg, ppg_peaks, fs)# extract pulse arrival timesrel_ppg_timings = []for ecg_timing in ecg_timings:ppg_timings = np.asarray(ppg_timings)different = ppg_timings - ecg_timingdifferent = np.where(different > 0, different, 100000)idx = different.argmin()rel_ppg_timings.append(ppg_timings[idx])pats = (rel_ppg_timings - ecg_timings) / fs# find median pulse arrival time for this subjectcurr_median_pat = median(pats)median_pats.append(curr_median_pat)return median_patsdef get_age(matching_recs):ages = []subj_nos = [i for i in range(len(matching_recs['name']))]for subj_no in subj_nos:# specify this subject's record name and directory:record_name = matching_recs['name'][subj_no]record_dir = matching_recs['dir'][subj_no]# extract this subject's agecurr_age = matching_recs['age'][subj_no]ages.append(curr_age)return ages

分别比较走，跑，坐

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