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 ons

def 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 record
        sigs_present = record_data.sig_name
        if not all(x in sigs_present for x in required_signals):
            print('   (missing signals)')
            continue

        if not required_activity in record:
            print('   (not required activity)')
            continue

        seg_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
        # Information
        matching_recs['dir'].append(database_name)
        matching_recs['name'].append(record_data.record_name)
        matching_recs['length'].append(seg_length)

        # Blood pressure measurements
        start_el = record_data.comments[0].index('<bp_sys_start>: ') + len('<bp_sys_start>: ')
        end_el = record_data.comments[0].index('<bp_sys_end>') - 1
        matching_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>') - 1
        matching_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])

        # ages
        start_el = record_data.comments[0].index('<age>: ') + len('<age>: ')
        end_el = record_data.comments[0].index('<bp_sys_start>') - 1
        matching_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_recs

def 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 signals

        record_data = wfdb.rdheader(record_name, pn_dir=record_dir, rd_segments=True)
        fs = record_data.fs

        # Specify timings of segment to be extracted
        sample_start = fs * start_seconds
        sample_end = fs * (start_seconds + n_seconds_to_load)

        # Load segment data
        segment_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.fs
        ppg = -1 * ppg_final

        # filter signals
        ppg = nk2.ppg_clean(ppg, sampling_rate=fs)
        ecg = nk2.ecg_clean(ecg_final, sampling_rate=fs)

        # detect beats in signals
        ppg_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 timings
        ecg_timings = ecg_peaks
        ppg_timings = get_ppg_onsets(ppg, ppg_peaks, fs)

        # extract pulse arrival times
        rel_ppg_timings = []
        for ecg_timing in ecg_timings:
            ppg_timings = np.asarray(ppg_timings)
            different = ppg_timings - ecg_timing
            different = 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 subject
        curr_median_pat = median(pats)
        median_pats.append(curr_median_pat)

    return median_pats

def 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 age
        curr_age = matching_recs['age'][subj_no]
        ages.append(curr_age)
    return ages

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