Unet心电信号分割方法（Pytorch）

心血管疾病是一种常见病，严重影响人们的健康及日常生活。 近年来随着人们生活习惯的不断变化，心血管疾病对人们影响愈加明显，发病率呈现出逐年攀升的趋势，心血管疾病是中国城乡居民死亡的首要原因。心电图ECG已被广泛用于研究心跳活动。作为一种无创的方法，ECG的相关研究为了解心脏的功能提供了便利。对心电图进行有效分析可以为心血管疾病的诊断和防治提供丰富的信息，进而大大减缓心血管疾病对人们生活的影响。

心电信号分割是心电图解读的第一步，通过心电信号分割，有助于进一步对运动员心脏健康状况进行分析，进而减少运动员受伤、致病及运动中猝死的风险。

from typing import Union,List, Tuple
# get signal with wfdb library# using an indexdef get_signal(index:int, as_p_signal:bool=True) -> Union[wfdb.Record, np.ndarray]:    record = wfdb.rdrecord(DATA_FOLDER + "/" +  str(index))    assert type(record) is wfdb.Record
    if as_p_signal:        assert type(record.p_signal) is np.ndarray        return record.p_signal            return record
# get annotations given the ecg leaddef get_annotations(index:int, lead, as_sample=True) -> Union[wfdb.Annotation, np.ndarray]:    annotations = wfdb.rdann(DATA_FOLDER +"/" + str(index), extension = lead)    if as_sample:        return np.array(annotations.sample)    return annotations
# get a full EGC with 12 leadsdef get_full_ecg(index:int):    signal = get_signal(index)    annotations = [        get_annotations(index, lead) for lead in LEADS    ]    return signal, annotations

def get_single_lead_ecg(index, lead) -> Tuple[np.ndarray, np.ndarray]:    """    return and ecg signal and its annotations    both as ndarray    """    signal = get_signal(index)    assert type(signal) is np.ndarray    signal = signal[:, LEADS.index(lead)]
    samples = get_annotations(index, lead)    assert type(samples) is np.ndarray        return signal, samples

def get_annotations_symbols(index, lead):    ann = get_annotations(index, lead, as_sample=False)    return ann.symbol
def paired_annotation_sample_and_symbol(index, lead):    annotations_symbols = get_annotations_symbols(index, lead)    annotations_sample = get_annotations(index, lead)    return zip(annotations_sample, annotations_symbols)

def get_single_lead_ecg_with_symbols(index, lead):    """    return and ecg signal and its annotations    both as ndarray    """    signal = get_signal(index)    assert type(signal) is np.ndarray    signal = signal[:, LEADS.index(lead)]
    data = paired_annotation_sample_and_symbol(index, lead)        return signal, np.array(list(data))
# plot single lead ecg with annotationsdef plot_single_lead_ecg(index, lead):    signal, samples