ECG_by_Diffenert_CNN_Test/main.py

import wfdb
import pywt
import seaborn
#   import torch
import numpy as np
import matplotlib;matplotlib.use('TkAgg')
import tensorflow as tf
import matplotlib.pyplot as plt
from sklearn.metrics import confusion_matrix

# 测试集在数据集中所占的比例
RATIO = 0.2


# 小波去噪预处理
def denoise(data):
    # 小波变换
    coeffs = pywt.wavedec(data=data, wavelet='db5', level=9)
    cA9, cD9, cD8, cD7, cD6, cD5, cD4, cD3, cD2, cD1 = coeffs
    # 阈值去噪
    threshold = (np.median(np.abs(cD1)) / 0.6745) * (np.sqrt(2 * np.log(len(cD1))))
    cD1.fill(0)
    cD2.fill(0)
    for i in range(1, len(coeffs) - 2):
        coeffs[i] = pywt.threshold(coeffs[i], threshold)
    # 小波反变换,获取去噪后的信号
    rdata = pywt.waverec(coeffs=coeffs, wavelet='db5')
    return rdata


# 读取心电数据和对应标签,并对数据进行小波去噪
def getDataSet(number, X_data, Y_data):
    ecgClassSet = ['N', 'A', 'V', 'L', 'R']
    # 读取心电数据记录
    print("正在读取 " + number + " 号心电数据...")
    # 读取MLII导联的数据
    record = wfdb.rdrecord('W:/MIT-BIT/mit-bih-arrhythmia-database-1.0.0/' + number, channel_names=['MLII'])
    data = record.p_signal.flatten()
    rdata = denoise(data=data)
    # 获取心电数据记录中R波的位置和对应的标签
    annotation = wfdb.rdann('W:/MIT-BIT/mit-bih-arrhythmia-database-1.0.0/' + number, 'atr')
    Rlocation = annotation.sample
    Rclass = annotation.symbol
    # 去掉前后的不稳定数据
    start = 10
    end = 5
    i = start
    j = len(annotation.symbol) - end
    # 因为只选择NAVLR五种心电类型,所以要选出该条记录中所需要的那些带有特定标签的数据,舍弃其余标签的点
    # X_data在R波前后截取长度为300的数据点
    # Y_data将NAVLR按顺序转换为01234
    while i < j:
        try:
            # Rclass[i] 是标签
            lable = ecgClassSet.index(Rclass[i])
            # 基于经验值，基于R峰向前取100个点，向后取200个点
            x_train = rdata[Rlocation[i] - 100:Rlocation[i] + 200]
            X_data.append(x_train)
            Y_data.append(lable)
            i += 1
        except ValueError:
            i += 1
    return


# 加载数据集并进行预处理
def loadData():
    numberSet = ['100', '101', '103', '105', '106', '107', '108', '109', '111', '112', '113', '114', '115',
                 '116', '117', '119', '121', '122', '123', '124', '200', '201', '202', '203', '205', '208',
                 '210', '212', '213', '214', '215', '217', '219', '220', '221', '222', '223', '228', '230',
                 '231', '232', '233', '234']
    dataSet = []
    lableSet = []
    for n in numberSet:
        getDataSet(n, dataSet, lableSet)
    # 转numpy数组,打乱顺序
    dataSet = np.array(dataSet).reshape(-1, 300)
    lableSet = np.array(lableSet).reshape(-1, 1)
    train_ds = np.hstack((dataSet, lableSet))
    np.random.shuffle(train_ds)
    # 数据集及其标签集
    X = train_ds[:, :300].reshape(-1, 300, 1)
    Y = train_ds[:, 300]
    # 测试集及其标签集
    shuffle_index = np.random.permutation(len(X))
    # 设定测试集的大小 RATIO是测试集在数据集中所占的比例
    test_length = int(RATIO * len(shuffle_index))
    # 测试集的长度
    test_index = shuffle_index[:test_length]
    # 训练集的长度
    train_index = shuffle_index[test_length:]
    X_test, Y_test = X[test_index], Y[test_index]
    X_train, Y_train = X[train_index], Y[train_index]
    return X_train, Y_train, X_test, Y_test


# 构建CNN模型
def buildModel():
    newModel = tf.keras.models.Sequential([
        tf.keras.layers.InputLayer(input_shape=(300, 1)),
        # 第一个卷积层, 4 个 21x1 卷积核
        tf.keras.layers.Conv1D(filters=4, kernel_size=21, strides=1, padding='SAME', activation='tanh'),
        # 第一个池化层, 最大池化,4 个 3x1 卷积核, 步长为 2
        tf.keras.layers.MaxPool1D(pool_size=3, strides=2, padding='SAME'),
        # 第二个卷积层, 16 个 23x1 卷积核
        tf.keras.layers.Conv1D(filters=16, kernel_size=23, strides=1, padding='SAME', activation='relu'),
        # 第二个池化层, 最大池化,4 个 3x1 卷积核, 步长为 2
        tf.keras.layers.MaxPool1D(pool_size=3, strides=2, padding='SAME'),
        # 第三个卷积层, 32 个 25x1 卷积核
        tf.keras.layers.Conv1D(filters=32, kernel_size=25, strides=1, padding='SAME', activation='tanh'),
        # 第三个池化层, 平均池化,4 个 3x1 卷积核, 步长为 2
        tf.keras.layers.AvgPool1D(pool_size=3, strides=2, padding='SAME'),
        # 第四个卷积层, 64 个 27x1 卷积核
        tf.keras.layers.Conv1D(filters=64, kernel_size=27, strides=1, padding='SAME', activation='relu'),
        # 打平层,方便全连接层处理'
        tf.keras.layers.Flatten(),
        # 全连接层,128 个节点 转换成128个节点
        tf.keras.layers.Dense(128, activation='relu'),
        # Dropout层,dropout = 0.2
        tf.keras.layers.Dropout(rate=0.2),
        # 全连接层,5 个节点
        tf.keras.layers.Dense(5, activation='softmax')
    ])
    return newModel


def plotHeatMap(Y_test, Y_pred):
    con_mat = confusion_matrix(Y_test, Y_pred)
    # 绘图
    fig=plt.figure(figsize=(4, 5))
    seaborn.heatmap(con_mat, annot=True, fmt='.20g', cmap='Blues')
    plt.ylim(0, 5)
    plt.xlabel('Predicted labels')
    plt.ylabel('True labels')
    plt.show(fig)


def main():
    # X_train,Y_train为所有的数据集和标签集
    # X_test,Y_test为拆分的测试集和标签集
    X_train, Y_train, X_test, Y_test = loadData()
    print(X_train.shape)

    model = buildModel()
    model.compile(optimizer='adam',
                  loss='sparse_categorical_crossentropy', metrics=['accuracy']
                  # metrics: 列表，包含评估模型在训练和测试时的性能的指标，典型用法是metrics=[‘accuracy’]。
                  )
    model.summary()

    # 训练与验证
    model.fit(X_train, Y_train, epochs=30, batch_size=128, validation_split=RATIO)  # validation_split 训练集所占比例
    # 预测
    Y_pred = model.predict(X_test)
    print(Y_pred)
    plotHeatMap(Y_test,Y_pred)

if __name__ == '__main__':
    main()