基于深度学习的心律失常检测（ECG）.zip

import wfdb import pywt import seaborn import numpy as np 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, 这边路径换成自己的下载路径就行 record = wfdb.rdrecord('D:/MIT-BIH-360/' + 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('D:/MIT-BIH-360/' + 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 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() 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) if __name__ == '__main__': main()