diff --git a/ECG_read_by_wfdb.py b/ECG_read_by_wfdb.py index 1abe4f5..c24dc22 100644 --- a/ECG_read_by_wfdb.py +++ b/ECG_read_by_wfdb.py @@ -14,7 +14,7 @@ def getDataset(number, X_data): Rclass = annotation.symbol X_data.append(data) - + wfdb.plot_wfdb(record=record, annotation=annotation, time_units='seconds') return @@ -26,14 +26,18 @@ def loadData(): dataSet = [] for n in numberSet: getDataset(n, dataSet) - print(dataSet) + return dataSet + def main(): - dataSet=loadData() - dataSet=np.array(dataSet) + dataSet = loadData() + dataSet = np.array(dataSet) + print(dataSet) + print(dataSet.__sizeof__()) print(dataSet.shape) print("get ECG data") + if __name__ == '__main__': - main() \ No newline at end of file + main() diff --git a/main.py b/main.py deleted file mode 100644 index 1ceca61..0000000 --- a/main.py +++ /dev/null @@ -1,159 +0,0 @@ -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('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('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 - 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) - plotHeatMap(Y_test,Y_pred) - -if __name__ == '__main__': - main() diff --git a/noise_take_ecg.py b/noise_take_ecg.py new file mode 100644 index 0000000..10fa811 --- /dev/null +++ b/noise_take_ecg.py @@ -0,0 +1,26 @@ +import numpy as np +import pywt +import wfdb +from matplotlib import pyplot as plt + +#from ECG_read_weiyuqing_without_wfdb import PATH + +record=wfdb.rdrecord('./mit-bih-arrhythmia-database-1.0.0/'+100,channel_names=['MLII']) +data=record.p_signal.flatten() + +coeffs=pywt.wavedec(data=data,wavelet='db5',level=9) +cA9,cD9, cD8, cD7, cD6, cD5, cD4, cD3, cD2, cD1 = coeffs + +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) + +rdata=pywt.waverec(coeffs=coeffs,wavelet='db5') + +plt.figure(figsize=(20,4)) +plt.plot(data) +plt.show() +plt.plot(rdata) +plt.show() \ No newline at end of file diff --git a/wfdb_use.py b/wfdb_use.py new file mode 100644 index 0000000..427e8c8 --- /dev/null +++ b/wfdb_use.py @@ -0,0 +1,45 @@ +import os + +import numpy as np +import wfdb +import matplotlib.pyplot + +PATH = './mit-bih-arrhythmia-database-1.0.0/' + + +def read_ecg_data(data): + # get the data from the ecg data file and set begin and end points + record = wfdb.rdrecord(PATH + data, sampfrom=0, sampto=1500) + # return type record + print(type(record)) + print(dir(record)) + # get the Lead signal （len、filename、number、signalname、Sample rate + print(record.p_signal) + print(np.shape(record.p_signal)) + + print(record.sig_len) + + print(record.record_name) + + print(record.n_sig) + + print(record.sig_name) + + print(record.fs) + + annotations = wfdb.rdann(PATH+data,'atr') + print(type(annotations)) + print(dir(annotations)) + + #get R + print(annotations.sample) + #get label of Heart beat + print(annotations.sample) + #num + print(annotations.ann_len) + #show label of beat + print(wfdb.show_ann_labels()) + + matplotlib.pyplot.draw_ecg(record.p_signal) + return record.p_signal +