take new file

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 = loadData()
-    dataSet=np.array(dataSet)
+    dataSet = np.array(dataSet)
+    print(dataSet)
+    print(dataSet.__sizeof__())
     print(dataSet.shape)
     print("get ECG data")
 
+
 if __name__ == '__main__':
-    main()
+    main()

main.py

@@ -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()

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()

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
+