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Author: Aiwiscal

Classification-DL_CNN.py

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+# -*- coding: utf-8 -*-
+"""
+Created on Thu May 17 21:01:53 2018
+
+@author: Winham
+
+===================基于1维CNN的ECG分类算法========================
+
+*需要第三方工具包numpy,h5py,scikit-learn
+*基于深度学习框架TensorFlow
+*涉及函数的使用方法可自行查看工具包文档，baidu即可得
+*ECG算法入门系列博客：https://blog.csdn.net/qq_15746879
+*开源github：https://github.com/Aiwiscal
+
+*本代码所需要的数据和标签文件来自matlab提取
+*详情：https://blog.csdn.net/qq_15746879/article/details/80340958
+==================================================================
+"""
+#载入所需工具包
+import time
+import numpy as np
+import h5py as hp
+import tensorflow as tf
+from sklearn.metrics import confusion_matrix
+
+sess=tf.InteractiveSession()
+
+#载入.mat文件的函数,h5py解码并转换为numpy数组
+def load_mat(path_data,name_data,dtype='float32'):
+    data=hp.File(path_data)
+    arrays_d={}
+    for k,v in data.items():
+        arrays_d[k]=np.array(v)
+    dataArr=np.array(arrays_d[name_data],dtype=dtype)
+    return dataArr
+
+#使用TensorFlow组件完成CNN网络的搭建，与教程中参数略有不同
+def CNNnet(inputs,n_class):
+    conv1 = tf.layers.conv1d(inputs=inputs, filters=4, kernel_size=31, strides=1, \
+                             padding='same', activation = tf.nn.relu)
+    avg_pool_1 = tf.layers.average_pooling1d(inputs=conv1, pool_size=5, strides=5, \
+                                         padding='same')
+    conv2 = tf.layers.conv1d(inputs=avg_pool_1, filters=8, kernel_size=6, strides=1,\
+                             padding='same', activation = tf.nn.relu)
+    avg_pool_2 = tf.layers.average_pooling1d(inputs=conv2, pool_size=5, strides=5,\
+                                         padding='same')
+    
+    flat = tf.reshape(avg_pool_2, (-1, int(250/5/5*8)))
+    
+    logits=tf.layers.dense(inputs=flat, units=n_class, activation=None)
+    logits=tf.nn.softmax(logits)
+    return logits
+
+#随机获取一个batch大小的数据，用于训练
+def get_batch(train_x,train_y,batch_size):
+    indices=np.random.choice(train_x.shape[0],batch_size,False)
+    batch_x=train_x[indices]
+    batch_y=train_y[indices]
+    return batch_x,batch_y
+
+#设定路径及文件名并载入，这里的心拍在Matlab下截取完成
+#详情：https://blog.csdn.net/qq_15746879/article/details/80340671
+Path='F:/Python files/ECGPrimer/' #自定义路径要正确
+DataFile='Data_CNN.mat'
+LabelFile='Label_OneHot.mat'
+
+print("Loading data and labels...")
+tic=time.time()
+Data=load_mat(Path+DataFile,'Data')
+Label=load_mat(Path+LabelFile,'Label')
+Data=Data.T
+Indices=np.arange(Data.shape[0]) #随机打乱索引并切分训练集与测试集
+np.random.shuffle(Indices)
+
+print("Divide training and testing set...")
+train_x=Data[Indices[:10000]]
+train_y=Label[Indices[:10000]]
+test_x=Data[Indices[10000:]]
+test_y=Label[Indices[10000:]]
+toc=time.time()
+print("Elapsed time is %f sec."%(toc-tic))
+print("======================================")
+
+print("1D-CNN setup and initialize...")
+tic=time.time()
+x=tf.placeholder(tf.float32, [None, 250]) #定义placeholder数据入口
+x_=tf.reshape(x,[-1,250,1])
+y_=tf.placeholder(tf.float32,[None,4])
+
+logits=CNNnet(x_,4)
+
+learning_rate=0.01
+batch_size=16
+maxiters=15000
+
+cost=tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits,labels=y_))
+#这里使用了自适应学习率的Adam训练方法，可以认为是SGD的高级演化版本之一
+train_step=tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost) 
+tf.global_variables_initializer().run()
+toc=time.time()
+print("Elapsed time is %f sec."%(toc-tic))
+print("======================================")
+
+print("1D-CNN training and testing...")
+tic=time.time()
+for i in range(maxiters):
+    batch_x,batch_y=get_batch(train_x,train_y,batch_size)
+    train_step.run(feed_dict={x:batch_x,y_:batch_y})
+    if i%500==0:
+        loss=cost.eval(feed_dict={x:train_x,y_:train_y})
+        print("Iteration %d/%d:loss %f"%(i,maxiters,loss))
+
+y_pred=logits.eval(feed_dict={x:test_x,y_:test_y})
+y_pred=np.argmax(y_pred,axis=1)
+y_true=np.argmax(test_y,axis=1)
+toc=time.time()
+print("Elapsed time is %f sec."%(toc-tic))
+
+Acc=np.mean(y_pred==y_true)
+Conf_Mat=confusion_matrix(y_true,y_pred) #利用专用函数得到混淆矩阵
+Acc_N=Conf_Mat[0][0]/np.sum(Conf_Mat[0])
+Acc_V=Conf_Mat[1][1]/np.sum(Conf_Mat[1])
+Acc_R=Conf_Mat[2][2]/np.sum(Conf_Mat[2])
+Acc_L=Conf_Mat[3][3]/np.sum(Conf_Mat[3])
+
+
+print('\nAccuracy=%.2f%%'%(Acc*100))
+print('Accuracy_N=%.2f%%'%(Acc_N*100))
+print('Accuracy_V=%.2f%%'%(Acc_V*100))
+print('Accuracy_R=%.2f%%'%(Acc_R*100))
+print('Accuracy_L=%.2f%%'%(Acc_L*100))
+print('\nConfusion Matrix:\n')
+print(Conf_Mat)
+print("======================================")

Classification-WT_ML.py

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+# -*- coding: utf-8 -*-
+"""
+Created on Thu May 17 18:13:04 2018
+
+@author: Winham
+
+===========基于小波系数特征和支持向量机SVM的ECG分类算法=============
+
+*需要第三方工具包numpy,h5py,scikit-learn
+*涉及函数的使用方法可自行查看工具包文档，baidu即可得
+*ECG算法入门系列博客：https://blog.csdn.net/qq_15746879
+*开源github：https://github.com/Aiwiscal
+
+*本代码所需要的特征和标签文件来自matlab提取
+*详情：https://blog.csdn.net/qq_15746879/article/details/80340910
+================================================================
+"""
+#载入所需工具包
+import time
+import numpy as np
+import h5py as hp
+from sklearn.svm import SVC
+from sklearn.model_selection import train_test_split
+from sklearn import preprocessing
+from sklearn.metrics import confusion_matrix
+
+#载入.mat文件的函数,h5py解码并转换为numpy数组
+def load_mat(path_data,name_data,dtype='float32'):
+    data=hp.File(path_data)
+    arrays_d={}
+    for k,v in data.items():
+        arrays_d[k]=np.array(v)
+    dataArr=np.array(arrays_d[name_data],dtype=dtype)
+    return dataArr
+
+#设定路径及文件名并载入，这里的特征来自Matlab中提取的小波系数
+#https://blog.csdn.net/qq_15746879/article/details/80340910
+
+print("Loading Features and Labels...")
+Path='F:/Python files/ECGPrimer/' #自定义路径要正确
+FeatureFile='Feature_WT_25.mat'
+LabelFile='Label.mat'
+
+tic=time.time()
+Feature=load_mat(Path+FeatureFile,'Feature')
+Feature=Feature.T
+Label=load_mat(Path+LabelFile,'Label')
+Label=Label.squeeze()
+toc=time.time()
+
+print("Elapsed time is %f sec."%(toc-tic))
+print("======================================")
+
+#划分数据集并归一化
+print("Data normalizing...")
+tic=time.time()
+train_x,test_x,train_y,test_y=train_test_split(Feature,Label, test_size=0.5, random_state=42)
+min_max_scaler=preprocessing.MinMaxScaler() 
+train_x=min_max_scaler.fit_transform(train_x)
+test_x= min_max_scaler.transform(test_x)
+toc=time.time()
+print("Elapsed time is %f sec."%(toc-tic))
+print("======================================")
+
+#模型训练及预测
+print("SVM training and testing...")
+tic=time.time()
+SVM=SVC(kernel='rbf',C=2,gamma=1)
+SVM.fit(train_x,train_y)
+y_pred=SVM.predict(test_x)
+toc=time.time()
+print("Elapsed time is %f sec."%(toc-tic))
+
+#评估结果
+Acc=np.mean(y_pred==test_y)
+Conf_Mat=confusion_matrix(test_y,y_pred) #利用专用函数得到混淆矩阵
+Acc_N=Conf_Mat[0][0]/np.sum(Conf_Mat[0])
+Acc_V=Conf_Mat[1][1]/np.sum(Conf_Mat[1])
+Acc_R=Conf_Mat[2][2]/np.sum(Conf_Mat[2])
+Acc_L=Conf_Mat[3][3]/np.sum(Conf_Mat[3])
+
+
+print('\nAccuracy=%.2f%%'%(Acc*100))
+print('Accuracy_N=%.2f%%'%(Acc_N*100))
+print('Accuracy_V=%.2f%%'%(Acc_V*100))
+print('Accuracy_R=%.2f%%'%(Acc_R*100))
+print('Accuracy_L=%.2f%%'%(Acc_L*100))
+print('\nConfusion Matrix:\n')
+print(Conf_Mat)
+
+print("======================================")