├── Classification-DL_BiLSTM.py ├── Classification-DL_CNN.py ├── Classification-DL_ULSTM.py ├── Classification-WT_ML.py ├── Data_CNN.mat ├── Feature_WT_25.mat ├── LICENSE ├── Label.mat ├── Label_OneHot.mat ├── README.md ├── correct_eval_v1.0.py ├── ecg_records_103.txt ├── ecg_records_117.txt ├── ecg_records_119.txt ├── extract_beats_v1.0.py └── rpeak_seg_simple_v1.0.py /Classification-DL_BiLSTM.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """ 3 | Created on Sat Jun 9 13:35:50 2018 4 | 5 | @author: Administrator 6 | """ 7 | 8 | """ 9 | ===================基于双向LSTM(BiLSTM)的ECG分类算法================ 10 | 11 | *需要第三方工具包numpy,h5py,scikit-learn 12 | *基于深度学习框架TensorFlow 13 | *涉及函数的使用方法可自行查看工具包文档，baidu即可得 14 | *ECG算法入门系列博客：https://blog.csdn.net/qq_15746879 15 | *开源github：https://github.com/Aiwiscal 16 | 17 | *本代码所需要的数据和标签文件来自matlab提取 18 | ================================================================== 19 | """ 20 | #载入所需工具包 21 | import time 22 | import numpy as np 23 | import h5py as hp 24 | import tensorflow as tf 25 | from sklearn.metrics import confusion_matrix 26 | 27 | sess=tf.InteractiveSession() 28 | 29 | #载入.mat文件的函数,h5py解码并转换为numpy数组 30 | def load_mat(path_data,name_data,dtype='float32'): 31 | data=hp.File(path_data) 32 | arrays_d={} 33 | for k,v in data.items(): 34 | arrays_d[k]=np.array(v) 35 | dataArr=np.array(arrays_d[name_data],dtype=dtype) 36 | return dataArr 37 | 38 | #使用TensorFlow组件完成BiLSTM网络的搭建 39 | def BiLSTM(x,n_input,n_hidden,n_steps,n_classes): 40 | 41 | x=tf.transpose(x,[1,0,2]) #整理数据，使之符合ULSTM接口要求 42 | x=tf.reshape(x,[-1,n_input]) 43 | x=tf.split(x,n_steps) 44 | 45 | #以下两句调用TF函数，生成一个单隐层的BiLSTM 46 | lstm_fw_cell=tf.contrib.rnn.BasicLSTMCell(n_hidden,forget_bias=1.0) 47 | lstm_bw_cell=tf.contrib.rnn.BasicLSTMCell(n_hidden,forget_bias=1.0) 48 | 49 | outputs,_,_=tf.contrib.rnn.static_bidirectional_rnn(lstm_fw_cell, 50 | lstm_bw_cell, 51 | x,dtype=tf.float32) 52 | 53 | #以下部分将BiLSTM每一步的输出拼接，形成特征向量 54 | for i in range(n_steps): 55 | if i==0: 56 | fv=outputs[0] 57 | else: 58 | fv=tf.concat([fv,outputs[i]],1) 59 | fvp=tf.reshape(fv,[-1,1,2*n_steps*n_hidden,1]) 60 | shp=fvp.get_shape() 61 | flatten_shape=shp[1].value*shp[2].value*shp[3].value 62 | 63 | fvp2=tf.reshape(fvp,[-1,flatten_shape]) 64 | 65 | #构建最后的全连接层 66 | weights=tf.Variable(tf.random_normal([flatten_shape,n_classes])) 67 | biases=tf.Variable(tf.random_normal([n_classes])) 68 | 69 | return tf.matmul(fvp2,weights)+biases 70 | 71 | #随机获取一个batch大小的数据，用于训练 72 | def get_batch(train_x,train_y,batch_size): 73 | indices=np.random.choice(train_x.shape[0],batch_size,False) 74 | batch_x=train_x[indices] 75 | batch_y=train_y[indices] 76 | return batch_x,batch_y 77 | 78 | #设定路径及文件名并载入，这里的心拍在Matlab下截取完成 79 | #详情：https://blog.csdn.net/qq_15746879/article/details/80340671 80 | Path='F:/Python files/ECGPrimer/' #自定义路径要正确 81 | DataFile='Data_CNN.mat' 82 | LabelFile='Label_OneHot.mat' 83 | 84 | print("Loading data and labels...") 85 | tic=time.time() 86 | Data=load_mat(Path+DataFile,'Data') 87 | Label=load_mat(Path+LabelFile,'Label') 88 | Data=Data.T 89 | Indices=np.arange(Data.shape[0]) #随机打乱索引并切分训练集与测试集 90 | np.random.shuffle(Indices) 91 | 92 | print("Divide training and testing set...") 93 | train_x=Data[Indices[:10000]] 94 | train_y=Label[Indices[:10000]] 95 | test_x=Data[Indices[10000:]] 96 | test_y=Label[Indices[10000:]] 97 | toc=time.time() 98 | print("Elapsed time is %f sec."%(toc-tic)) 99 | print("======================================") 100 | 101 | print("BiLSTM setup and initialize...") 102 | 103 | n_input=1 104 | n_hidden=1 105 | n_steps=250 106 | n_classes=4 107 | tic=time.time() 108 | x=tf.placeholder(tf.float32, [None, 250]) #定义placeholder数据入口 109 | x_=tf.reshape(x,[-1,250,1]) 110 | y_=tf.placeholder(tf.float32,[None,4]) 111 | 112 | logits=BiLSTM(x_,n_input,n_hidden,n_steps,n_classes) 113 | 114 | learning_rate=0.001 115 | batch_size=16 116 | maxiters=10000 117 | 118 | cost=tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits,labels=y_)) 119 | #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 120 | train_step=tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost) 121 | tf.global_variables_initializer().run() 122 | toc=time.time() 123 | print("Elapsed time is %f sec."%(toc-tic)) 124 | print("======================================") 125 | 126 | print("ULSTM training and testing...") 127 | tic=time.time() 128 | for i in range(maxiters): 129 | batch_x,batch_y=get_batch(train_x,train_y,batch_size) 130 | train_step.run(feed_dict={x:batch_x,y_:batch_y}) 131 | if i%500==0: 132 | loss=cost.eval(feed_dict={x:train_x,y_:train_y}) 133 | print("Iteration %d/%d:loss %f"%(i,maxiters,loss)) 134 | 135 | y_pred=logits.eval(feed_dict={x:test_x,y_:test_y}) 136 | y_pred=np.argmax(y_pred,axis=1) 137 | y_true=np.argmax(test_y,axis=1) 138 | toc=time.time() 139 | print("Elapsed time is %f sec."%(toc-tic)) 140 | 141 | Acc=np.mean(y_pred==y_true) 142 | Conf_Mat=confusion_matrix(y_true,y_pred) #利用专用函数得到混淆矩阵 143 | Acc_N=Conf_Mat[0][0]/np.sum(Conf_Mat[0]) 144 | Acc_V=Conf_Mat[1][1]/np.sum(Conf_Mat[1]) 145 | Acc_R=Conf_Mat[2][2]/np.sum(Conf_Mat[2]) 146 | Acc_L=Conf_Mat[3][3]/np.sum(Conf_Mat[3]) 147 | 148 | 149 | print('\nAccuracy=%.2f%%'%(Acc*100)) 150 | print('Accuracy_N=%.2f%%'%(Acc_N*100)) 151 | print('Accuracy_V=%.2f%%'%(Acc_V*100)) 152 | print('Accuracy_R=%.2f%%'%(Acc_R*100)) 153 | print('Accuracy_L=%.2f%%'%(Acc_L*100)) 154 | print('\nConfusion Matrix:\n') 155 | print(Conf_Mat) 156 | print("======================================") -------------------------------------------------------------------------------- /Classification-DL_CNN.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """ 3 | Created on Thu May 17 21:01:53 2018 4 | 5 | @author: Winham 6 | 7 | ===================基于1维CNN的ECG分类算法======================== 8 | 9 | *需要第三方工具包numpy,h5py,scikit-learn 10 | *基于深度学习框架TensorFlow 11 | *涉及函数的使用方法可自行查看工具包文档，baidu即可得 12 | *ECG算法入门系列博客：https://blog.csdn.net/qq_15746879 13 | *开源github：https://github.com/Aiwiscal 14 | 15 | *本代码所需要的数据和标签文件来自matlab提取 16 | *详情：https://blog.csdn.net/qq_15746879/article/details/80340958 17 | ================================================================== 18 | """ 19 | #载入所需工具包 20 | import time 21 | import numpy as np 22 | import h5py as hp 23 | import tensorflow as tf 24 | from sklearn.metrics import confusion_matrix 25 | 26 | sess=tf.InteractiveSession() 27 | 28 | #载入.mat文件的函数,h5py解码并转换为numpy数组 29 | def load_mat(path_data,name_data,dtype='float32'): 30 | data=hp.File(path_data) 31 | arrays_d={} 32 | for k,v in data.items(): 33 | arrays_d[k]=np.array(v) 34 | dataArr=np.array(arrays_d[name_data],dtype=dtype) 35 | return dataArr 36 | 37 | #使用TensorFlow组件完成CNN网络的搭建，与教程中参数略有不同 38 | def CNNnet(inputs,n_class): 39 | conv1 = tf.layers.conv1d(inputs=inputs, filters=4, kernel_size=31, strides=1, \ 40 | padding='same', activation = tf.nn.relu) 41 | avg_pool_1 = tf.layers.average_pooling1d(inputs=conv1, pool_size=5, strides=5, \ 42 | padding='same') 43 | conv2 = tf.layers.conv1d(inputs=avg_pool_1, filters=8, kernel_size=6, strides=1,\ 44 | padding='same', activation = tf.nn.relu) 45 | avg_pool_2 = tf.layers.average_pooling1d(inputs=conv2, pool_size=5, strides=5,\ 46 | padding='same') 47 | 48 | flat = tf.reshape(avg_pool_2, (-1, int(250/5/5*8))) 49 | 50 | logits=tf.layers.dense(inputs=flat, units=n_class, activation=None) 51 | logits=tf.nn.softmax(logits) 52 | return logits 53 | 54 | #随机获取一个batch大小的数据，用于训练 55 | def get_batch(train_x,train_y,batch_size): 56 | indices=np.random.choice(train_x.shape[0],batch_size,False) 57 | batch_x=train_x[indices] 58 | batch_y=train_y[indices] 59 | return batch_x,batch_y 60 | 61 | #设定路径及文件名并载入，这里的心拍在Matlab下截取完成 62 | #详情：https://blog.csdn.net/qq_15746879/article/details/80340671 63 | Path='F:/Python files/ECGPrimer/' #自定义路径要正确 64 | DataFile='Data_CNN.mat' 65 | LabelFile='Label_OneHot.mat' 66 | 67 | print("Loading data and labels...") 68 | tic=time.time() 69 | Data=load_mat(Path+DataFile,'Data') 70 | Label=load_mat(Path+LabelFile,'Label') 71 | Data=Data.T 72 | Indices=np.arange(Data.shape[0]) #随机打乱索引并切分训练集与测试集 73 | np.random.shuffle(Indices) 74 | 75 | print("Divide training and testing set...") 76 | train_x=Data[Indices[:10000]] 77 | train_y=Label[Indices[:10000]] 78 | test_x=Data[Indices[10000:]] 79 | test_y=Label[Indices[10000:]] 80 | toc=time.time() 81 | print("Elapsed time is %f sec."%(toc-tic)) 82 | print("======================================") 83 | 84 | print("1D-CNN setup and initialize...") 85 | tic=time.time() 86 | x=tf.placeholder(tf.float32, [None, 250]) #定义placeholder数据入口 87 | x_=tf.reshape(x,[-1,250,1]) 88 | y_=tf.placeholder(tf.float32,[None,4]) 89 | 90 | logits=CNNnet(x_,4) 91 | 92 | learning_rate=0.01 93 | batch_size=16 94 | maxiters=15000 95 | 96 | cost=tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits,labels=y_)) 97 | #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 98 | train_step=tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost) 99 | tf.global_variables_initializer().run() 100 | toc=time.time() 101 | print("Elapsed time is %f sec."%(toc-tic)) 102 | print("======================================") 103 | 104 | print("1D-CNN training and testing...") 105 | tic=time.time() 106 | for i in range(maxiters): 107 | batch_x,batch_y=get_batch(train_x,train_y,batch_size) 108 | train_step.run(feed_dict={x:batch_x,y_:batch_y}) 109 | if i%500==0: 110 | loss=cost.eval(feed_dict={x:train_x,y_:train_y}) 111 | print("Iteration %d/%d:loss %f"%(i,maxiters,loss)) 112 | 113 | y_pred=logits.eval(feed_dict={x:test_x,y_:test_y}) 114 | y_pred=np.argmax(y_pred,axis=1) 115 | y_true=np.argmax(test_y,axis=1) 116 | toc=time.time() 117 | print("Elapsed time is %f sec."%(toc-tic)) 118 | 119 | Acc=np.mean(y_pred==y_true) 120 | Conf_Mat=confusion_matrix(y_true,y_pred) #利用专用函数得到混淆矩阵 121 | Acc_N=Conf_Mat[0][0]/np.sum(Conf_Mat[0]) 122 | Acc_V=Conf_Mat[1][1]/np.sum(Conf_Mat[1]) 123 | Acc_R=Conf_Mat[2][2]/np.sum(Conf_Mat[2]) 124 | Acc_L=Conf_Mat[3][3]/np.sum(Conf_Mat[3]) 125 | 126 | 127 | print('\nAccuracy=%.2f%%'%(Acc*100)) 128 | print('Accuracy_N=%.2f%%'%(Acc_N*100)) 129 | print('Accuracy_V=%.2f%%'%(Acc_V*100)) 130 | print('Accuracy_R=%.2f%%'%(Acc_R*100)) 131 | print('Accuracy_L=%.2f%%'%(Acc_L*100)) 132 | print('\nConfusion Matrix:\n') 133 | print(Conf_Mat) 134 | print("======================================") -------------------------------------------------------------------------------- /Classification-DL_ULSTM.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """ 3 | Created on Sat Jun 9 12:53:14 2018 4 | 5 | @author: Winham 6 | """ 7 | """ 8 | ===================基于单向LSTM(ULSTM)的ECG分类算法======================== 9 | 10 | *需要第三方工具包numpy,h5py,scikit-learn 11 | *基于深度学习框架TensorFlow 12 | *涉及函数的使用方法可自行查看工具包文档，baidu即可得 13 | *ECG算法入门系列博客：https://blog.csdn.net/qq_15746879 14 | *开源github：https://github.com/Aiwiscal 15 | 16 | *本代码所需要的数据和标签文件来自matlab提取 17 | ================================================================== 18 | """ 19 | #载入所需工具包 20 | import time 21 | import numpy as np 22 | import h5py as hp 23 | import tensorflow as tf 24 | from sklearn.metrics import confusion_matrix 25 | 26 | sess=tf.InteractiveSession() 27 | 28 | #载入.mat文件的函数,h5py解码并转换为numpy数组 29 | def load_mat(path_data,name_data,dtype='float32'): 30 | data=hp.File(path_data) 31 | arrays_d={} 32 | for k,v in data.items(): 33 | arrays_d[k]=np.array(v) 34 | dataArr=np.array(arrays_d[name_data],dtype=dtype) 35 | return dataArr 36 | 37 | #使用TensorFlow组件完成ULSTM网络的搭建 38 | def ULSTM(x,n_input,n_hidden,n_steps,n_classes): 39 | 40 | x=tf.transpose(x,[1,0,2]) #整理数据，使之符合ULSTM接口要求 41 | x=tf.reshape(x,[-1,n_input]) 42 | x=tf.split(x,n_steps) 43 | 44 | #以下两句调用TF函数，生成一个单隐层的ULSTM 45 | lstm_cell=tf.contrib.rnn.BasicLSTMCell(n_hidden,forget_bias=1.0) 46 | outputs,_=tf.contrib.rnn.static_rnn(lstm_cell,x,dtype=tf.float32) 47 | 48 | #以下部分将ULSTM每一步的输出拼接，形成特征向量 49 | for i in range(n_steps): 50 | if i==0: 51 | fv=outputs[0] 52 | else: 53 | fv=tf.concat([fv,outputs[i]],1) 54 | fvp=tf.reshape(fv,[-1,1,n_steps*n_hidden,1]) 55 | shp=fvp.get_shape() 56 | flatten_shape=shp[1].value*shp[2].value*shp[3].value 57 | 58 | fvp2=tf.reshape(fvp,[-1,flatten_shape]) 59 | 60 | #构建最后的全连接层 61 | weights=tf.Variable(tf.random_normal([flatten_shape,n_classes])) 62 | biases=tf.Variable(tf.random_normal([n_classes])) 63 | 64 | return tf.matmul(fvp2,weights)+biases 65 | 66 | #随机获取一个batch大小的数据，用于训练 67 | def get_batch(train_x,train_y,batch_size): 68 | indices=np.random.choice(train_x.shape[0],batch_size,False) 69 | batch_x=train_x[indices] 70 | batch_y=train_y[indices] 71 | return batch_x,batch_y 72 | 73 | #设定路径及文件名并载入，这里的心拍在Matlab下截取完成 74 | #详情：https://blog.csdn.net/qq_15746879/article/details/80340671 75 | Path='F:/Python files/ECGPrimer/' #自定义路径要正确 76 | DataFile='Data_CNN.mat' 77 | LabelFile='Label_OneHot.mat' 78 | 79 | print("Loading data and labels...") 80 | tic=time.time() 81 | Data=load_mat(Path+DataFile,'Data') 82 | Label=load_mat(Path+LabelFile,'Label') 83 | Data=Data.T 84 | Indices=np.arange(Data.shape[0]) #随机打乱索引并切分训练集与测试集 85 | np.random.shuffle(Indices) 86 | 87 | print("Divide training and testing set...") 88 | train_x=Data[Indices[:10000]] 89 | train_y=Label[Indices[:10000]] 90 | test_x=Data[Indices[10000:]] 91 | test_y=Label[Indices[10000:]] 92 | toc=time.time() 93 | print("Elapsed time is %f sec."%(toc-tic)) 94 | print("======================================") 95 | 96 | print("ULSTM setup and initialize...") 97 | 98 | n_input=1 99 | n_hidden=1 100 | n_steps=250 101 | n_classes=4 102 | tic=time.time() 103 | x=tf.placeholder(tf.float32, [None, 250]) #定义placeholder数据入口 104 | x_=tf.reshape(x,[-1,250,1]) 105 | y_=tf.placeholder(tf.float32,[None,4]) 106 | 107 | logits=ULSTM(x_,n_input,n_hidden,n_steps,n_classes) 108 | 109 | learning_rate=0.001 110 | batch_size=16 111 | maxiters=10000 112 | 113 | cost=tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits,labels=y_)) 114 | #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 115 | train_step=tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost) 116 | tf.global_variables_initializer().run() 117 | toc=time.time() 118 | print("Elapsed time is %f sec."%(toc-tic)) 119 | print("======================================") 120 | 121 | print("ULSTM training and testing...") 122 | tic=time.time() 123 | for i in range(maxiters): 124 | batch_x,batch_y=get_batch(train_x,train_y,batch_size) 125 | train_step.run(feed_dict={x:batch_x,y_:batch_y}) 126 | if i%500==0: 127 | loss=cost.eval(feed_dict={x:train_x,y_:train_y}) 128 | print("Iteration %d/%d:loss %f"%(i,maxiters,loss)) 129 | 130 | y_pred=logits.eval(feed_dict={x:test_x,y_:test_y}) 131 | y_pred=np.argmax(y_pred,axis=1) 132 | y_true=np.argmax(test_y,axis=1) 133 | toc=time.time() 134 | print("Elapsed time is %f sec."%(toc-tic)) 135 | 136 | Acc=np.mean(y_pred==y_true) 137 | Conf_Mat=confusion_matrix(y_true,y_pred) #利用专用函数得到混淆矩阵 138 | Acc_N=Conf_Mat[0][0]/np.sum(Conf_Mat[0]) 139 | Acc_V=Conf_Mat[1][1]/np.sum(Conf_Mat[1]) 140 | Acc_R=Conf_Mat[2][2]/np.sum(Conf_Mat[2]) 141 | Acc_L=Conf_Mat[3][3]/np.sum(Conf_Mat[3]) 142 | 143 | 144 | print('\nAccuracy=%.2f%%'%(Acc*100)) 145 | print('Accuracy_N=%.2f%%'%(Acc_N*100)) 146 | print('Accuracy_V=%.2f%%'%(Acc_V*100)) 147 | print('Accuracy_R=%.2f%%'%(Acc_R*100)) 148 | print('Accuracy_L=%.2f%%'%(Acc_L*100)) 149 | print('\nConfusion Matrix:\n') 150 | print(Conf_Mat) 151 | print("======================================") -------------------------------------------------------------------------------- /Classification-WT_ML.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """ 3 | Created on Thu May 17 18:13:04 2018 4 | 5 | @author: Winham 6 | 7 | ===========基于小波系数特征和支持向量机SVM的ECG分类算法============= 8 | 9 | *需要第三方工具包numpy,h5py,scikit-learn 10 | *涉及函数的使用方法可自行查看工具包文档，baidu即可得 11 | *ECG算法入门系列博客：https://blog.csdn.net/qq_15746879 12 | *开源github：https://github.com/Aiwiscal 13 | 14 | *本代码所需要的特征和标签文件来自matlab提取 15 | *详情：https://blog.csdn.net/qq_15746879/article/details/80340910 16 | ================================================================ 17 | """ 18 | #载入所需工具包 19 | import time 20 | import numpy as np 21 | import h5py as hp 22 | from sklearn.svm import SVC 23 | from sklearn.model_selection import train_test_split 24 | from sklearn import preprocessing 25 | from sklearn.metrics import confusion_matrix 26 | 27 | #载入.mat文件的函数,h5py解码并转换为numpy数组 28 | def load_mat(path_data,name_data,dtype='float32'): 29 | data=hp.File(path_data) 30 | arrays_d={} 31 | for k,v in data.items(): 32 | arrays_d[k]=np.array(v) 33 | dataArr=np.array(arrays_d[name_data],dtype=dtype) 34 | return dataArr 35 | 36 | #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 37 | #https://blog.csdn.net/qq_15746879/article/details/80340910 38 | 39 | print("Loading Features and Labels...") 40 | Path='F:/Python files/ECGPrimer/' #自定义路径要正确 41 | FeatureFile='Feature_WT_25.mat' 42 | LabelFile='Label.mat' 43 | 44 | tic=time.time() 45 | Feature=load_mat(Path+FeatureFile,'Feature') 46 | Feature=Feature.T 47 | Label=load_mat(Path+LabelFile,'Label') 48 | Label=Label.squeeze() 49 | toc=time.time() 50 | 51 | print("Elapsed time is %f sec."%(toc-tic)) 52 | print("======================================") 53 | 54 | #划分数据集并归一化 55 | print("Data normalizing...") 56 | tic=time.time() 57 | train_x,test_x,train_y,test_y=train_test_split(Feature,Label, test_size=0.5, random_state=42) 58 | min_max_scaler=preprocessing.MinMaxScaler() 59 | train_x=min_max_scaler.fit_transform(train_x) 60 | test_x= min_max_scaler.transform(test_x) 61 | toc=time.time() 62 | print("Elapsed time is %f sec."%(toc-tic)) 63 | print("======================================") 64 | 65 | #模型训练及预测 66 | print("SVM training and testing...") 67 | tic=time.time() 68 | SVM=SVC(kernel='rbf',C=2,gamma=1) 69 | SVM.fit(train_x,train_y) 70 | y_pred=SVM.predict(test_x) 71 | toc=time.time() 72 | print("Elapsed time is %f sec."%(toc-tic)) 73 | 74 | #评估结果 75 | Acc=np.mean(y_pred==test_y) 76 | Conf_Mat=confusion_matrix(test_y,y_pred) #利用专用函数得到混淆矩阵 77 | Acc_N=Conf_Mat[0][0]/np.sum(Conf_Mat[0]) 78 | Acc_V=Conf_Mat[1][1]/np.sum(Conf_Mat[1]) 79 | Acc_R=Conf_Mat[2][2]/np.sum(Conf_Mat[2]) 80 | Acc_L=Conf_Mat[3][3]/np.sum(Conf_Mat[3]) 81 | 82 | 83 | print('\nAccuracy=%.2f%%'%(Acc*100)) 84 | print('Accuracy_N=%.2f%%'%(Acc_N*100)) 85 | print('Accuracy_V=%.2f%%'%(Acc_V*100)) 86 | print('Accuracy_R=%.2f%%'%(Acc_R*100)) 87 | print('Accuracy_L=%.2f%%'%(Acc_L*100)) 88 | print('\nConfusion Matrix:\n') 89 | print(Conf_Mat) 90 | 91 | print("======================================") -------------------------------------------------------------------------------- /Data_CNN.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Aiwiscal/ECG-ML-DL-Algorithm-Python/23b24a965fdede3552d33943a26ad824a8c03325/Data_CNN.mat -------------------------------------------------------------------------------- /Feature_WT_25.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Aiwiscal/ECG-ML-DL-Algorithm-Python/23b24a965fdede3552d33943a26ad824a8c03325/Feature_WT_25.mat -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | Apache License 2 | Version 2.0, January 2004 3 | http://www.apache.org/licenses/ 4 | 5 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 6 | 7 | 1. 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We also recommend that a 185 | file or class name and description of purpose be included on the 186 | same "printed page" as the copyright notice for easier 187 | identification within third-party archives. 188 | 189 | Copyright [yyyy] [name of copyright owner] 190 | 191 | Licensed under the Apache License, Version 2.0 (the "License"); 192 | you may not use this file except in compliance with the License. 193 | You may obtain a copy of the License at 194 | 195 | http://www.apache.org/licenses/LICENSE-2.0 196 | 197 | Unless required by applicable law or agreed to in writing, software 198 | distributed under the License is distributed on an "AS IS" BASIS, 199 | WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 200 | See the License for the specific language governing permissions and 201 | limitations under the License. 202 | -------------------------------------------------------------------------------- /Label.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Aiwiscal/ECG-ML-DL-Algorithm-Python/23b24a965fdede3552d33943a26ad824a8c03325/Label.mat -------------------------------------------------------------------------------- /Label_OneHot.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Aiwiscal/ECG-ML-DL-Algorithm-Python/23b24a965fdede3552d33943a26ad824a8c03325/Label_OneHot.mat -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # ECG-ML-DL-Algorithm-Python-version 2 | Basic Algorithm For Beginners(Python version) 3 | 4 | Detailed tutorials:https://blog.csdn.net/qq_15746879 5 | -------------------------------------------------------------------------------- /correct_eval_v1.0.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/python 2 | # -*- coding:utf-8 -*- 3 | 4 | import time 5 | import logging 6 | from biosppy.storage import load_txt 7 | from biosppy.signals import ecg 8 | import matplotlib.pyplot as plt 9 | 10 | logging.basicConfig(level=logging.DEBUG, 11 | format='%(asctime)s - %(name)s - %(levelname)s - %(message)s') 12 | logger = logging.getLogger(__name__) 13 | 14 | signal_path = "./data/ecg_records_117.txt" 15 | ann_path = "./data/ecg_ann_117.txt" 16 | 17 | logging.info("--------------------------------------------------") 18 | signal, _ = load_txt(signal_path) 19 | logging.info("载入信号-%s, 长度 = %d. " % (signal_path, len(signal))) 20 | ann, _ = load_txt(ann_path) 21 | logging.info("载入R峰位置人工标记, 共 %d 个R峰." % (len(ann))) 22 | 23 | fs = 360 # 信号采样率 360 Hz 24 | logging.info("调用 hamilton_segmenter 进行R波检测 ...") 25 | tic = time.time() 26 | rpeaks = ecg.hamilton_segmenter(signal, sampling_rate=fs) 27 | toc = time.time() 28 | logging.info("完成. 用时: %f 秒. " % (toc - tic)) 29 | rpeaks = rpeaks[0] 30 | 31 | logging.info("使用compare_segmentation对比算法结果与人工标记 ...") 32 | tic = time.time() 33 | eval_results = ecg.compare_segmentation(ann, rpeaks, fs, tol=0.02) 34 | toc = time.time() 35 | logging.info("完成. 用时: %f 秒. 返回结果类型为 %s ." % (toc - tic, str(type(eval_results)))) 36 | 37 | dict_results = eval_results.as_dict() 38 | logging.info("********** 结果报告 *************") 39 | logging.info("* 准确率(acc): %.3f *" % dict_results["acc"]) 40 | logging.info("* 总体表现(performance): %.3f *" % dict_results["performance"]) 41 | logging.info("*********************************") 42 | 43 | correct_tol = 0.05 44 | logging.info("使用correct_rpeaks校正R波位置, 最大校正范围 %.3f 秒 ..." % correct_tol) 45 | tic = time.time() 46 | rpeaks_correct = ecg.correct_rpeaks(signal, rpeaks, fs, tol=correct_tol) 47 | toc = time.time() 48 | logging.info("完成. 用时: %f 秒. 返回结果类型为 %s ." % (toc - tic, 49 | str(type(rpeaks_correct)))) 50 | rpeaks_correct = rpeaks_correct.as_dict()["rpeaks"] 51 | 52 | logging.info("绘制部分R峰校正前后的位置 ...") 53 | num_plot_samples = 3600 54 | sig_plot = signal[:num_plot_samples] 55 | rpeaks_plot = rpeaks[rpeaks <= num_plot_samples] 56 | rpeaks_correct_plot = rpeaks_correct[rpeaks_correct <= num_plot_samples] 57 | plt.figure() 58 | plt.grid(True) 59 | plt.plot(sig_plot, label="ECG") 60 | plt.plot(rpeaks_plot, sig_plot[rpeaks_plot], "ro", label="rpeaks") 61 | plt.plot(rpeaks_correct_plot, sig_plot[rpeaks_correct_plot], "b*", label="corrected rpeaks") 62 | plt.legend() 63 | plt.show() 64 | logging.info("绘图完成.") 65 | 66 | logging.info("使用biosppy.signals.ecg.ecg 综合处理 ...") 67 | tic = time.time() 68 | summary_result = ecg.ecg(sig_plot, fs, True) 69 | toc = time.time() 70 | logging.info("完成. 用时: %f 秒. 返回结果类型为 %s ." % (toc - tic, 71 | str(type(summary_result)))) 72 | 73 | summary_result = summary_result.as_dict() 74 | -------------------------------------------------------------------------------- /ecg_records_103.txt: 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-0.515 10587 | -0.55 10588 | -0.525 10589 | -0.46 10590 | -0.39 10591 | -0.27 10592 | -0.07 10593 | 0.22 10594 | 0.6 10595 | 0.965 10596 | 1.305 10597 | 1.605 10598 | 1.815 10599 | 1.885 10600 | 1.82 10601 | 1.56 10602 | 1.095 10603 | 0.53 10604 | 0.02 10605 | -0.31 10606 | -0.46 10607 | -0.455 10608 | -0.415 10609 | -0.35 10610 | -0.29 10611 | -0.245 10612 | -0.24 10613 | -0.27 10614 | -0.285 10615 | -0.305 10616 | -0.315 10617 | -0.335 10618 | -0.335 10619 | -0.34 10620 | -0.34 10621 | -0.36 10622 | -0.38 10623 | -0.405 10624 | -0.39 10625 | -0.4 10626 | -0.385 10627 | -0.38 10628 | -0.385 10629 | -0.395 10630 | -0.395 10631 | -0.39 10632 | -0.39 10633 | -0.395 10634 | -0.39 10635 | -0.415 10636 | -0.405 10637 | -0.405 10638 | -0.39 10639 | -0.39 10640 | -0.4 10641 | -0.4 10642 | -0.405 10643 | -0.405 10644 | -0.4 10645 | -0.39 10646 | -0.39 10647 | -0.4 10648 | -0.4 10649 | -0.38 10650 | -0.38 10651 | -0.39 10652 | -0.385 10653 | -0.385 10654 | -0.37 10655 | -0.365 10656 | -0.345 10657 | -0.35 10658 | -0.345 10659 | -0.35 10660 | -0.335 10661 | -0.335 10662 | -0.32 10663 | -0.325 10664 | -0.305 10665 | -0.315 10666 | -0.31 10667 | -0.285 10668 | -0.28 10669 | -0.27 10670 | -0.25 10671 | -0.245 10672 | -0.23 10673 | -0.205 10674 | -0.18 10675 | -0.175 10676 | -0.18 10677 | -0.16 10678 | -0.145 10679 | -0.12 10680 | -0.09 10681 | -0.07 10682 | -0.065 10683 | -0.05 10684 | -0.015 10685 | 0.005 10686 | 0.02 10687 | 0.035 10688 | 0.045 10689 | 0.045 10690 | 0.065 10691 | 0.085 10692 | 0.11 10693 | 0.115 10694 | 0.12 10695 | 0.115 10696 | 0.135 10697 | 0.15 10698 | 0.155 10699 | 0.145 10700 | 0.15 10701 | 0.15 10702 | 0.155 10703 | 0.16 10704 | 0.16 10705 | 0.15 10706 | 0.125 10707 | 0.12 10708 | 0.085 10709 | 0.08 10710 | 0.06 10711 | 0.025 10712 | 0.0 10713 | -0.025 10714 | -0.045 10715 | -0.055 10716 | -0.07 10717 | -0.11 10718 | -0.12 10719 | -0.15 10720 | -0.17 10721 | -0.19 10722 | -0.195 10723 | -0.21 10724 | -0.21 10725 | -0.215 10726 | -0.22 10727 | -0.23 10728 | -0.235 10729 | -0.225 10730 | -0.235 10731 | -0.255 10732 | -0.245 10733 | -0.25 10734 | -0.24 10735 | -0.265 10736 | -0.265 10737 | -0.28 10738 | -0.27 10739 | -0.255 10740 | -0.245 10741 | -0.24 10742 | -0.25 10743 | -0.26 10744 | -0.26 10745 | -0.245 10746 | -0.24 10747 | -0.23 10748 | -0.245 10749 | -0.245 10750 | -0.24 10751 | -0.23 10752 | -0.225 10753 | -0.225 10754 | -0.22 10755 | -0.225 10756 | -0.22 10757 | -0.215 10758 | -0.21 10759 | -0.22 10760 | -0.225 10761 | -0.23 10762 | -0.23 10763 | -0.225 10764 | -0.225 10765 | -0.22 10766 | -0.23 10767 | -0.235 10768 | -0.22 10769 | -0.215 10770 | -0.21 10771 | -0.21 10772 | -0.22 10773 | -0.23 10774 | -0.23 10775 | -0.225 10776 | -0.215 10777 | -0.22 10778 | -0.23 10779 | -0.245 10780 | -0.24 10781 | -0.245 10782 | -0.23 10783 | -0.235 10784 | -0.245 10785 | -0.255 10786 | -0.255 10787 | -0.235 10788 | -0.23 10789 | -0.245 10790 | -0.26 10791 | -0.27 10792 | -0.27 10793 | -0.265 10794 | -0.27 10795 | -0.26 10796 | -0.27 10797 | -0.29 10798 | -0.275 10799 | -0.27 10800 | -0.285 10801 | -------------------------------------------------------------------------------- /extract_beats_v1.0.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/python 2 | # -*- coding:utf-8 -*- 3 | 4 | import time 5 | import logging 6 | import numpy as np 7 | from biosppy.signals import ecg 8 | from biosppy.storage import load_txt 9 | import matplotlib.pyplot as plt 10 | 11 | logging.basicConfig(level=logging.DEBUG, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s') 12 | logger = logging.getLogger(__name__) 13 | 14 | def test_extract_beats(data_path): 15 | signal, mdata = load_txt(data_path) 16 | logging.info("--------------------------------------------------") 17 | logging.info("载入信号-%s, 长度 = %d " % (data_path, len(signal))) 18 | fs = 360 # 信号采样率 360 Hz 19 | logging.info("调用 hamilton_segmenter 进行R波检测 ...") 20 | tic = time.time() 21 | rpeaks = ecg.hamilton_segmenter(signal, sampling_rate=fs) 22 | toc = time.time() 23 | logging.info("完成. 用时: %f 秒. " % (toc - tic)) 24 | rpeaks = rpeaks[0] 25 | 26 | heart_rate = 60 / (np.mean(np.diff(rpeaks)) / fs) 27 | # np.diff 计算相邻R峰之间的距离分别有多少个采样点，np.mean求平均后，除以采样率， 28 | # 将单位转化为秒，然后计算60秒内可以有多少个RR间期作为心率 29 | logging.info("平均心率: %.3f / 分钟." % (heart_rate)) 30 | 31 | win_before = 0.2 32 | win_after = 0.4 33 | logging.info("根据R波位置截取心拍, 心拍前窗口：%.3f 秒 ~ 心拍后窗口：%.3f 秒 ..." \ 34 | % (win_before, win_after)) 35 | tic = time.time() 36 | beats, rpeaks_beats = ecg.extract_heartbeats(signal, rpeaks, fs, win_before, win_after) 37 | toc = time.time() 38 | logging.info("完成. 用时: %f 秒." % (toc - tic)) 39 | logging.info("共截取到 %d 个心拍, 每个心拍长度为 %d 个采样点" % \ 40 | (beats.shape[0], beats.shape[1])) 41 | 42 | plt.figure() 43 | plt.grid(True) 44 | for i in range(beats.shape[0]): 45 | plt.plot(beats[i]) 46 | plt.title(data_path) 47 | plt.show() 48 | return 49 | 50 | if __name__ == '__main__': 51 | test_extract_beats("./data/ecg_records_103.txt") 52 | test_extract_beats("./data/ecg_records_117.txt") 53 | test_extract_beats("./data/ecg_records_119.txt") 54 | 55 | -------------------------------------------------------------------------------- /rpeak_seg_simple_v1.0.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/python 2 | # -*- coding:utf-8 -*- 3 | 4 | import sys 5 | import time 6 | import logging 7 | import numpy as np 8 | from biosppy.signals import ecg 9 | from biosppy.storage import load_txt 10 | import matplotlib.pyplot as plt 11 | 12 | logging.basicConfig(level = logging.DEBUG,format = '%(asctime)s - %(name)s - %(levelname)s - %(message)s') 13 | logger = logging.getLogger(__name__) 14 | 15 | def test_rpeaks_simple(data_path): 16 | signal, mdata = load_txt(data_path) 17 | logging.info("--------------------------------------------------") 18 | logging.info("载入信号-%s, 长度 = %d " % (data_path, len(signal))) 19 | fs = 360 # 信号采样率 360 Hz 20 | logging.info("调用 christov_segmenter 进行R波检测 ...") 21 | tic = time.time() 22 | rpeaks = ecg.christov_segmenter(signal, sampling_rate=fs) 23 | toc = time.time() 24 | logging.info("完成. 用时: %f 秒. " % (toc - tic)) 25 | # 以上这种方式返回的rpeaks类型为biosppy.utils.ReturnTuple, biosppy的内置类 26 | logging.info("直接调用 christov_segmenter 返回类型为 " + str(type(rpeaks))) 27 | 28 | # 得到R波位置序列的方法： 29 | # 1) 取返回值的第1项： 30 | logging.info("使用第1种方式取R波位置序列 ... ") 31 | rpeaks_indices_1 = rpeaks[0] 32 | logging.info("完成. 结果类型为 " + str(type(rpeaks_indices_1))) 33 | # 2) 调用ReturnTuple的as_dict()方法，得到Python有序字典（OrderedDict）类型 34 | logging.info("使用第2种方式取R波位置序列 ... ") 35 | rpeaks_indices_2 = rpeaks.as_dict() 36 | # 然后使用说明文档中的参数名（这里是rpeaks）作为key取值。 37 | rpeaks_indices_2 = rpeaks_indices_2["rpeaks"] 38 | logging.info("完成. 结果类型为 " + str(type(rpeaks_indices_2))) 39 | 40 | # 检验两种方法得到的结果是否相同： 41 | check_sum = np.sum(rpeaks_indices_1 == rpeaks_indices_2) 42 | if check_sum == len(rpeaks_indices_1): 43 | logging.info("两种取值方式结果相同 ... ") 44 | else: 45 | logging.info("两种取值方式结果不同，退出 ...") 46 | sys.exit(1) 47 | 48 | # 与 christov_segmenter 接口一致的还有 hamilton_segmenter 49 | logging.info("调用接口一致的 hamilton_segmenter 进行R波检测") 50 | tic = time.time() 51 | rpeaks = ecg.hamilton_segmenter(signal, sampling_rate=fs) 52 | toc = time.time() 53 | logging.info("完成. 用时: %f 秒. " % (toc - tic)) 54 | rpeaks_indices_3 = rpeaks.as_dict()["rpeaks"] 55 | # 绘波形图和R波位置 56 | num_plot_samples = 3600 57 | logging.info("绘制波形图和检测的R波位置 ...") 58 | sig_plot = signal[:num_plot_samples] 59 | rpeaks_plot_1 = rpeaks_indices_1[rpeaks_indices_1 <= num_plot_samples] 60 | plt.figure() 61 | plt.plot(sig_plot, "g", label="ECG") 62 | plt.grid(True) 63 | plt.plot(rpeaks_plot_1, sig_plot[rpeaks_plot_1], "ro", label="christov_segmenter") 64 | rpeaks_plot_3 = rpeaks_indices_3[rpeaks_indices_3 <= num_plot_samples] 65 | plt.plot(rpeaks_plot_3, sig_plot[rpeaks_plot_3], "b^", label="hamilton_segmenter") 66 | plt.legend() 67 | plt.title(data_path) 68 | plt.show() 69 | logging.info("完成.") 70 | return 71 | 72 | 73 | if __name__ == '__main__': 74 | test_rpeaks_simple("./data/ecg_records_117.txt") 75 | test_rpeaks_simple("./data/ecg_records_103.txt") 76 | test_rpeaks_simple("./data/ecg_records_119.txt") 77 | --------------------------------------------------------------------------------