--- a
+++ b/Conv1D_ECG.py
@@ -0,0 +1,115 @@
+# source:https://github.com/ismorphism/DeepECG
+# 2019/11/25    YANG Jie    小修改
+from sklearn.metrics import confusion_matrix, accuracy_score
+from keras.callbacks import ModelCheckpoint
+from sklearn.preprocessing import MinMaxScaler, RobustScaler
+import pandas as pd
+import scipy.io as sio
+from os import listdir
+from os.path import isfile, join
+import numpy as np
+import keras
+from keras.models import Sequential
+from keras.layers import Dense, Activation, Dropout, Conv1D, GlobalAveragePooling1D, MaxPooling1D
+from keras import regularizers
+from keras.utils import np_utils
+
+number_of_classes = 4  # Total number of classes
+
+
+def to_one_hot(y):
+    return np_utils.to_categorical(y)
+
+
+def change(x):  # From boolean arrays to decimal arrays
+    answer = np.zeros((np.shape(x)[0]))
+    for i in range(np.shape(x)[0]):
+        max_value = max(x[i, :])
+        max_index = list(x[i, :]).index(max_value)
+        answer[i] = max_index
+    return answer.astype(np.int)
+
+
+mypath = 'training2017/'  # Training directory
+onlyfiles = [f for f in listdir(mypath) if (isfile(join(mypath, f)) and f[0] == 'A')]
+bats = [f for f in onlyfiles if f[7] == 'm']
+check = 100
+mats = [f for f in bats if (np.shape(sio.loadmat(mypath + f)['val'])[1] >= check)]
+size = len(mats)
+print('Total training size is ', size)
+big = 10100
+X = np.zeros((size, big))
+
+for i in range(size):
+    dummy = sio.loadmat(mypath + mats[i])['val'][0, :]
+    if (big - len(dummy)) <= 0:
+        X[i, :] = dummy[0:big]
+    else:
+        b = dummy[0:(big - len(dummy))]
+        goal = np.hstack((dummy, b))
+        while len(goal) != big:
+            b = dummy[0:(big - len(goal))]
+            goal = np.hstack((goal, b))
+        X[i, :] = goal
+
+target_train = np.zeros((size, 1))
+Train_data = pd.read_csv(mypath + 'REFERENCE.csv', sep=',', header=None, names=None)
+for i in range(size):
+    if Train_data.loc[Train_data[0] == mats[i][:6], 1].values == 'N':
+        target_train[i] = 0
+    elif Train_data.loc[Train_data[0] == mats[i][:6], 1].values == 'A':
+        target_train[i] = 1
+    elif Train_data.loc[Train_data[0] == mats[i][:6], 1].values == 'O':
+        target_train[i] = 2
+    else:
+        target_train[i] = 3
+
+Label_set = to_one_hot(target_train)
+
+X = (X - X.mean()) / (X.std())  # Some normalization here
+X = np.expand_dims(X, axis=2)  # For Keras's data input size
+
+values = [i for i in range(size)]
+permutations = np.random.permutation(values)
+X = X[permutations, :]
+Label_set = Label_set[permutations, :]
+
+train = 0.9  # Size of training set in percentage
+X_train = X[:int(train * size), :]
+Y_train = Label_set[:int(train * size), :]
+X_val = X[int(train * size):, :]
+Y_val = Label_set[int(train * size):, :]
+
+# def create_model():
+model = Sequential()
+model.add(Conv1D(128, 55, activation='relu', input_shape=(big, 1)))
+model.add(MaxPooling1D(10))
+model.add(Dropout(0.5))
+model.add(Conv1D(128, 25, activation='relu'))
+model.add(MaxPooling1D(5))
+model.add(Dropout(0.5))
+model.add(Conv1D(128, 10, activation='relu'))
+model.add(MaxPooling1D(5))
+model.add(Dropout(0.5))
+model.add(Conv1D(128, 5, activation='relu'))
+model.add(GlobalAveragePooling1D())
+# model.add(Flatten())
+model.add(Dense(256, kernel_initializer='normal', activation='relu'))
+model.add(Dropout(0.5))
+model.add(Dense(128, kernel_initializer='normal', activation='relu'))
+model.add(Dropout(0.5))
+model.add(Dense(64, kernel_initializer='normal', activation='relu'))
+model.add(Dropout(0.5))
+model.add(Dense(number_of_classes, kernel_initializer='normal', activation='softmax'))
+
+model.summary()
+model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
+hist = model.fit(X_train, Y_train, validation_data=(X_val, Y_val), batch_size=256, epochs=50, verbose=2, shuffle=True)
+pd.DataFrame(hist.history).to_csv(path_or_buf='Conv_models/History.csv')
+predictions = model.predict(X_val)
+score = accuracy_score(change(Y_val), change(predictions))
+print('Last epoch\'s validation score is ', score)
+df = pd.DataFrame(change(predictions))
+df.to_csv(path_or_buf='Conv_models/Preds_' + str(format(score, '.4f')) + '.csv', index=None, header=None)
+pd.DataFrame(confusion_matrix(change(Y_val), change(predictions))).to_csv(
+    path_or_buf='Conv_models/Result_Conf' + str(format(score, '.4f')) + '.csv', index=None, header=None)