Heart disease is the leading cause of death in humans

and most cardiovascular diseases are accompanied by arrhythmias. In order to realize the automatic analysis of different types of electrocardiogram (ECG) signals and recognize abnormal heart rhythm

a new classification algorithm based on deep learning was studied and proposed. Considering the characteristics of the EGG

the convolutional neural network (CNN) was used to extract the local correlation features

and the long-short term memory (LSTM) network was used to capture the long-term dependence of ECG sequence data to identify five different types of heart beats automatically. The deep learning method based on LSTM and CNN directly took the preprocessed ECG signals as the input of the network

and integrated the feature extraction and ECG classification into a single learner. In terms of the problem of imbalance

sampling by sliding window was performed on minority class data to get more training data. The effectiveness of the algorithm was evaluated with the MIT-BIH arrhythmia dataset

and the accuracy

specificity

and sensitivity of the classification results in more than 20 000 cardiac beats recorded in the test set reached 99.11%

99.44%

and 97.27%

respectively. In addition

the operation of sliding window sampling significantly improved the sensitivity of minority class. The experimental results show that compared with the traditional methods

the parallel combination model based on LSTM and CNN did not require separate feature extraction steps and achieved better classification performance

which is suitable for wearable ECG devices and remote monitoring field.