Arrhythmia Classification Based on Convolutional Long Short Term Memory Network

Abstract

Chronic cardiovascular diseases such as arrhythmia seriously affect human health. The automatic classification of ElectroCardioGram(ECG) signals can effectively improve the diagnostic efficiency of such diseases and reduce labor costs. To tackle this problem, an improved Long-Short Term Memory (LSTM) method is proposed to achieve automatic classification of one dimensional ECG signals. Firstly, deep Convolutional Neural Network (CNN) is designed to deeply encode the ECG signal, and ECG signal morphological features are extracted. Secondly, the LSTM classification network is used to realize automatic classification of arrhythmia of ECG signal features. Experimental studies based on the MIT-BIH arrhythmia database show that the training duration is significantly shortened and more than 99.2% classification accuracy is obtained. Sensitivity and other evaluation parameters are improved to meet the real-time and efficient requirements for automatic classification of ECG signals.