ARRHYTHMIA CLASSIFICATION USING DEEP RESIDUAL NEURAL NETWORKS

Abstract

Arrhythmia classification with electrocardiogram (ECG) is of great importance for the identification of arrhythmia diseases. However, since the variance of ECG signal in wave appears frequently, it is still a very challenging task to obtain a very good classification result. In this paper, an arrhythmia classification with ECG based on deep residual networks is proposed, of which two improved residual blocks are used to combine soft and hard subsampling. With such blocks, the network can well hold spatial information and improve the classification performance with a simple model structure. Experiments on the MIT-BIH arrhythmia database show that the proposed method obtained an average classification accuracy of 99.59% and an average classification specificity 99.63%, which are 0.26% and 0.57% higher than that of the most state-of-art method based on deep learning.