Automated Heartbeat Classification Using 3-D Inputs Based on Convolutional Neural Network with Multi-Fields of View

Abstract

A high-performance method of automated heartbeat classification based on Convolutional Neural Network (CNN) is proposed in this paper. To make full use of the electrocardiogram information acquired from different parts of the human body, we present a novel 3-D data structure as the input of the CNN. The 3-D structure consists of multiple feature maps, each of which indicates the information collected from one lead and contains morphological characteristic, RR-interval, and beat-to-beat correlation feature. Besides, atrous spatial pyramid pooling (ASPP) module which uses filters with different resolutions is adopted to extract deep features in multi-fields of view. Validated on the MIT-BIH arrhythmia database, the proposed method yields an overall accuracy of 91.44% in the inter-patient practice. In particular, this method achieves 89.05% and 95.15% in the sensitivities of the supraventricular ectopic beat (SVEB) and ventricular ectopic beat (VEB) classes, respectively. With the high performance in detecting these two pathological classes, this method has potential clinical application.