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Study of cardiac arrhythmia classification based on convolutional neural network

Yonghui Dai¹, Bo Xu¹, Siyu Yan² and Jing Xu²

1. Management School, Shanghai University of International Business and Economics
   Shanghai 201620, China
   daiyonghui@suibe.edu.cn, brianxubo@163.com
2. School of Information Management and Engineering, Shanghai University of Finance and Economics
   Shanghai 200433, China
   ysy18956@163.com, 1053517221@qq.com

Abstract

Cardiovascular disease is one of the diseases threatening the human health, and its diagnosis has always been a research hotspot in the medical field. In particular, the diagnosis technology based on ECG (electrocardiogram) signal as an effective method for studying cardiovascular diseases has attracted many scholars’ attention. In this paper, Convolutional Neural Network (CNN) is used to study the feature classification of three kinds of ECG signals, which including sinus rhythm (SR), Ventricular Tachycardia (VT) and Ventricular Fibrillation (VF). Specifically, different convolution layer structures and different time intervals are used for ECG signal classification, such as the division of 2-layer and 4-layer convolution layers, the setting of four time periods (1s, 2s, 3s, 10s), etc. by performing the above classification conditions, the best classification results are obtained. The contribution of this paper is mainly in two aspects. On the one hand, the convolution neural network is used to classify the arrhythmia data, and different classification effects are obtained by setting different convolution layers. On the other hand, according to the data characteristics of three kinds of ECG signals, different time periods are designed to optimize the classification performance. The research results provide a reference for the classification of ECG signals and contribute to the research of cardiovascular diseases.

Key words

Cardiac arrhythmia classification, convolutional neural network, ECG signals, segment size

Digital Object Identifier (DOI)

https://doi.org/10.2298/CSIS191229011D

Publication information

Volume 17, Issue 2 (June 2020)
Year of Publication: 2020
ISSN: 2406-1018 (Online)
Publisher: ComSIS Consortium

Full text

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How to cite

Dai, Y., Xu, B., Yan, S., Xu, J.: Study of cardiac arrhythmia classification based on convolutional neural network. Computer Science and Information Systems, Vol. 17, No. 2, 445–458. (2020), https://doi.org/10.2298/CSIS191229011D