Title: Arrhythmia recognition and classification through deep learning-based approach

Authors: Rui Zhou; Xue Li; Binbin Yong; Zebang Shen; Chen Wang; Qingguo Zhou; Yunshan Cao; Kuan-Ching Li

Addresses: School of Information Science and Engineering, Lanzhou University, China ' School of Information Science and Engineering, Lanzhou University, China ' School of Information Science and Engineering, Lanzhou University, China ' School of Information Science and Engineering, Lanzhou University, China ' School of Information Science and Engineering, Lanzhou University, China ' School of Information Science and Engineering, Lanzhou University, China ' Department of Cardiology, Gansu Provincial Hospital, China ' Hubei University of Education, Zhongzhou Rd, Jiangxia Qu, Wuhan Shi, Hubei Sheng, China; Providence University, Taiwan

Abstract: Arrhythmia is a cardiac condition caused by abnormal electrical activity of the heart, which can be life-threatening. Electrocardiogram (ECG) is the principal diagnostic tool used to detect arrhythmias or heart abnormalities. It contains information about the different types of arrhythmias. However, due to the complexity and nonlinearity of ECG signals, such as the presence of noise, the time dependence of ECG signals and the irregularity of the heartbeat, it is troublesome to analyse ECG signals manually. Moreover, the interpretation of ECG signals is subjective and might vary among experts in the field. Therefore, an automatic, high-precision ECG recognition method is important to arrhythmia detection. For such, a method is proposed in this paper for arrhythmia classification, which is based on deep learning-based approach long short-term memory (LSTM), where five classes of arrhythmias as recommended by the Association for Advancement of Medical Instrumentation (AAMI) are analysed. The method has been tested on the MIT-BIH arrhythmia database with a number of useful performance evaluation measures, showing that is a promising and better performance than other artificial intelligence methods used.

Keywords: electrocardiogram signal; long short-term memory; arrhythmia classification; artificial intelligence; deep learning.

DOI: 10.1504/IJCSE.2019.101897

International Journal of Computational Science and Engineering, 2019 Vol.19 No.4, pp.506 - 517

Received: 30 Aug 2017
Accepted: 12 Sep 2017
Published online: 30 Aug 2019 *

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