Open Access

ARTICLE

Classification of Arrhythmia Based on Convolutional Neural Networks and Encoder-Decoder Model

Jian Liu^1,*, Xiaodong Xia¹, Chunyang Han², Jiao Hui³, Jim Feng⁴

1 School of Computer & Communication Engineering, University of Science and Technology Beijing, Beijing, 100083, China
2 Beijing Satellite Navigation Center, Beijing, 100093, China
3 The Institute of NBC Defense, Chinese PLA Army, Beijing, 102205, China
4 Amphenol Global Interconnect Systems, San Jose, CA 95131, US

* Corresponding Author: Jian Liu. Email:

Computers, Materials & Continua 2022, 73(1), 265-278. https://doi.org/10.32604/cmc.2022.029227

Received 28 February 2022; Accepted 31 March 2022; Issue published 18 May 2022

Abstract

As a common and high-risk type of disease, heart disease seriously threatens people’s health. At the same time, in the era of the Internet of Thing (IoT), smart medical device has strong practical significance for medical workers and patients because of its ability to assist in the diagnosis of diseases. Therefore, the research of real-time diagnosis and classification algorithms for arrhythmia can help to improve the diagnostic efficiency of diseases. In this paper, we design an automatic arrhythmia classification algorithm model based on Convolutional Neural Network (CNN) and Encoder-Decoder model. The model uses Long Short-Term Memory (LSTM) to consider the influence of time series features on classification results. Simultaneously, it is trained and tested by the MIT-BIH arrhythmia database. Besides, Generative Adversarial Networks (GAN) is adopted as a method of data equalization for solving data imbalance problem. The simulation results show that for the inter-patient arrhythmia classification, the hybrid model combining CNN and Encoder-Decoder model has the best classification accuracy, of which the accuracy can reach 94.05%. Especially, it has a better advantage for the classification effect of supraventricular ectopic beats (class S) and fusion beats (class F).

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Keywords

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