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

Jian Liu1,*, 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: email

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

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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APA Style
Liu, J., Xia, X., Han, C., Hui, J., Feng, J. (2022). Classification of arrhythmia based on convolutional neural networks and encoder-decoder model. Computers, Materials & Continua, 73(1), 265-278. https://doi.org/10.32604/cmc.2022.029227
Vancouver Style
Liu J, Xia X, Han C, Hui J, Feng J. Classification of arrhythmia based on convolutional neural networks and encoder-decoder model. Comput Mater Contin. 2022;73(1):265-278 https://doi.org/10.32604/cmc.2022.029227
IEEE Style
J. Liu, X. Xia, C. Han, J. Hui, and J. Feng "Classification of Arrhythmia Based on Convolutional Neural Networks and Encoder-Decoder Model," Comput. Mater. Contin., vol. 73, no. 1, pp. 265-278. 2022. https://doi.org/10.32604/cmc.2022.029227



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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