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Deep arrhythmia classification based on SENet and lightweight context transform

Authors :
Yuni Zeng
Hang Lv
Mingfeng Jiang
Jucheng Zhang
Ling Xia
Yaming Wang
Zhikang Wang
Source :
Mathematical Biosciences and Engineering, Vol 20, Iss 1, Pp 1-17 (2023)
Publication Year :
2023
Publisher :
AIMS Press, 2023.

Abstract

Arrhythmia is one of the common cardiovascular diseases. Nowadays, many methods identify arrhythmias from electrocardiograms (ECGs) by computer-aided systems. However, computer-aided systems could not identify arrhythmias effectively due to various the morphological change of abnormal ECG data. This paper proposes a deep method to classify ECG samples. Firstly, ECG features are extracted through continuous wavelet transform. Then, our method realizes the arrhythmia classification based on the new lightweight context transform blocks. The block is proposed by improving the linear content transform block by squeeze-and-excitation network and linear transformation. Finally, the proposed method is validated on the MIT-BIH arrhythmia database. The experimental results show that the proposed method can achieve a high accuracy on arrhythmia classification.

Details

Language :
English
ISSN :
15510018
Volume :
20
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Mathematical Biosciences and Engineering
Publication Type :
Academic Journal
Accession number :
edsdoj.ff3a6cfa1c4d42149504259fb66137b7
Document Type :
article
Full Text :
https://doi.org/10.3934/mbe.2023001?viewType=HTML