Deep arrhythmia classification based on SENet and lightweight context transform

Yuni Zeng; Hang Lv; Mingfeng Jiang; Jucheng Zhang; Ling Xia; Yaming Wang; Zhikang Wang; Yuni Zeng; Hang Lv; Mingfeng Jiang; Jucheng Zhang; Ling Xia; Yaming Wang; Zhikang Wang

doi:10.3934/mbe.2023001

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 1: 1-17. doi: 10.3934/mbe.2023001

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Theory article Special Issues

Deep arrhythmia classification based on SENet and lightweight context transform

1.
School of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China
2.
Department of Clinical Engineering, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310019, China
3.
Department of Biomedical Engineering, Zhejiang University, Hangzhou 310027, China
4.
Lishui University, Lishui 323000, China

Received: 15 July 2022 Revised: 04 September 2022 Accepted: 12 September 2022 Published: 29 September 2022

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.
- continuous wavelet transform,
- Squeeze-and-Excitation network,
- lightweight context transform,
- arrhythmia classification
Citation: Yuni Zeng, Hang Lv, Mingfeng Jiang, Jucheng Zhang, Ling Xia, Yaming Wang, Zhikang Wang. Deep arrhythmia classification based on SENet and lightweight context transform[J]. Mathematical Biosciences and Engineering, 2023, 20(1): 1-17. doi: 10.3934/mbe.2023001

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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.

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