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Deep arrhythmia classification based on SENet and lightweight context transform
- 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