CLINet: A novel deep learning network for ECG signal classification

Machine learning is poised to revolutionize medicine with algorithms that spot cardiac arrhythmia. An automated diagnostic approach can boost the efficacy of diagnosing life-threatening arrhythmia disorders in routine medical procedures. In this paper, we propose a deep learning network CLINet for E...

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Bibliographic Details
Published in:Journal of electrocardiology 2024-03, Vol.83, p.41-48
Main Authors: Mantravadi, Ananya, Saini, Siddharth, R., Sai Chandra Teja, Mittal, Sparsh, Shah, Shrimay, R., Sri Devi, Singhal, Rekha
Format: Article
Language:English
Subjects:
convolution
ECG signal classification
involution
LSTM
Machine learning
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Summary:Machine learning is poised to revolutionize medicine with algorithms that spot cardiac arrhythmia. An automated diagnostic approach can boost the efficacy of diagnosing life-threatening arrhythmia disorders in routine medical procedures. In this paper, we propose a deep learning network CLINet for ECG signal classification. Our network uses convolution, LSTM and involution layers to bring their unique advantages together. For both convolution and involution layers, we use multiple, large size kernels for multi-scale representation learning. CLINet does not require complicated pre-processing and can handle electrocardiograms of any length. Our network achieves 99.90% accuracy on the ICCAD dataset and 99.94% accuracy on the MIT-BIH dataset. With only 297 K parameters, our model can be easily embedded in smart wearable devices. The source code of CLINet is available at https://github.com/CandleLabAI/CLINet-ECG-Classification-2024.
ISSN:0022-0736
1532-8430
DOI:10.1016/j.jelectrocard.2024.01.004