WavelNet: A novel convolutional neural network architecture for arrhythmia classification from electrocardiograms

Background and objective: Automated detection of arrhythmias from electrocardiograms (ECGs) can be of considerable assistance to medical professionals in providing efficient treatment for patients with car-diovascular diseases. In recent times, convolutional neural network (CNN)-based arrhythmia classifica-tion models have been introduced, but their decision-making processes remain unclear and their per-formances are not reproducible. This paper proposes an accurate, interpretable, and reproducible end -to-end arrhythmia classification model based on a novel CNN architecture named WavelNet, which is interpretable and optimal for dealing with ECGs.Methods: Inspired by SincNet, which is capable of band-pass filtering-based spectral analysis, WavelNet was devised to achieve wavelet transform-based spectral analysis. WavelNet was trained using a subject -oriented five-class ECG arrhythmia dataset generated from the MIT-BIH Arrhythmia Database while fol-lowing a benchmark scheme. By adopting various mother wavelets, multiple WavelNet-based arrhythmia classification models were implemented. To investigate whether our wavelet transform-based approach outperforms original end-to-end and band-pass filtering-based approaches, our proposed models were compared with vanilla CNN-and SincNet-based models. Model implementation and evaluation processes were repeated ten times in a Google Colab Pro + environment. Furthermore, our most successful model was compared with state-of-the-art arrhythmia classification models for performance evaluation.Results: The proposed WavelNet-based models showed excellent performance on classifying non-ectopic, supraventricular ectopic, and ventricular ectopic beats because of their ability to perform adaptive spec-tral analysis while preserving temporal ECG information compared with vanilla CNN-and SincNet-based models. In particular, a Symlet 4 wavelet-adopting WavelNet-based model achieved the best performance with nearly 90% overall accuracy as well as the highest levels of sensitivity in classifying each arrhythmia class: 91.4%, 49.3%, and 91.4% for non-ectopic, supraventricular ectopic, and ventricular ectopic beat clas-sifications, respectively. These results were comparable to those of state-of-the-art models. In addition, the results are reproducible, which differentiates our study from previous studies.Conclusions: Our proposed WavelNet-based arrhythmia classification model achieved remarkable perfor-mance based on a reasonable decision-making process, in comparison with other models. As its note-worthy performance is clinically reasonable and reproducible, our proposed model can contribute toward implementing a real-world precision healthcare system for patients with cardiovascular diseases.(c) 2023 Elsevier B.V. All rights reserved.