Interpretable Wavelet-Based Hybrid Architecture for Multiclass ECG Beat Classification
DOI:
https://doi.org/10.33414/rtyc.56.22-39.2026Keywords:
ECG, cardiac arrhythmia classification, wavelet transform, Deep learning, transformer, deep neural networksAbstract
A hybrid deep learning architecture for multiclass electrocardiographic (ECG) beat classification using the MIT-BIH Arrhythmia database is presented. The proposed approach combines multiscale Inception-based convolutional blocks with depthwise separable convolutions, one-dimensional Transformer encoders (1D Transformers), bidirectional recurrent units based on Gated Recurrent Units (BiGRU), and contextual attention mechanisms. Each heartbeat representation is enriched through statistical features extracted from multiscale discrete wavelet transforms, together with a decimated temporal branch derived from the original signal. To address class imbalance, controlled physiological augmentation strategies, Borderline-SMOTE, and a weighted focal loss function are jointly employed. The model achieved 99.88% training accuracy and 98.97% validation accuracy, while also exhibiting high latent-space separability and consistent interpretability patterns through SHapley Additive exPlanations (SHAP) analysis. The results suggest that integrating explicit spectro-temporal representations with hybrid deep modeling provides a robust, interpretable, and computationally efficient alternative for automatic cardiac arrhythmia classification.
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Copyright (c) 2026 Cesar Christian Holote Larrosa, Carlos Marcelo Gomez, Daniel Turra
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