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Transformer-enhanced EWT framework for cardiac arrhythmia classification: Integrating Aliev-Panfilov modeling and improved multi-objective quantum-behaved particle swarm optimization

bioinformatics
cardiology
data-mining-and-machine-learning

Abstract

Accurate arrhythmia detection is vital for cardiac disease diagnosis. While deep learning is widely used in ECG-based classification, few models incorporate cardiac electrophysiology principles. This study developed a finite element model using the Aliev-Panfilov model to simulate cardiac electrophysiology and generate synthetic ECG signals. Two arrhythmia types were modeled by perturbing key variables, producing distinct ECG abnormalities. An improved Multi-Objective Quantum-behaved Particle Swarm Optimization (MOQPSO) method optimized empirical wavelet transform (EWT) parameters, using spectral kurtosis and Jensen-Shannon divergence as metrics. The Pareto optimal front enabled the selection of optimal parameters, yielding high-quality intrinsic mode functions. A transformer-enhanced EWT network was constructed for classification. Ablation experiments validated the decomposition strategy and feature extraction modules. Comparative results showed the proposed method outperformed EEMD, standard EWT, CNN, and LSTM networks. Hyperband optimization achieved 98.27 % accuracy. Evaluation on the MIT-BIH arrhythmia database attained 97.87 % accuracy, confirming robustness and generalizability. This work bridges biophysical modeling with deep learning, offering a promising ECG analysis framework.
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