TL;DR
A permutation-equivariant deep learning architecture that infers lead labels directly from waveforms, using convolutional, recurrent, and attention components to model temporal and inter-lead structure is proposed.
Abstract Accurate reconstruction of the standard 12-lead electrocardiogram (ECG) from a reduced number of leads has significant potential for simplifying cardiac monitoring systems and enabling wearable healthcare solutions. This study proposes a novel multi-lead convolutional autoencoder (MLCAE) for reconstructing sta...
It is demonstrated that hybrid spatio-temporal architectures can achieve diagnostic performance comparable to strong convo-lutional baselines while offering significantly improved trans-parency through a quantitative comparison of representative models and visual analysis of performance—interpretability trade-offs.
Results suggest the effectiveness of using GADF as a complementary representation and incorporating explicit graph-based modeling of inter-lead dependency for learning robust 12-lead ECG representations, and suggest that the proposed framework achieves competitive performance compared to supervised learning methods.
X- Beat is presented, an explainable and reliability-aware benchmark framework for ECG image classification designed to support trustworthy AI systems in healthcare and provides a structured and reproducible bench- mark for evaluating both predictive performance and explanation reliability in ECG image classification.
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