A clinically-oriented XAI framework for arrhythmia triage via Hierarchically-Decomposed Neural Attribution: From...

Deep learning models for biomedical signal analysis achieve high accuracy but suffer from interpretability limitations that hinder their deployment in critical applications. This paper presents the Hierarchically-Decomposed Neural Attribution (HDNA) framework, a novel explainable AI (XAI) methodology for biomedical data analysis with application to electrocardiogram (ECG) signal processing. HDNA employs a dual-stream architecture that decomposes the decision-making process into two sequential, interpretable components: a rhythmicity stream that identifies critical temporal events through contrastive learning-based deviation analysis, and a morphology stream that performs targeted gradient-based feature localization on these events. The framework's "Where-then-What" reasoning pipeline incorporates a structural gating mechanism that reduces computational overhead by 90% compared to full-sequence gradient methods, achieving real-time inference (2.4 ms latency) on standard CPU hardware. As a case study in computational biology, we evaluated HDNA on cardiac arrhythmia detection using the PTB-XL (Macro F1: 0.760 ± 0.008) and MIT-BIH (Weighted F1: 0.89 ± 0.01) datasets, demonstrating robust performance with high explanation faithfulness (93.0% pointing accuracy, 0.78 ± 0.04 IoU). The framework's lightweight design (1.4M parameters) and efficient explanation generation make it suitable for resource-constrained systems and real-time biomedical signal monitoring applications. This work contributes to the field of computational biology by providing a transparent, interpretable machine learning methodology that can be applied to diverse biomedical data analysis tasks.