Brugada Detection from 12-Lead ECG Using a Multi-Scale Hybrid Transformer with Explainability

AI-Assisted Multilabel Diagnosis of 12-Lead Electrocardiograms Using an Interpretable Stacked Deep Learning Model with External Validation

A compact and interpretable framework for five-superclass multi-label ECG diagnosis that integrates leakage-aware development, threshold-controlled testing, frozen external validation, and multimethod interpretability is developed.

Mamba—GCN: Fusion Framework for Interpretable Arrhythmia Diagnosis Using 12-Lead ECG Signals

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.

Signals Are All You Need: A Deep Learning Architecture for ECG Lead Classification

MTENet: A Multi-Representation Time-Series Evidential Network for Automated Heart Murmur Detection from Phonocardiogram Signals

MTENet is proposed, a Multi-representation Time-series Evidential Network that models a phase-enhanced one-dimensional PCG waveform through a bidirectional Mamba state-space encoder, capturing long-range temporal dependencies with linear-time complexity.

Self-Supervised ECG Foundation Models for Enhanced Detection of Paroxysmal Atrial Fibrillation

Results show that training design choices, particularly dataset size and Lr, are decisive for SSL performance, and that neural networks have emerged as a promising approach for detecting PAF from ECG signals.

X-Beat: An Explainable Framework for ECG Image Classification

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.