Key result
Deep learning with soft attention cuts Brugada Syndrome classification errors by ~50% versus legacy models.
Why the study?
Conventional deep learning models for automated electrocardiographic interpretation remain poorly adopted clinically due to their lack of transparency.
Does an interpretable deep learning framework with soft attention mechanisms improve diagnostic accuracy and explainability for Brugada Syndrome screening compared to legacy models?
Does an interpretable deep learning framework with soft attention mechanisms improve diagnostic accuracy and explainability for Brugada Syndrome screening compared to legacy models?
An interpretable deep learning model using attention mechanisms provides high accuracy (95.1% lead-level) for Brugada Syndrome detection while offering visual explainability of diagnostically relevant ECG segments.
Hypothesis-generating for explainable AI Brugada screening; prospective validation required before clinical use.
Brugada Syndrome (BrS) is a genetic cardiac disorder associated with sudden cardiac death (SCD) and characterized by specific ST-segment patterns in right precordial ECG leads. Despite advances in automated electrocardiographic interpretation, conventional deep learning (DL) models remain poorly adopted in clinical settings due to their lack of transparency. In this work, it is presented an interpretable DL framework for BrS screening that leverages soft attention mechanisms to highlight diagnostically relevant ECG segments. The proposed architecture combines convolutional layers, bidirectional LSTM units, and a trainable attention module, operating on 850 ms windows centered on the S-peak. Predictions are aggregated at the lead level to reflect clinical practice. On a multi-center labeled dataset, the model achieves 93.7% window-level accuracy and 95.1% lead-level accuracy, with F1-scores of 0.95, 0.92, and 0.89 for Brugada-like, Non-Brugada, and Ambiguous classes, respectively. Attention heatmaps consistently align with ST-segment regions in true-positive Brugada cases and distinguish ambiguous from pathological inputs more robustly than threshold-based baselines. Classification errors at the lead level are reduced by over 50% compared to a legacy binary model. These findings demonstrate the feasibility of explainable AI for Brugada detection and offer a practical step toward interpretable, trust-driven ECG triage in cardiology.
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Pasero et al. (2025) studied Brugada Syndrome. Interpretable deep learning framework with soft attention mechanisms vs. Legacy binary model was evaluated on Lead-level accuracy. An interpretable deep learning framework with soft attention mechanisms achieved 95.1% lead-level accuracy for Brugada Syndrome detection, reducing classification errors by >50% vs a legacy model.
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