Explainable attention-based deep learning for classification and interpretation of heart murmurs using...

Area of Science:

• Cardiology
• Artificial Intelligence
• Biomedical Signal Processing

Background:

• Cardiovascular diseases (CVDs) pose a global health challenge, with traditional heart murmur diagnosis relying on subjective auscultation.
• Existing automated methods often lack reproducibility and clinical interpretability, hindering widespread adoption.
• There is a critical need for accurate, reproducible, and transparent diagnostic tools for heart murmurs.

Purpose of the Study:

• To develop and validate an Explainable Attention-Based Deep Learning framework for heart murmur classification and interpretation.
• To improve diagnostic accuracy and clinical trust in automated phonocardiogram (PCG) analysis.
• To provide visual explanations for model predictions, highlighting critical murmur segments.

Main Methods:

• Utilized a Transformer architecture for time-frequency feature extraction from PCG signals (spectrograms, MFCCs).
• Integrated Gradient-weighted Class Activation Mapping (Grad-CAM) for generating visual explanations of model predictions.
• Validated the framework across multiple large-scale datasets (HeartWave, CirCor DigiScope, PhysioNet, Shenzhen) using robust A-Test methods.

Main Results:

• Achieved high diagnostic performance: 96.7% accuracy, 95.5% macro-F1 score, and AUC > 0.97.
• Outperformed ten baseline models by 3-5% in accuracy and 2-4% in macro F1 score.
• Demonstrated strong alignment between model-generated explanations and expert annotations, confirming interpretability.

Conclusions:

• The proposed Explainable Attention-Based Deep Learning framework offers a significant advancement in automated heart murmur diagnosis.
• The model provides both high accuracy and crucial clinical interpretability, fostering greater trust in AI-driven cardiac diagnostics.
• Future work aims to enhance scalability and integrate multimodal data for comprehensive clinical decision support.