An Explainable Quality-Aware ECG-PCG Fusion for Cardiovascular Disease Detection Using Robust Feature Modeling

Background/Objectives: To develop a fast and interpretable multimodal framework for the automatic detection of cardiac abnormalities using electrocardiogram (ECG) and phonocardiogram (PCG) signals. Methods: A multimodal classification scheme was designed by combining ECG and PCG recordings. For each modality, tailored preprocessing and temporal and spectral feature extraction were applied. The resulting information was fused through a quality-aware strategy that prioritized more reliable signal segments. The explainability results showed that the model focused on physiologically meaningful regions, providing supportive interpretability for its predictions. Experiments were conducted on the PhysioNet/CinC 2016 heart sound dataset, including normal and pathological recordings, using 10-fold cross-validation. Results: The proposed method achieved a mean F1 score of 97.2%, an accuracy of 95.6%, a specificity of 88.6%, and a sensitivity of 97.7%. In addition, the lightweight preprocessing and fast feature extraction pipeline allowed the full 10-fold cross-validation procedure to be completed in only 66 s. Conclusions: The proposed ECG-PCG framework provides a fast, accurate, and interpretable solution for automated cardiac abnormality detection and appears well suited for real-time cardiac screening applications.