X-CBNet: An Explainable Effective Deep Learning Framework Based on Spectrograms for Predicting Valvular Disorder using PCG Signals

Valvular Heart Disease (VHD), caused by malfunctioning heart valves, poses significant diagnostic challenges due to the complexity of heart sound patterns and variability in clinical presentations. Traditional auscultation methods are subjective, and existing automated models often function as black boxes, offering limited insight into the reasoning behind predictions. Therefore, there is a pressing need for accurate, interpretable, and multi-class diagnostic tools to aid clinicians in early and reliable detection of VHD using phonocardiogram (PCG) signals. X-CBNet, a convolutional neural network (CNN) followed by a bidirectional long short-term memory (Bi-LSTM) network framework, is employed in this research to harness deep learning’s capability to achieve high diagnostic accuracy while ensuring interpretability through explainable AI methods. Melspectrograms are used to capture essential features of the phonocardiograms. The proposed model is designed as a five-class classifier distinguishing between aortic stenosis, mitral stenosis, mitral regurgitation, mitral valve prolapse, and normal heart sounds. Gradient-weighted class activation mapping (Grad-CAM) is utilized for explainability, generating heatmaps that visualize model decision-making. The proposed X-CBNet model achieved an overall accuracy of 99.15%. Per-class accuracies are: Normal 99.15%, Aortic Stenosis 98.43%, Mitral Regurgitation 98.79%, Mitral Stenosis 99.58%, and Mitral Valve Prolapse 99.80%. The model also achieved a macro-average AUC of 0.99 and a micro-average AUC of 0.98, while maintaining a misclassification rate of less than 1%. The Grad-CAM heatmaps further validate model decisions by highlighting class-discriminative features. The robust performance demonstrates that the proposed model is suitable for real-time clinical applications, offering enhanced transparency through the integration of explainable AI.