HSD-Net: a dual-branch CNN-BiLSTM network with hybrid cepstral fusion for heart sound classification

Cardiovascular diseases (CVDs) represent a major global health threat, making early detection crucial. While cardiac auscultation is cost-effective, its reliance on clinical expertise leads to significant variability in diagnostic accuracy. Existing automated heart sound classification methods suffer from inadequate feature representation and limited model performance due to constraints imposed by the randomness, nonstationarity, and complex acoustic environment of phonocardiogram (PCG) signals.

In this paper, an improved dual-branch CNN-BiLSTM network with hybrid cepstral fusion, named HSD-Net, is proposed for automated heart sound classification. To overcome feature representation limitations, we introduce a hybrid cepstral fusion feature set that combines Mel-Frequency Cepstral Coefficients (MFCC) and Inverse Mel-Frequency Cepstral Coefficients (IMFCC) with their first-order delta coefficients, capturing complementary low-frequency and high-frequency acoustic information. This feature set is processed by a dedicated dual-branch architecture: a convolutional neural network (CNN) branch extracts localized spectrotemporal patterns, while a bidirectional Long Short-Term Memory (BiLSTM) branch models long-range temporal dependencies. A Squeeze-and-Excitation (SE) attention mechanism is integrated to adaptively recalibrate feature importance.

Extensive evaluations on two public datasets demonstrate that the proposed HSD-Net achieves superior performance compared with existing methods. On the PhysioNet/Computing in Cardiology Challenge 2016 dataset, the model achieved an accuracy of 96.25%, a sensitivity of 95.85%, and a specificity of 96.50%. On the additional five-class Yaseen dataset, the model achieved an average accuracy of 99.32% in valvular heart disease classification. Ablation studies quantitatively confirmed that both the hybrid features and the dual-branch architecture contribute significantly to the overall performance.

The proposed HSD-Net provides an effective framework for automated heart sound analysis. This work offers a reliable technical foundation for developing clinical decision-support systems, with significant potential for application in early screening and telemedicine.