Parameter-Efficient Densely Connected Dual Attention Network for Phonocardiogram Classification.

Area of Science:

• Cardiology
• Biomedical Engineering
• Artificial Intelligence

Background:

• Cardiac auscultation using phonocardiograms (PCG) is a vital non-invasive diagnostic tool for cardiovascular diseases (CVDs).
• Challenges in PCG analysis include inherent murmurs and limited supervised data, hindering accurate heart sound classification.
• Current deep learning methods often require extensive pre-processing, relying on time-consuming expert engineering.

Purpose of the Study:

• To propose a parameter-efficient, densely connected dual attention network (DDA) for automated heart sound classification.
• To develop an end-to-end deep learning architecture that integrates hierarchical feature extraction and attention mechanisms.
• To improve the computational efficiency and classification performance of computer-aided heart sound analysis.

Main Methods:

• A densely connected structure was employed for hierarchical extraction of heart sound features.
• A dual attention mechanism, utilizing self-attention, was implemented to aggregate local and global feature dependencies across positional and channel axes.
• The proposed DDA model was evaluated using stratified 10-fold cross-validation on the Cinc2016 benchmark dataset.

Main Results:

• The DDA model demonstrated superior performance compared to existing 1D deep learning models on the Cinc2016 benchmark.
• The network achieved significant computational efficiency, reducing the reliance on time-consuming pre-processing steps.
• Hierarchical feature extraction and dual attention mechanisms effectively captured complex patterns in heart sound data.

Conclusions:

• The proposed DDA model offers an effective and computationally efficient solution for heart sound classification.
• This approach advances computer-aided diagnosis of cardiovascular diseases by leveraging deep learning and attention mechanisms.
• The DDA network provides a promising direction for developing robust and accessible diagnostic tools in cardiology.