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[MS-DTNet: an efficient model combining multi-scale convolution and dual-tower attention for detecting abnormal heart
Objectives:
To develop an efficient model for detecting abnormal heart sounds and providing technical support for automated phonocardiogram (PCG) analysis.
Methods:
The proposed model, MS-DTNet, integrates multi-scale convolution and a dual-tower attention mechanism. The network employs depthwise separable convolution to achieve a lightweight architecture, applies multi-scale convolution to capture features across receptive fields in parallel, utilizes a dual-tower structure to extract both short- and long-term temporal information, and incorporates a flipped-attention mechanism to further enhance feature representation. Experiments were conducted on the public CinC2016 dataset using 5-fold cross-validation.
Results:
In the abnormal heart sound classification task, the proposed model achieved an average accuracy of 94.49%, a specificity of 93.44%, a sensitivity of 95.49%, a precision of 93.81%, F1-score of 94.64%, and ROC-AUC of 98.65%, outperforming all the comparison models.
Conclusions:
The proposed MS-DTNet model demonstrates excellent performance in PCG classification and provides technical assistance for multi-source heart sound analysis and clinical decision support.
