LightCardiacNet: light-weight deep ensemble network with attention mechanism for cardiac sound classification
| Autor: | Suma K. V., Deepali B. Koppad, Dharini Raghavan, Manjunath P. R. |
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| Jazyk: | angličtina |
| Rok vydání: | 2024 |
| Předmět: | |
| Zdroj: | Systems Science & Control Engineering, Vol 12, Iss 1 (2024) |
| Druh dokumentu: | article |
| ISSN: | 21642583 2164-2583 |
| DOI: | 10.1080/21642583.2024.2420912 |
| Popis: | Cardiovascular diseases (CVDs) account for about 32% of global deaths. While digital stethoscopes can record heart sounds, expert analysis is often lacking. To address this, we propose LightCardiacNet, an interpretable, lightweight ensemble neural network using Bi-Directional Gated Recurrent Units (Bi-GRU). It is trained on the PASCAL Heart Challenge and CirCor DigiScope datasets. Static network pruning enhances model sparsity for real-time deployment. We employ various data augmentation techniques to improve resilience to background noise. An ensemble of the two networks is constructed by employing a weighted average approach that combines the two light-weight attention Bi-GRU networks trained on different datasets, which outperforms several state-of-the-art networks achieving an accuracy of 99.8%, specificity of 99.6%, sensitivity of 95.2%, ROC-AUC of 0.974 and inference time of 17 ms on the PASCAL dataset, accuracy of 98.5%, specificity of 95.1%, sensitivity of 90.9%, ROC-AUC of 0.961 and inference time of 18 ms on the CirCor dataset, and an accuracy of 96.21%, sensitivity of 92.78%, specificity of 93.16%, ROC-AUC of 0.913 and inference time of 17.5 ms on real-world data. We adopt the SHAP algorithm to incorporate model interpretability and provide insights to make it clinically explainable and useful to healthcare professionals. |
| Databáze: | Directory of Open Access Journals |
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