Key result
Novel DSEM deep-learning architecture achieves ~95% diagnostic accuracy for ECG-detected heart disease over comparator models.
Why the study?
Early detection of heart disease via ECG is crucial but manual examination of paper-based ECGs presents challenges requiring improved diagnostic methods.
Does the DSEM deep learning architecture improve diagnostic accuracy for heart disease using ECG signals compared to other models?
Does the DSEM deep learning architecture improve diagnostic accuracy for heart disease using ECG signals compared to other models?
Answers are grounded in this paper and cite their sources.
A novel deep learning architecture (DSEM) for ECG signal analysis demonstrates high diagnostic accuracy (95%) for heart disease detection.
Heart disease poses a substantial health risk, especially among older adults, and remains a leading global cause of death. Early detection is crucial, emphasising the importance of ECG in cardiovascular diagnosis. Traditionally, manual examination of paper-based ECGs is common but presents challenges. To tackle this, a novel DSEM architecture is proposed for heart disease diagnosis. This architecture integrates MWF for data pre-processing and ISLBT for improved shape feature extraction. The framework includes pre-processing, feature extraction, and classification phases. Initially, ECG signals undergo MWF pre-processing for clean representations. Then, comprehensive feature extraction, including ISLBT for shape features, is conducted. The DSEM architecture involves training bi-GRU, RNN, and LSTM models to capture various data aspects. The higher-level ImLinkNet model integrates predictions for final diagnosis. When TD = 90, the proposed method achieves a higher accuracy of 95% as compared to the other models.
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Babu et al. (2026) studied Heart disease. DSEM architecture vs. other models was evaluated on Diagnostic accuracy. The proposed DSEM architecture for ECG signal analysis achieved a diagnostic accuracy of 95% for heart disease, outperforming other models.
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