A multichannel energy-based noisy-segment rejection step combined with an MFCC-Conformer classifier improves CAD detection accuracy to 78.4% on 297 subjects, a 4.1% gain over baseline training without rejection.
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Noise-Robust Contrastive Learning with an MFCC-Conformer For Coronary Artery Disease Detection
A multichannel energy-based noisy-segment rejection step combined with an MFCC-Conformer classifier improves CAD detection accuracy to 78.4% on 297 subjects, a 4.1% gain over baseline training without rejection.