A deep learning framework for multi-lead ECG arrhythmia classification with CAM interpretability and LLM-driven clinical guidance.

Authors

P Kavitha, L Shakkeera

Published in

Computer methods in biomechanics and biomedical engineering. Pages 1-17. Jul 31, 2026. Epub Jul 31, 2026.

Abstract

Cardiac arrhythmias remain a major cause of morbidity and mortality, requiring accurate and interpretable automated diagnosis. This reserach presents a lightweight deep learning framework for multi-lead ECG arrhythmia classification. A Winograd-scaled 1D MobileNet efficiently extracts discriminative features, while the Lead-aware Skip Weighting Residual ConvNeXt Attention with Builder Optimizer (LSW-RCABO) enhances classification through adaptive residual weighting and lead-aware attention. Grad-CAM provides visual explanations by highlighting waveform regions, and BioGPT generates patient-specific clinical recommendations using classification results and metadata. Experimental evaluation on the MIT-BIH, 12-lead ECG, and PhysioNet 2020 datasets demonstrates superior performance, achieving classification accuracies of 99.98%, 99.98%, and 99.97%, respectively.

PMID:
42537049
Bibliographic data and abstract were imported from PubMed on 01 Aug 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.