A deep learning framework for arrhythmia classification: mitigating data leakage and class imbalance in ECG analysis.
- DOI
- 10.1080/03091902.2026.2724902
- Published
- 2026 Sep 9
- Container
- Journal of medical engineering & technology
- Publisher
- Not recorded
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1080/03091902.2026.2724902,
title = {A deep learning framework for arrhythmia classification: mitigating data leakage and class imbalance in ECG analysis.},
author = {Sani S and Mitra D and Banerjee P and Srivastava D},
year = {2026},
journal = {Journal of medical engineering \& technology},
doi = {10.1080/03091902.2026.2724902},
url = {https://doi.org/10.1080/03091902.2026.2724902}
}RIS
TY - JOUR TI - A deep learning framework for arrhythmia classification: mitigating data leakage and class imbalance in ECG analysis. AU - Sani S AU - Mitra D AU - Banerjee P AU - Srivastava D PY - 2026 JO - Journal of medical engineering & technology DO - 10.1080/03091902.2026.2724902 UR - https://doi.org/10.1080/03091902.2026.2724902 ER -
APA
S, S., D, M., P, B., & D, S. (2026). A deep learning framework for arrhythmia classification: mitigating data leakage and class imbalance in ECG analysis.. Journal of medical engineering & technology. https://doi.org/10.1080/03091902.2026.2724902
Source records
- pubmed · retrieved 2026-09-27T16:07:26.378Z