An interpretable multi-scale hybrid attention network (MSHAN) for explainable ECG arrhythmia classification.

Karmel A, Das J, Rajesh K, Kanimozhi G

DOI: 10.3389/frai.2026.1867173
Published: 2026
Container: Frontiers in artificial intelligence
Publisher: Not recorded
Open access: yes

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Cite this work

BibTeX

@article{allodium:10.3389/frai.2026.1867173,
  title = {An interpretable multi-scale hybrid attention network (MSHAN) for explainable ECG arrhythmia classification.},
  author = {Karmel A and Das J and Rajesh K and Kanimozhi G},
  year = {2026},
  journal = {Frontiers in artificial intelligence},
  doi = {10.3389/frai.2026.1867173},
  url = {https://doi.org/10.3389/frai.2026.1867173}
}

RIS

TY  - JOUR
TI  - An interpretable multi-scale hybrid attention network (MSHAN) for explainable ECG arrhythmia classification.
AU  - Karmel A
AU  - Das J
AU  - Rajesh K
AU  - Kanimozhi G
PY  - 2026
JO  - Frontiers in artificial intelligence
DO  - 10.3389/frai.2026.1867173
UR  - https://doi.org/10.3389/frai.2026.1867173
ER  -

APA

A, K., J, D., K, R., & G, K. (2026). An interpretable multi-scale hybrid attention network (MSHAN) for explainable ECG arrhythmia classification.. Frontiers in artificial intelligence. https://doi.org/10.3389/frai.2026.1867173

Source records

pubmed · retrieved 2026-11-10T11:38:38.703Z