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Peer-reviewedLicence not statedBiomedical Engineering Letters

Patient-adaptive and clinically interpretable arrhythmia detection from ECG using lightweight hybrid deep learning

  • Gauri S. Bhagat1
  • Nitin S. More1
  1. 1Department of Computer Science and Engineering, School of Computing, MIT Art, Design and Technology University, Pune, Maharashtra India
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Cite

BibTeXRIS
BibTeX
@article{bhagat2026patientadaptive,
  title = {{Patient-adaptive and clinically interpretable arrhythmia detection from ECG using lightweight hybrid deep learning}},
  author = {Gauri S. Bhagat and Nitin S. More},
  journal = {Biomedical Engineering Letters},
  year = {2026},
  month = sep,
  doi = {10.1007/s13534-026-00615-7},
  url = {https://doi.org/10.1007/s13534-026-00615-7},
}
RIS
TY  - JOUR
TI  - Patient-adaptive and clinically interpretable arrhythmia detection from ECG using lightweight hybrid deep learning
AU  - Gauri S. Bhagat
AU  - Nitin S. More
PY  - 2026
DA  - 2026/09/19
JO  - Biomedical Engineering Letters
DO  - 10.1007/s13534-026-00615-7
UR  - https://doi.org/10.1007/s13534-026-00615-7
SN  - 2093-9868
SN  - 2093-985X
ER  -