Multi-Feature Fusion and Compressed Bi-LSTM for Memory-Efficient Heartbeat Classification on Wearable Devices
- DOI
- 10.1109/jbhi.2026.3669719
- Published
- 2026
- Container
- IEEE Journal of Biomedical and Health Informatics
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1109/jbhi.2026.3669719,
title = {Multi-Feature Fusion and Compressed Bi-LSTM for Memory-Efficient Heartbeat Classification on Wearable Devices},
author = {Reza Nikandish and Jiayu He and Benyamin Haghi},
year = {2026},
journal = {IEEE Journal of Biomedical and Health Informatics},
doi = {10.1109/jbhi.2026.3669719},
url = {https://doi.org/10.1109/jbhi.2026.3669719}
}RIS
TY - JOUR TI - Multi-Feature Fusion and Compressed Bi-LSTM for Memory-Efficient Heartbeat Classification on Wearable Devices AU - Reza Nikandish AU - Jiayu He AU - Benyamin Haghi PY - 2026 JO - IEEE Journal of Biomedical and Health Informatics DO - 10.1109/jbhi.2026.3669719 UR - https://doi.org/10.1109/jbhi.2026.3669719 ER -
APA
Nikandish, R., He, J., & Haghi, B. (2026). Multi-Feature Fusion and Compressed Bi-LSTM for Memory-Efficient Heartbeat Classification on Wearable Devices. IEEE Journal of Biomedical and Health Informatics. https://doi.org/10.1109/jbhi.2026.3669719
Source records
- crossref · retrieved 2026-09-25T12:12:49.136Z