Explainable CNN-BiLSTM Framework for Multi-Class Sleep Apnea Severity Detection Using Single-Lead ECG Signals: A Comprehensive Machine Learning Approach.
- DOI
- 10.3390/diagnostics16152353
- Published
- 2026-07-27
- Container
- Diagnostics (Basel)
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.3390/diagnostics16152353,
title = {Explainable CNN-BiLSTM Framework for Multi-Class Sleep Apnea Severity Detection Using Single-Lead ECG Signals: A Comprehensive Machine Learning Approach.},
author = {Al-Quran F and Jawarneh M and Al-Mrayat OI and Ibrahim D and Samara G and Sheta A and Elmarhomy G and Baghdadi NA and Malki A and Atlam ES.},
year = {2026},
journal = {Diagnostics (Basel)},
doi = {10.3390/diagnostics16152353},
url = {https://doi.org/10.3390/diagnostics16152353}
}RIS
TY - JOUR TI - Explainable CNN-BiLSTM Framework for Multi-Class Sleep Apnea Severity Detection Using Single-Lead ECG Signals: A Comprehensive Machine Learning Approach. AU - Al-Quran F AU - Jawarneh M AU - Al-Mrayat OI AU - Ibrahim D AU - Samara G AU - Sheta A AU - Elmarhomy G AU - Baghdadi NA AU - Malki A AU - Atlam ES. PY - 2026 JO - Diagnostics (Basel) DO - 10.3390/diagnostics16152353 UR - https://doi.org/10.3390/diagnostics16152353 ER -
APA
F, A., M, J., OI, A., D, I., G, S., A, S., G, E., NA, B., A, M., & ES., A. (2026). Explainable CNN-BiLSTM Framework for Multi-Class Sleep Apnea Severity Detection Using Single-Lead ECG Signals: A Comprehensive Machine Learning Approach.. Diagnostics (Basel). https://doi.org/10.3390/diagnostics16152353
Source records
- europe-pmc · retrieved 2026-09-25T02:57:39.765Z
- doaj · retrieved 2026-09-25T02:57:39.739Z