Development and Validation of a Class Imbalance-Resilient Cardiac Arrest Prediction Framework Incorporating Multiscale Aggregation, ICA and Explainability
This method is significant to biomedical research as it provides superior performance in CA prediction while capturing both short- and long-term dependencies in patient data, potentially improving patient outcomes and guiding clinical decision-making.
Authors
- Abdulla Al‐Ansari (ORCID: https://orcid.org/0000-0002-6242-5667)
- Afaf K. Hamze
- Sarada Prasad Dakua (ORCID: https://orcid.org/0000-0003-2979-0272)
- Santu Paul
- Osama Halabi (ORCID: https://orcid.org/0000-0002-2052-0500)
- Jassim Zaheen Shah (ORCID: https://orcid.org/0009-0004-8210-1282)
- Mohammed Yusuf Ansari (ORCID: https://orcid.org/0000-0001-6123-3893)
- Omar M. Aboumarzouk (ORCID: https://orcid.org/0000-0002-7961-7614)
- Iffa Afsa Changaai Mangalote (ORCID: https://orcid.org/0009-0005-1643-5337)
- Ezzedin Alataresh
Institutions
- Hamad Medical Corporation (QA)
- Qatar University (QA)
- Texas A&M University (US)
Publication Details
- Journal
- IEEE Transactions on Biomedical Engineering
- Published
- 2024-12-18
- DOI
- https://doi.org/10.1109/tbme.2024.3517635
- Citations
- 26
- Primary Topic
- ECG Monitoring and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 10.45