A Novel Deep Ensemble Method for Selective Classification of Electrocardiograms
These findings highlight the potential for improving the utility of telehealth systems by integrating advanced CDSSs capable of managing uncertainty and ensuring higher accuracy, thereby improving patient outcomes in remote healthcare settings.
Authors
- G. Schreier (ORCID: https://orcid.org/0000-0003-3724-4255)
- Branko G. Celler (ORCID: https://orcid.org/0000-0003-3790-2895)
- Stephen J. Redmond (ORCID: https://orcid.org/0000-0002-2630-5449)
- Martin Baumgartner (ORCID: https://orcid.org/0000-0001-9539-7204)
- Hamid Alinejad‐Rokny (ORCID: https://orcid.org/0000-0002-2189-9153)
- Ahmadreza Argha (ORCID: https://orcid.org/0000-0002-8276-9774)
- Sze‐Yuan Ooi (ORCID: https://orcid.org/0000-0001-6916-7958)
- Nigel Hamilton Lovell (ORCID: https://orcid.org/0000-0003-1637-1079)
- Kenneth Butcher (ORCID: https://orcid.org/0000-0002-0590-7918)
Institutions
- University College Dublin (IE)
- AIT Austrian Institute of Technology GmbH (AT)
- UNSW Sydney (AU)
- Prince of Wales Hospital (AU)
Publication Details
- Journal
- IEEE Transactions on Biomedical Engineering
- Published
- 2024-10-08
- DOI
- https://doi.org/10.1109/tbme.2024.3476088
- Citations
- 6
- Primary Topic
- ECG Monitoring and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 2.41