A Comprehensive Study of Complexity and Performance of Automatic Detection of Atrial Fibrillation: Classification of Long ECG Recordings Based on the PhysioNet Computing in Cardiology Challenge 2017.
- DOI
- 10.1088/2057-1976/ab6e1e
- Published
- 2020 Feb 18
- Container
- Biomedical physics & engineering express
- Publisher
- Not recorded
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1088/2057-1976/ab6e1e,
title = {A Comprehensive Study of Complexity and Performance of Automatic Detection of Atrial Fibrillation: Classification of Long ECG Recordings Based on the PhysioNet Computing in Cardiology Challenge 2017.},
author = {Kleyko D and Osipov E and Wiklund U},
year = {2020},
journal = {Biomedical physics \& engineering express},
doi = {10.1088/2057-1976/ab6e1e},
url = {https://doi.org/10.1088/2057-1976/ab6e1e}
}RIS
TY - JOUR TI - A Comprehensive Study of Complexity and Performance of Automatic Detection of Atrial Fibrillation: Classification of Long ECG Recordings Based on the PhysioNet Computing in Cardiology Challenge 2017. AU - Kleyko D AU - Osipov E AU - Wiklund U PY - 2020 JO - Biomedical physics & engineering express DO - 10.1088/2057-1976/ab6e1e UR - https://doi.org/10.1088/2057-1976/ab6e1e ER -
APA
D, K., E, O., & U, W. (2020). A Comprehensive Study of Complexity and Performance of Automatic Detection of Atrial Fibrillation: Classification of Long ECG Recordings Based on the PhysioNet Computing in Cardiology Challenge 2017.. Biomedical physics & engineering express. https://doi.org/10.1088/2057-1976/ab6e1e
Source records
- pubmed · retrieved 2026-09-26T01:48:09.480Z