Machine learning of electrophysiological signals for the prediction of ventricular arrhythmias: systematic review and examination of heterogeneity between studies.
- DOI
- 10.1016/j.ebiom.2023.104462
- Published
- 2023 Mar
- Container
- EBioMedicine
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.1016/j.ebiom.2023.104462,
title = {Machine learning of electrophysiological signals for the prediction of ventricular arrhythmias: systematic review and examination of heterogeneity between studies.},
author = {Kolk MZH and Deb B and Ruipérez-Campillo S and Bhatia NK and Clopton P and Wilde AAM and Narayan SM and Knops RE and Tjong FVY},
year = {2023},
journal = {EBioMedicine},
doi = {10.1016/j.ebiom.2023.104462},
url = {https://doi.org/10.1016/j.ebiom.2023.104462}
}RIS
TY - JOUR TI - Machine learning of electrophysiological signals for the prediction of ventricular arrhythmias: systematic review and examination of heterogeneity between studies. AU - Kolk MZH AU - Deb B AU - Ruipérez-Campillo S AU - Bhatia NK AU - Clopton P AU - Wilde AAM AU - Narayan SM AU - Knops RE AU - Tjong FVY PY - 2023 JO - EBioMedicine DO - 10.1016/j.ebiom.2023.104462 UR - https://doi.org/10.1016/j.ebiom.2023.104462 ER -
APA
MZH, K., B, D., S, R., NK, B., P, C., AAM, W., SM, N., RE, K., & FVY, T. (2023). Machine learning of electrophysiological signals for the prediction of ventricular arrhythmias: systematic review and examination of heterogeneity between studies.. EBioMedicine. https://doi.org/10.1016/j.ebiom.2023.104462
Source records
- pubmed · retrieved 2026-09-27T06:25:40.227Z