Morphology Features Self-Learned by Explainable Deep Learning for Atrial Fibrillation Detection Correspond to Fibrillatory Waves
- DOI
- 10.22489/cinc.2024.305
- Published
- 2024-12-01
- Container
- Computing in Cardiology Conference (CinC)
- Publisher
- Computing in Cardiology
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.22489/cinc.2024.305,
title = {Morphology Features Self-Learned by Explainable Deep Learning for Atrial Fibrillation Detection Correspond to Fibrillatory Waves},
author = {Alexander Hammer and Hagen Malberg and Martin Schmidt},
year = {2024},
journal = {Computing in Cardiology Conference (CinC)},
doi = {10.22489/cinc.2024.305},
url = {https://doi.org/10.22489/cinc.2024.305}
}RIS
TY - JOUR TI - Morphology Features Self-Learned by Explainable Deep Learning for Atrial Fibrillation Detection Correspond to Fibrillatory Waves AU - Alexander Hammer AU - Hagen Malberg AU - Martin Schmidt PY - 2024 JO - Computing in Cardiology Conference (CinC) DO - 10.22489/cinc.2024.305 UR - https://doi.org/10.22489/cinc.2024.305 ER -
APA
Hammer, A., Malberg, H., & Schmidt, M. (2024). Morphology Features Self-Learned by Explainable Deep Learning for Atrial Fibrillation Detection Correspond to Fibrillatory Waves. Computing in Cardiology Conference (CinC). https://doi.org/10.22489/cinc.2024.305
Source records
- crossref · retrieved 2026-09-26T00:30:53.804Z