Paperdoi:10.1016/j.hroo.2026.08.003
The wearable AI-PPG AF burden: a passive, dynamic burden tool with an ECG-distilled deep foundation model (HRS 2026 abstract)
Heart Rhythm O2 7(9) suppl., meeting abstract ID 4569904 (2026-09-01)
Licence: CC BY-NC-ND (OpenAlex)
TL;DR
Meeting abstract. Retrievable text gives the motivation only: AF management is moving from binary detection to AF burden (guides anticoagulation duration and post-ablation surveillance); consumer watches collect continuous PPG plus intermittent ECG but report only binary alerts, and no deployed method yields a continuous ECG-validated AF burden. The title states the tool is an ECG-distilled deep foundation model applied to watch PPG.
- Form factor: wrist
Abstract
AF management is shifting from binary detection to AF burden, which guides anticoagulation duration and post-ablation surveillance. Consumer watches collect continuous PPG and intermittent ECG but report only binary alerts; no deployed method yields a continuous, ECG-validated AF burden.
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APA
Liu, Y., Darzi, A. E., Chung, Y., Massad, C. M., Jia, Y., Khoury, C. E., Atasi, M. M., Khalil, M. A., Menassa, Y., Hassan, A., Zhao, C., Liu, K., et al. (2026). The wearable AI-PPG AF burden: a passive, dynamic burden tool with an ECG-distilled deep foundation model (HRS 2026 abstract). Heart Rhythm O2 7 suppl., meeting abstract ID 4569904. https://doi.org/10.1016/j.hroo.2026.08.003
BibTeX
@article{liu2026wearable,
title = {{The wearable AI-PPG AF burden: a passive, dynamic burden tool with an ECG-distilled deep foundation model (HRS 2026 abstract)}},
author = {Liu, Yingshuo and Darzi, Alex El and Chung, Yunsung and Massad, Christian M. and Jia, Yishi and Khoury, Carlo El and Atasi, Mohammad Montaser and Khalil, Michel Abou and Menassa, Yara and Hassan, Abboud and Zhao, Cong and Liu, Kexin and others},
year = {2026},
journal = {Heart Rhythm O2 7 suppl., meeting abstract ID 4569904},
doi = {10.1016/j.hroo.2026.08.003}
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