Deep learning of awake and sleep electrocardiography to identify atrial fibrillation risk in sleep apnea
Description: Project Narrative We will investigate whether heart rate response, a novel marker of obstructive sleep apnea (OSA) severity, improves risk prediction of incident AF. We will also develop a deep learning model incorporating awake and sleep electrocardiography to predict incident AF among people with OSA. Both observational cohort and clinical data will be used for this purpose.
Funding type: research
Funder scheme: R21
Start date: 2023-01-15
End date: 2025-12-31
Investigator: OGUZ AKBILGIC
Institution: UNIVERSITY OF WASHINGTON
Subfield: Physiology
Field: Medicine
Domain: Health Sciences
Funded outputs count: 24
Funder award ID: R21HL167126
OpenAlex ID: G3052515844
