# Deep learning of awake and sleep electrocardiography to identify atrial fibrillation risk in sleep apnea

> **NIH NIH R21** · UNIVERSITY OF WASHINGTON · 2024 · $92,786

## Abstract

Project Summary
Atrial fibrillation (AF) is the most common cardiac arrhythmia responsible for significant morbidity and mortality
burden. Obstructive sleep apnea (OSA) is a common sleep disorder but disproportionately more common in
patients with AF. OSA has been proposed as a risk for AF. However, clarifying the association between the
OSA and AF has been challenging due to many commonly shared risk factors such as obesity. No studies
have demonstrated whether information about OSA improves prediction of future risk of AF. In particular,
identifying who “among those with OSA” would be at risk for AF is unclear. Better identification of the group
most vulnerable to developing AF among those with OSA will inform clinicians and patients of critical
information needed for therapeutic decision making. One major challenge in OSA evaluation is that
conventional metrics used in the evaluation, such as the apnea hypopnea index (AHI) do not adequately
capture downstream cardiovascular (CV) responses. We and others have identified promising physiologically-
driven polysomnography (PSG) markers that better capture the severity of OSA and improve CV risk
stratification. Specifically related to AF, our preliminary study shows that heart rate response (HRR) to OSA
events, but not AHI, is associated with incident AF in community dwelling elderly men. Electrocardiography
(ECG) is a readily available diagnostic tool that captures electrical activity of the heart. Deep learning (DL) has
shown great promise in detection and risk prediction of various clinical outcomes including AF from `awake'
ECGs alone. `Sleep' ECG is affected by sleep state, respiration and particularly by pathological respiration
such as OSA events. Based on this, we propose Aim 1: To evaluate whether novel HRR-based OSA metrics
improves risk prediction of AF beyond the current AF risk prediction model. We will use a combined
prospective cohort of Atherosclerosis Risk in Communities Study (ARIC)-Sleep Heart Health Study (SHHS),
Cardiovascular Health Study (CHS)-SHHS and Multi-Ethnic Study of Atherosclerosis (MESA) (N~5000, AF
events~800). Aim 2: To develop and test the DL model using an awake ECG (10 sec 12 lead) and sleep ECG
(single lead) to predict a new onset AF in general population “with OSA”. We will develop a convolutional
neural network (CNN) model utilizing ARIC + CHS cohorts (combined N with OSA~1500, AF events ~400) and
externally validate in MESA cohort (OSA~1000, AF events ~100). The performance will be compared with the
CHARGE-AF risk prediction model. Aim 3: Same as Aim 2 except it will be the DL model in prediction of new
onset AF patients with OSA in clinical practice. Building upon the CNN model from Aim 2, we will develop a
separate CNN model using clinical ECG data from a single academic medical center (N= 2000, AF~200) that
may be more relevant in real world clinical practice. 50% of the dataset will be used for training and 50% for
validation. The findings of this study ...

## Key facts

- **NIH application ID:** 10758964
- **Project number:** 5R21HL167126-02
- **Recipient organization:** UNIVERSITY OF WASHINGTON
- **Principal Investigator:** Oguz Akbilgic
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $92,786
- **Award type:** 5
- **Project period:** 2023-01-15 → 2025-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10758964

## Citation

> US National Institutes of Health, RePORTER application 10758964, Deep learning of awake and sleep electrocardiography to identify atrial fibrillation risk in sleep apnea (5R21HL167126-02). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10758964. Licensed CC0.

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