Beyond ECG: Robust Deep Learning-enabled Atrial Fibrillation Detection in HSATs with PPG

Introduction

Atrial fibrillation (AFib) is a prevalent and often underdiagnosed comorbidity in patients with obstructive sleep apnea (OSA). Photoplethysmography (PPG) is widely available in PSG and home sleep apnea tests (HSAT), yet detection of AFib and atrial flutter (AFL) typically relies on electrocardiography (ECG). We developed and evaluated a deep learning model to detect AF events (AFib and AFL combined) from PPG signals, offering a scalable solution to implement high-accuracy, routine detection within large clinical sleep cohorts.

Methods

A total of N=10,767 studies with simultaneous ECG and PPG were randomly sampled from a large clinical PSG/HSAT database. Sampling was stratified uniformly across AFib burden to construct balanced training (N=7,752), validation (N=1,938), and test (N=1,077) datasets. AFib burden was estimated using a validated open-source ECG model and defined as the percentage of segments classified as AFib. Definitive AF labels were derived from a validated ECG analysis algorithm applied to a single-lead ECG II. A deep learning model was trained exclusively on PPG signals, and performance was evaluated on a per-epoch (30-second) basis on the test set. A total of 67,023 (7.5%) of the 30-second epochs in the test sample group were excluded due to insufficient PPG signal quality or artifacts.

Results

Conclusions

The deep learning PPG model demonstrated robust detection of AFib, achieving performance comparable to the gold-standard of ECG for AFib diagnosis. These findings highlight the potential to integrate automated AF detection into existing HSAT and PSG workflows, enabling scalable, high-accuracy arrhythmia screening in patients undergoing sleep studies without the need for additional hardware.