  Atrial fibrillation (AF) is the most prevalent arrhythmia and is associated
with a five-fold increase in stroke risk. Many individuals with AF go
undetected. These individuals are often asymptomatic. There are ongoing debates
on whether mass screening for AF is to be recommended. However, there is
incentive in performing screening for specific at risk groups such as
individuals suspected of sleep-disordered breathing where an important
association between AF and obstructive sleep apnea (OSA) has been demonstrated.
We introduce a new methodology leveraging digital biomarkers and recent
advances in artificial intelligence (AI) for the purpose of mass AF diagnosis.
We demonstrate the value of such methodology in a large population sample at
risk of sleep disordered breathing. Four databases, totaling n=3,088 patients
and p=26,913 hours of ECG raw data were used. Three of the databases (n=125,
p=2,513) were used for training a machine learning model in recognizing AF
events from beat-to-beat interval time series. The visit 1 of the sleep heart
health study database (SHHS1, n=2,963, p=24,400) consists of overnight
polysomnographic (PSG) recordings, and was considered as the test set. In
SHHS1, expert inspection identified a total of 70 patients with a prominent AF
rhythm. Model prediction on the SHHS1 showed an overall
Se=0.97,Sp=0.99,NPV=0.99,PPV=0.67 in classifying individuals with or without
prominent AF. PPV was non-inferior (p=0.03) for individuals with an
apnea-hypopnea index (AHI) > 15 versus AHI < 15. Over 22% of correctly
identified prominent AF rhythm cases were not documented as AF in the SHHS1.
Individuals with prominent AF can be automatically diagnosed from an overnight
single channel ECG recording, with an accuracy unaffected by the presence of
OSA. AF detection from overnight ECG recording revealed a large proportion of
undiagnosed AF and may enhance the phenotyping of OSA.
