Machine learning for nocturnal mass diagnosis of atrial fibrillation in a population at risk of sleep-disordered breathing.

Objective: In this research, we introduce a new methodology for atrial fibrillation (AF) diagnosis during sleep in a large population sample at risk of sleep-disordered breathing. Approach: The approach leverages digital biomarkers and recent advances in machine learning (ML) for mass AF diagnosis from overnight-hours of single-channel electrocardiogram (ECG) recording. Four databases, totaling n = 3088 patients and p = 26 913 h of continuous single-channel electrocardiogram raw data were used. Three of the databases (n = 125, p = 2513) were used for training a ML model in recognizing AF events from beat-to-beat time series. Visit 1 of the sleep heart health study database (SHHS1, n = 2963, p = 24 400) was used as the test set to evaluate the feasibility of identifying prominent AF from polysomnographic recordings. By combining AF diagnosis history and a cardiologist's visual inspection of individuals suspected of having AF (n = 118), a total of 70 patients were diagnosed with prominent AF in SHHS1. Main results: Model prediction on SHHS1 showed an overall Se = 0.97, Sp = 0.99, NPV = 0.99 and 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 previously documented as AF in SHHS1. Significance: Individuals with prominent AF can be automatically diagnosed from an overnight single-channel ECG recording, with an accuracy unaffected by the presence of moderate-to-severe obstructive sleep apnea. This approach enables identifying a large proportion of AF individuals that were otherwise missed by regular care.