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In Physiological measurement ; h5-index 36.0

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=3,088 patients and p=26,913 hours of continuous single-channel electrocardiogram 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 time series. Visit 1 of the sleep heart health study database (SHHS1, n=2,963, 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 OSA. This approach enables identifying a large proportion of AF individuals that were otherwise missed by regular care.

Chocron Armand, Efraim Roi, Mandel Franck, Rueschman Michael, Palmius Niclas, Penzel Thomas, Elbaz Meyer, Behar Joachim


atrial fibrillation, digital biomarkers, machine learning, medicine during sleep, obstructive sleep apnea