Prediction of cardiovascular outcomes using electrocardiography from overnight polysomnography

Polysomnography (PSG) is the gold standard for diagnosing sleep disorders, which are recognized as cardiovascular risk factors. Single-lead electrocardiograms (ECGs) are recorded during PSG but rarely analyzed, presenting an opportunity to leverage overnight ECGs for cardiovascular risk prediction.

We applied a deep residual neural network with attention to single-lead ECGs from PSG, combined with sleep stage data, to predict 10-year risk of atrial fibrillation (AF), stroke, myocardial infarction (MI), heart failure (HF), and death. The network, trained for arrhythmia detection, was finetuned on 15 809 Massachusetts General Hospital patients. External validation used cohorts from Emory University Hospital (EUH) (n = 9810) and Beth Israel Deaconess Medical Center (BIDMC) (n = 12 576), with outcomes derived from electronic health records (ICD-9/10 codes).

Cox proportional hazards models showed that neural-network output (NN-output) was strongly associated with long-term cardiovascular risk (p < .0001), even after adjusting for risk factors (age, sex, body mass index, smoking, hypertension, diabetes) and sleep characteristics (apnea-hypopnea-index, arousal-index, periodic limb movement index, time spend in N1-N3, REM and sleep efficiency). A one-standard deviation increase in NN-output was associated with elevated hazard ratios (95% CI): AF 2.03 (1.82-2.27) (EUH) and 2.72 (2.31-3.21) (BIDMC); stroke 1.19 (1.09-1.31) and 1.40 (1.12-1.74); MI 1.38 (1.22-1.55) and 1.10 (0.86-1.40); HF 1.69 (1.55-1.85) and 2.33 (2.05-2.65); all-cause mortality 1.82 (1.60-2.06) and 1.65 (1.41-1.93).

Deep learning applied to nocturnal ECGs with sleep stage information captures cardiac abnormalities, offering a tool for cardiovascular risk stratification. Integrating such prediction into PSG could enhance detection and support personalized clinical decision-making.