Electrocardiogram sleep staging on par with expert polysomnography

Abstract

AbstractAccurate classification of sleep stages is crucial in sleep medicine and neuroscience research, providing valuable insights for diagnoses and understanding of brain states. The current gold standard for this task is polysomnography (PSG), an expensive and cumbersome process involving numerous electrodes, often performed in an unfamiliar clinic and professionally annotated. Although commercial devices like smartwatches track sleep, their performance compares poorly with PSG. To address this, we present a neural network that achieves gold-standard levels of agreement using a single lead of electrocardiogram (ECG) data (five-stage Cohen’s kappa = 0.725 on subjects 5 to 90 years old). Our method offers an inexpensive, automated, and convenient alternative. Cardiosomnography, or a sleep study conducted with electrocardiography only, could take expert-level sleep studies outside the confines of clinics and laboratories and into realistic settings. This would render higher-quality studies accessible to a broader community, enabling improved sleep research and sleep-related healthcare interventions.