Transformer Models Enable Accurate Age Prediction From Sleep Physiology

Biological age estimation based on physiological signatures such as brain activity has emerged as a valuable biomarker; the gap between an individuals predicted biological age and their chronological age is increasingly being linked to diverse health and cognitive outcomes. This study explores polysomnographic (PSG) recordings of sleep to evaluate how well diverse physiological signals--including EEG, ECG, EOG, and EMG--can support accurate age estimation, both individually and in combination. PSG serves as an ideal platform for age estimation due to its standardized data collection protocols, abundant public data resources, and its capture of well-documented age-related changes in sleep architecture that also relate to chronic health outcomes. Accordingly, we trained transformer-based neural network models on over 10,000 nights of public PSG data and performed rigorous internal and external validation. The best models achieved age estimates with an absolute error of 5-10 years on held out data from a single channel of electroencephalography (EEG). Interestingly, accuracy in these models was stage-dependent, with higher concentrations of N2 sleep in a given input sequence yielding more precise estimates than sequences dominated by other sleep stages. Age overestimation was associated with worse depression and cognitive performance outcomes. Electrocardiogram (ECG) signals, although less accurate overall, tended to overestimate age in association with health conditions such as elevated blood pressure, higher body mass index, and sleep apnea. Despite strong performance, generalization to external data remains a challenge (age estimation errors increase between internal validation and external data by at least 3 to 5 years). These findings show that non-invasive sleep-derived electrophysiological signals, particularly EEG, can support age estimation with accuracy comparable to functional MRI-based (5 to 11 years estimation error), yet at a fraction of the cost. The proliferation of consumer sleep monitoring devices, coupled with these highly accurate electrophysiological models, makes population-scale, at-home assessment of biological brain aging increasingly feasible.1