Learning individual autonomic representations of sleep stages to improve photoplethysmography based sleep monitoring.

Abstract

Objective.Wrist-worn photoplethysmography (PPG) enables scalable, long-term unobtrusive sleep monitoring through the expression of sympathetic and parasympathetic activity in heart rate variability. However, interindividual differences in the sympatho-vagal balance may inherently limited general PPG-based sleep staging models. This study investigates whether learning individual autonomic representations through model personalization can improve PPG-based automated sleep staging performance.Approach.Concurrent wrist-worn PPG and wearable electroencephalography (EEG) were collected during home monitoring for up to seven nights in a heterogeneous sleep-disordered population (n= 59). Personalization was performed through finetuning (i.e. partial retraining) a general PPG-based model by coupling the subject-specific PPG data with the wearable EEG stage classifications. Performance of the general and personalized models were compared on PPG acquired during a gold-standard clinical polysomnography, testing their agreement on 4-stage classification (W/N1+N2/N3/REM) with the manual scoring.Main result.Overall performance increased in 82.5% of the subjects, with significantly improved performance reached when personalizing the model on three or more training nights. Performance increased with personalization on additional training nights for each stage: wake (β= .005,p< .001), N1+N2 (β= .003,p< .001), N3 (β= .004,p< .001), and REM (β= .005,p< .001). Effects were strongest for younger individuals (β= .009,p< .001) and patients with insomnia (β= .011,p< .001). Personalization greatly impacted the derived sleep macrostructural sleep parameters, with considerable improvement in N3 sleep classification, and in capturing rapid eye movement (REM) sleep fragmentation.Significance.Personalization can overcome one-size-fits-all limitations of a general model and should be considered for PPG-based sleep staging when an altered autonomic modulation is expected that deviates from the general model's global representation.