0265 Validation Framework for Sleep Stage Scoring in Wearable Sleep Trackers and Monitors with Polysomnography Ground Truth

Abstract

Abstract Introduction The rapid growth of point-of-care polysomnographic alternatives has necessitated standardized evaluation and validation frameworks. The current average across participant validation methods may overestimate the agreement between wearable sleep tracker devices and polysomnography (PSG) systems because of the high base rate of sleep during the night and the interindividual difference across the sampling population. Methods This study proposes an evaluation framework to assess the aggregating differences of the sleep architecture features and the chronologically epoch-by-epoch mismatch of the wearable sleep tracker devices and the PSG ground truth. An AASM-based sleep stage categorizing method was proposed to standardize the sleep stages scored by different types of wearable trackers. Sleep features and sleep stage architecture were extracted from the PSG and the wearable device’s hypnograms. Therefrom, a localized quantifier index was developed to characterize the local mismatch of sleep scoring. Results We evaluated different commonly used wearable sleep tracking devices with the data collected from 22 different subjects over 30 nights of 8-h sleeping. The proposed localization quantifiers can characterize the chronologically localized mismatches over sleeping time. The proposed framework not only characterizes the difference in the sleep stage distribution but also quantifies the chronologically localized mismatches over the sleeping time. Three major components of the framework consist of AASM-based sleep stage classification, sleep staging distribution analysis, and localized comparison analysis. The outperformance of the proposed method over existing evaluation methods was reported. Conclusion The proposed research extends our earlier work on evaluating commercial sleep trackers compared to PSG. The proposed evaluation method can be utilized to improve the sensor design and scoring algorithm. Support (if any) Nguyen, Quyen NT, et al. "Validation Framework for Sleep Stage Scoring in Wearable Sleep Trackers and Monitors with Polysomnography Ground Truth." Clocks & sleep 3.2 (2021): 274-288