Beyond traditional sleep scoring: Massive feature extraction and data-driven clustering of sleep time series

The widely used guidelines for sleep staging were developed for the visual inspection of electrophysi-ological recordings by the human eye. As such, these rules reflect a limited range of features in these data and are therefore restricted in accurately capturing the physiological changes associated with sleep. Here we present a novel analysis framework that extensively characterizes sleep dynamics using over 7700 time-series features from the hctsa software. We used clustering to categorize sleep epochs based on the similarity of their time-series features, without relying on established scoring conventions. The resulting sleep structure overlapped substantially with that defined by visual scoring. However, we also observed discrepancies between our approach and traditional scoring. This divergence principally stemmed from the extensive characterization by hctsa features, which captured distinctive time-series properties within the traditionally defined sleep stages that are overlooked with visual scoring. Lastly, we report time -series features that are highly discriminative of stages. Our framework lays the groundwork for a data-driven exploration of sleep sub-stages and has significant potential to identify new signatures of sleep disorders and conscious sleep states. (c) 2022 Elsevier B.V. All rights reserved.

Automated summary

The widely used guidelines for sleep staging have limitations in accurately capturing the physiological changes associated with sleep. In this study, a novel analysis framework was developed to extensively characterize sleep dynamics using over 7700 time-series features. The results showed significant overlap between the defined sleep structure by the new approach and the traditional visual scoring. However, discrepancies were observed due to the extensive characterization that captured distinctive time-series properties within traditionally defined sleep stages.