Beyond traditional visual sleep scoring: massive feature extraction and unsupervised clustering of sleep time series
Decat, N.; Walter, J.; Koh, Z. H.; Sribanditmongkol, P.; Fulcher, B. D.; Windt, J. M.; Andrillon, T.; Tsuchiya, N.
Show abstract
Sleep is classically measured with electrophysiological recordings, which are then scored based on guidelines tailored for the visual inspection of these recordings. As such, these rules reflect a limited range of features easily captured by the human eye and do not always reflect the physiological changes associated with sleep. Here we present a novel analysis framework that characterizes sleep using over 7700 time-series features from the hctsa software. We used clustering to categorize sleep epochs based on the similarity of their features, without relying on established scoring conventions. The resulting structure overlapped substantially with that defined by visual scoring and we report novel features that are highly discriminative of sleep stages. However, we also observed discrepancies as hctsa features unraveled distinctive properties within traditional sleep stages. Our framework lays the groundwork for a data-driven exploration of sleep and the identification of new signatures of sleep disorders and conscious sleep states.
Matching journals
The top 8 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Somnotate: A probabilistic sleep stage classifier for studying vigilance state transitions 97%
- Circadian distribution of epileptiform discharges in epilepsy: candidate mechanisms of variability 94%
- Perturbations in dynamical models of whole-brain activity dissociate between the level and stability of consciousness 94%
Similar papers in this journal
- A foundational transformer leveraging full night, multichannel sleep study data accurately classifies sleep stages 96%
- The Aging Slow Wave: A Shifting Amalgam of Distinct Slow Wave and Spindle Coupling Subtypes Define Slow Wave Sleep Across the Human Lifespan 96%
- Corticothalamic modelling of sleep neurophysiology with applications to mobile EEG 96%
Similar papers in this journal
- Decreased electrocortical temporal complexity distinguishes sleep from wakefulness 96%
- Topographical relocation of adolescent sleep spindles reveals a new maturational pattern of the human brain 95%
- Multi-channel recordings reveal age-related differences in the sleep of juvenile and adult zebra finches 95%
Similar papers in this journal
Similar papers in this journal
- The Rise and Fall of Slow Wave Tides: Vacillations of Slow Wave/Spindle Coupling Shift the Composition of Slow Wave Activity Through Sleep Cycles in Accordance with Depth of Sleep 96%
- Night watch during REM sleep for the first-night effect 94%
- Simultaneous electrophysiological recording and fiber photometry in freely behaving mice 93%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.