Universal approach to actigraphic sleep/wake scoring, verified against 5 classic algorithms on 3 datasets

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dc.abstract.enActigraphy is a non-invasive and inexpensive method to monitor sleep/wake patterns in a natural environment via a wrist-worn activity sensor. Traditionally, detection of sleep/wake periods from actigraphic data relies on smoothing and thresholding the time series of recorded “activity counts”. The first step is implemented by convolution with empirically chosen coefficients, tailored separately for the data and hardware used in each study. We propose to implement this step via a universal low-pass filter, applicable to wide ranges of recording hardware and sampling rates. For verification of this approach, we used 1635 overnight coregistrations of actigraphic and polysomnographic (PSG) data from three different datasets, including one dataset recorded for this study. Optimizations of the filter for concordance of sleep/wake scoring with PSG for different subsets of these data converged to similar parameters, which we tentatively treat as fluctuations around the characteristics of a universal filter. We assess the performance of the proposed approach and five classic algorithms (Cole-Kripke, Sazonov, Scripps, UCSD and Webster) in the same cross-validation scheme. Concordance with PSG, achieved using the universal filter, is significantly higher (at p< 0.001 ) than any of the classical algorithms for the most relevant metrics.
dc.affiliationBehavioural Neuroscience Lab, Institute of Psychology / Wydział Psychologii w Warszawie / Instytut Psychologii
dc.affiliationWydział Psychologii w Warszawie
dc.affiliationInstytut Psychologii
dc.contributor.authorBiegański, Piotr
dc.contributor.authorDuszyk-Bogorodzka, Anna
dc.contributor.authorWołyńczyk-Gmaj, Dorota
dc.contributor.authorGmaj, Bartłomiej
dc.contributor.authorDurka, Piotr
dc.date.access2026-04-17
dc.date.accessioned2026-06-15T11:55:09Z
dc.date.available2026-06-15T11:55:09Z
dc.date.created2026
dc.date.issued2026-04-17
dc.description.abstract<jats:title>Abstract</jats:title> <jats:p> Actigraphy is a non-invasive and inexpensive method to monitor sleep/wake patterns in a natural environment via a wrist-worn activity sensor. Traditionally, detection of sleep/wake periods from actigraphic data relies on smoothing and thresholding the time series of recorded “activity counts”. The first step is implemented by convolution with empirically chosen coefficients, tailored separately for the data and hardware used in each study. We propose to implement this step via a universal low-pass filter, applicable to wide ranges of recording hardware and sampling rates. For verification of this approach, we used 1635 overnight coregistrations of actigraphic and polysomnographic (PSG) data from three different datasets, including one dataset recorded for this study. Optimizations of the filter for concordance of sleep/wake scoring with PSG for different subsets of these data converged to similar parameters, which we tentatively treat as fluctuations around the characteristics of a universal filter. We assess the performance of the proposed approach and five classic algorithms (Cole-Kripke, Sazonov, Scripps, UCSD and Webster) in the same cross-validation scheme. Concordance with PSG, achieved using the universal filter, is significantly higher (at <jats:inline-formula> <jats:tex-math>$$p &lt; 0.001$$</jats:tex-math> </jats:inline-formula> ) than any of the classical algorithms for the most relevant metrics. </jats:p>
dc.description.accesstimeat_publication
dc.description.issue1
dc.description.physical1-9
dc.description.sdgGoodHealthAndWellBeing
dc.description.versionfinal_published
dc.description.volume16
dc.identifier.doi10.1038/s41598-026-45568-0
dc.identifier.issn2045-2322
dc.identifier.urihttps://share.swps.edu.pl/handle/swps/2409
dc.identifier.weblinkhttps://www.nature.com/articles/s41598-026-45568-0
dc.languageen
dc.pbn.affiliationpsychologia
dc.rightsCC-BY
dc.rights.questionYes_rights
dc.share.articleOPEN_JOURNAL
dc.swps.sciencecloudsend
dc.titleUniversal approach to actigraphic sleep/wake scoring, verified against 5 classic algorithms on 3 datasets
dc.title.journalScientific Reports
dc.typeJournalArticle
dspace.entity.typeArticle