Bayesian Model Search for Nonstationary Periodic Time Series.
Bayesian spectral analysis
Change-points
Reversible-jump MCMC
Sleep apnea
Ultradian sleep cycles
Journal
Journal of the American Statistical Association
ISSN: 0162-1459
Titre abrégé: J Am Stat Assoc
Pays: United States
ID NLM: 01510020R
Informations de publication
Date de publication:
09 Jul 2019
09 Jul 2019
Historique:
entrez:
5
4
2021
pubmed:
9
7
2019
medline:
9
7
2019
Statut:
epublish
Résumé
We propose a novel Bayesian methodology for analyzing nonstationary time series that exhibit oscillatory behavior. We approximate the time series using a piecewise oscillatory model with unknown periodicities, where our goal is to estimate the change-points while simultaneously identifying the potentially changing periodicities in the data. Our proposed methodology is based on a trans-dimensional Markov chain Monte Carlo algorithm that simultaneously updates the change-points and the periodicities relevant to any segment between them. We show that the proposed methodology successfully identifies time changing oscillatory behavior in two applications which are relevant to e-Health and sleep research, namely the occurrence of ultradian oscillations in human skin temperature during the time of night rest, and the detection of instances of sleep apnea in plethysmographic respiratory traces. Supplementary materials for this article are available online.
Identifiants
pubmed: 33814652
doi: 10.1080/01621459.2019.1623043
pii: 1623043
pmc: PMC7984273
doi:
Types de publication
Journal Article
Langues
eng
Pagination
1320-1335Subventions
Organisme : Medical Research Council
ID : MR/M013170/1
Pays : United Kingdom
Informations de copyright
© 2019 The Author(s). Published with license by Taylor & Francis Group, LLC.
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