An Efficient Segmentation Algorithm to Estimate Sleep Duration from Actigraphy Data

Jonggyu Baek, Magaret Banker, Erica C. Jansen, Xichen She, Karen E. Peterson, E. Andrew Pitchford and Peter X. K. Song ()
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Jonggyu Baek: University of Massachusetts Medical School
Magaret Banker: University of Michigan
Erica C. Jansen: University of Michigan
Xichen She: University of Michigan
Karen E. Peterson: University of Michigan
E. Andrew Pitchford: Iowa State University
Peter X. K. Song: University of Michigan

Statistics in Biosciences, 2021, vol. 13, issue 3, No 10, 563-583

Abstract: Abstract Sleep duration is a recognized determinant of mental health, obesity and cardiovascular disease, cognition, and memory across the lifespan. Due to convenience and cost, sleep duration is often measured through self-report; yet, self-reported sleep duration can be highly biased. Actigraphy is a viable alternative as an objective measure of sleep. To analyze this actigraphy data, various sleep evaluation algorithms have been developed using regression methods, with coefficients constructed on minute-by-minute data measured at a specific device placement (wrist or hip). Because activity counts per minute may be affected by various factors in the study (e.g., type of device, sampling frequencies), regression-based algorithms developed within specific populations may not be generalizable to wider use. To address these concerns, we propose a new learning method to obtain robust and consistent sleep duration estimates. First, we identify temporal segments via pruned dynamic programming; then, we develop a calling algorithm with individual-specific thresholds and capture sleep periods. Our proposed method is motivated by and demonstrated in the Multi-Ethnic Study of Atherosclerosis (MESA) Sleep study and the Early Life Exposure in Mexico to ENvironmental Toxicants (ELEMENT) study.

Keywords: Actigraphy; Change-point; Pruned dynamic programming; Sleep duration (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s12561-021-09309-3

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