Performance evaluation of a machine learning-based methodology using dynamical features to detect nonwear intervals in actigraphy data in a free-living setting

dc.contributor.authorNirupam Das, Jyotirmoy
dc.contributor.authorJi, Linying
dc.contributor.authorShen, Yuqi
dc.contributor.authorKumara, Soundar
dc.contributor.authorBuxton, Orfeu M.
dc.contributor.authorChow, Sy-Miin
dc.date.accessioned2025-03-24T21:05:22Z
dc.date.issued2025-01
dc.description.abstractGoal and aims. One challenge using wearable sensors is nonwear time. Without a nonwear (e.g., capacitive) sensor, actigraphy data quality can be biased by subjective determinations confounding sleep/wake classification. We developed and evaluated a machine learning algorithm supplemented by dynamic features to discern wear/nonwear episodes. Focus technology. Actigraphy data from wrist actigraph (Spectrum, Philips-Respironics). Reference technology. The built-in nonwear sensor as “ground truth” to classify nonwear periods using other data, mimicking features of Actiwatch 2. Sample. Data were collected over 1 week from employed adults (n = 853). Design. Extreme gradient boosting (XGBoost), a tree-based classifier algorithm, was used to classify wear/nonwear, supplemented by dynamic features calculated over various time windows. Core analytics. The performance of the proposed algorithm was tested over 30-second epochs. Additional analytics and exploratory analyses: Evaluation of the SHapley Additive exPlanations (SHAP) values to find the effectiveness of the dynamic features. Core outcomes. The XGBoost classifier yielded substantial improvements in balanced accuracy, sensitivity, and specificity, including dynamic features and comparison to default actiwatch classification algorithms. Important supplemental outcomes. The proposed classifier effectively distinguished between valid and invalid days, and the duration of contiguous periods of nonwear correctly identified. Core conclusion. Our findings highlight the potential of XGBoost using dynamic features of varying activity levels across the time series to provide insights on wear/nonwear classification using a large dataset. The methodology provides an alternative to laborious manual benchmarking of the data for similar devices that do not have a nonwear sensor.
dc.identifier.doi10.1016/j.sleh.2024.10.003
dc.identifier.issn2352-7218
dc.identifier.urihttps://scholarworks.montana.edu/handle/1/19207
dc.language.isoen_US
dc.publisherElsevier BV
dc.rightscc-by-nc-nd
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectNonwear detection
dc.subjectMachine learning
dc.subjectActigrpahy
dc.subjectSleep
dc.titlePerformance evaluation of a machine learning-based methodology using dynamical features to detect nonwear intervals in actigraphy data in a free-living setting
dc.typeArticle
mus.citation.extentfirstpage1
mus.citation.extentlastpage8
mus.citation.journaltitleSleep Health
mus.relation.collegeCollege of Letters & Science
mus.relation.departmentPsychology
mus.relation.universityMontana State University - Bozeman

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