What goes on when the lights go off? Using machine learning techniques to characterize a child’s settling down period

We present a novel use of machine learning techniques that successfully uncovered differentiating features within the settling down period for our groups. These differences have been difficult to capture using standard sleep and rest-activity metrics. Our data suggests that activity during the settling down period may be a unique target for future research for children with SS.

Authors

Amy G. Hartman (ORCID: https://orcid.org/0000-0003-0737-8375)
Murat Akcakaya
Adriane M. Soehner (ORCID: https://orcid.org/0000-0003-3172-1815)
Roxanna Bendixen (ORCID: https://orcid.org/0000-0002-7728-0624)
Deniz Kocanaogullari (ORCID: https://orcid.org/0000-0003-0493-2297)

Institutions

University of Pittsburgh (US)

Publication Details

Journal: Frontiers in Network Physiology
Published: 2025-05-30
DOI: https://doi.org/10.3389/fnetp.2025.1519407
Primary Topic: Sleep and Work-Related Fatigue
Type: article
Field-Weighted Citation Impact: 0.00

Funders

University of Pittsburgh
National Institutes of Health

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article

What goes on when the lights go off? Using machine learning techniques to characterize a child’s settling down period

Amy G. Hartman, Murat Akcakaya, Adriane M. Soehner, Roxanna Bendixen et al.

May 30, 2025

Frontiers in Network Physiology

Sleep and Work-Related Fatigue

article

What goes on when the lights go off? Using machine learning techniques to characterize a child’s settling down period

Amy G. Hartman, Murat Akcakaya, Adriane M. Soehner, Roxanna Bendixen, Deniz Kocanaogullari

May 30, 2025article en

Abstract

We present a novel use of machine learning techniques that successfully uncovered differentiating features within the settling down period for our groups. These differences have been difficult to capture using standard sleep and rest-activity metrics. Our data suggests that activity during the settling down period may be a unique target for future research for children with SS.

Frontiers in Network PhysiologyVol. 5

University of Pittsburgh (US)

University of Pittsburgh, National Institutes of Health

Openalex Percentile: Top 88%

Sleep and Work-Related Fatigue

DOI Open Access PDF OpenAlex

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