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An unbiased, efficient sleep-wake detection algorithm for a population with sleep disorders: change point decoder.
Cakmak, Ayse S; Da Poian, Giulia; Willats, Adam; Haffar, Ammer; Abdulbaki, Rami; Ko, Yi-An; Shah, Amit J; Vaccarino, Viola; Bliwise, Donald L; Rozell, Christopher; Clifford, Gari D.
Afiliação
  • Cakmak AS; School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA.
  • Da Poian G; Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA, USA.
  • Willats A; Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA.
  • Haffar A; Rollins School of Public Health, Emory University, Atlanta, GA, USA.
  • Abdulbaki R; Rollins School of Public Health, Emory University, Atlanta, GA, USA.
  • Ko YA; Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA, USA.
  • Shah AJ; Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA, USA.
  • Vaccarino V; Department of Medicine, Emory University School of Medicine, Atlanta, GA, USA.
  • Bliwise DL; Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA, USA.
  • Rozell C; Department of Medicine, Emory University School of Medicine, Atlanta, GA, USA.
  • Clifford GD; Department of Neurology, Emory University School of Medicine, Atlanta, GA, USA.
Sleep ; 43(8)2020 08 12.
Article em En | MEDLINE | ID: mdl-32006429
ABSTRACT
STUDY

OBJECTIVES:

The usage of wrist-worn wearables to detect sleep-wake states remains a formidable challenge, particularly among individuals with disordered sleep. We developed a novel and unbiased data-driven method for the detection of sleep-wake and compared its performance with the well-established Oakley algorithm (OA) relative to polysomnography (PSG) in elderly men with disordered sleep.

METHODS:

Overnight in-lab PSG from 102 participants was compared with accelerometry and photoplethysmography simultaneously collected with a wearable device (Empatica E4). A binary segmentation algorithm was used to detect change points in these signals. A model that estimates sleep or wake states given the changes in these signals was established (change point decoder, CPD). The CPD's performance was compared with the performance of the OA in relation to PSG.

RESULTS:

On the testing set, OA provided sleep accuracy of 0.85, wake accuracy of 0.54, AUC of 0.67, and Kappa of 0.39. Comparable values for CPD were 0.70, 0.74, 0.78, and 0.40. The CPD method had sleep onset latency error of -22.9 min, sleep efficiency error of 2.09%, and underestimated the number of sleep-wake transitions with an error of 64.4. The OA method's performance was 28.6 min, -0.03%, and -17.2, respectively.

CONCLUSIONS:

The CPD aggregates information from both cardiac and motion signals for state determination as well as the cross-dimensional influences from these domains. Therefore, CPD classification achieved balanced performance and higher AUC, despite underestimating sleep-wake transitions. The CPD could be used as an alternate framework to investigate sleep-wake dynamics within the conventional time frame of 30-s epochs.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Transtornos do Sono-Vigília / Actigrafia Idioma: En Ano de publicação: 2020 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Transtornos do Sono-Vigília / Actigrafia Idioma: En Ano de publicação: 2020 Tipo de documento: Article