4 Citations (Scopus)

Abstract

Sleep quality impacts virtually all aspects of life, including health, mood, emotions, cognition, memory, behavior, and performance. Actigraphy offers a lower-cost alternative to conventional polysomnography (PSG), the gold standard for measuring sleep quality. Effective use of actigraphy for assessing sleep quality requires reliable methods for detecting sleep/wake states from actigraphy measurements. Machine learning offers a promising approach to building sleep/wake state detectors from actigraphy data. However, current machine learning approaches rely on expert labeled training data that can be expensive and laborious to acquire. In this work, we introduce a novel approach for integrating unsupervised learning algorithms and domain knowledge heuristics, based on statistical properties of clustered sleep and wake epochs, to develop reliable sleep/wake state prediction models using unlabeled wrist actigraphy data. Experimental results using a dataset of 37 participants and covering 282 sleeping periods demonstrate the viability of the proposed approach on developing sleep/wake state detection models from unlabeled actigraphy data with a predictive performance that is comparable with the performance of models developed using some state-of-the-art supervised learning algorithms applied to labeled actigraphy data. Our results lay the groundwork for developing fully automated machine learning models for sleep/wake state prediction and sleep parameters estimations by eliminating the need for costly and labor-intensive expert annotations of PSG recordings for labeling actigraphy data.

Original languageEnglish (US)
Title of host publicationProceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017
EditorsIllhoi Yoo, Jane Huiru Zheng, Yang Gong, Xiaohua Tony Hu, Chi-Ren Shyu, Yana Bromberg, Jean Gao, Dmitry Korkin
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages718-723
Number of pages6
ISBN (Electronic)9781509030491
DOIs
StatePublished - Dec 15 2017
Event2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017 - Kansas City, United States
Duration: Nov 13 2017Nov 16 2017

Publication series

NameProceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017
Volume2017-January

Other

Other2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017
CountryUnited States
CityKansas City
Period11/13/1711/16/17

Fingerprint

Actigraphy
Parameter estimation
Cluster Analysis
Sleep
Learning systems
Polysomnography
Learning algorithms
Learning
Unsupervised learning
Supervised learning
Wrist
Labeling
Cognition
Emotions
Health
Personnel
Detectors
Data storage equipment
Costs and Cost Analysis

All Science Journal Classification (ASJC) codes

  • Biomedical Engineering
  • Health Informatics

Cite this

Elmanzalawi, Y., Buxton, O. M., & Honavar, V. G. (2017). Sleep/wake state prediction and sleep parameter estimation using unsupervised classification via clustering. In I. Yoo, J. H. Zheng, Y. Gong, X. T. Hu, C-R. Shyu, Y. Bromberg, J. Gao, ... D. Korkin (Eds.), Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017 (pp. 718-723). (Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017; Vol. 2017-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM.2017.8217742
Elmanzalawi, Yasser ; Buxton, Orfeu M. ; Honavar, Vasant Gajanan. / Sleep/wake state prediction and sleep parameter estimation using unsupervised classification via clustering. Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017. editor / Illhoi Yoo ; Jane Huiru Zheng ; Yang Gong ; Xiaohua Tony Hu ; Chi-Ren Shyu ; Yana Bromberg ; Jean Gao ; Dmitry Korkin. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 718-723 (Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017).
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abstract = "Sleep quality impacts virtually all aspects of life, including health, mood, emotions, cognition, memory, behavior, and performance. Actigraphy offers a lower-cost alternative to conventional polysomnography (PSG), the gold standard for measuring sleep quality. Effective use of actigraphy for assessing sleep quality requires reliable methods for detecting sleep/wake states from actigraphy measurements. Machine learning offers a promising approach to building sleep/wake state detectors from actigraphy data. However, current machine learning approaches rely on expert labeled training data that can be expensive and laborious to acquire. In this work, we introduce a novel approach for integrating unsupervised learning algorithms and domain knowledge heuristics, based on statistical properties of clustered sleep and wake epochs, to develop reliable sleep/wake state prediction models using unlabeled wrist actigraphy data. Experimental results using a dataset of 37 participants and covering 282 sleeping periods demonstrate the viability of the proposed approach on developing sleep/wake state detection models from unlabeled actigraphy data with a predictive performance that is comparable with the performance of models developed using some state-of-the-art supervised learning algorithms applied to labeled actigraphy data. Our results lay the groundwork for developing fully automated machine learning models for sleep/wake state prediction and sleep parameters estimations by eliminating the need for costly and labor-intensive expert annotations of PSG recordings for labeling actigraphy data.",
author = "Yasser Elmanzalawi and Buxton, {Orfeu M.} and Honavar, {Vasant Gajanan}",
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Elmanzalawi, Y, Buxton, OM & Honavar, VG 2017, Sleep/wake state prediction and sleep parameter estimation using unsupervised classification via clustering. in I Yoo, JH Zheng, Y Gong, XT Hu, C-R Shyu, Y Bromberg, J Gao & D Korkin (eds), Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017. Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017, vol. 2017-January, Institute of Electrical and Electronics Engineers Inc., pp. 718-723, 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017, Kansas City, United States, 11/13/17. https://doi.org/10.1109/BIBM.2017.8217742

Sleep/wake state prediction and sleep parameter estimation using unsupervised classification via clustering. / Elmanzalawi, Yasser; Buxton, Orfeu M.; Honavar, Vasant Gajanan.

Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017. ed. / Illhoi Yoo; Jane Huiru Zheng; Yang Gong; Xiaohua Tony Hu; Chi-Ren Shyu; Yana Bromberg; Jean Gao; Dmitry Korkin. Institute of Electrical and Electronics Engineers Inc., 2017. p. 718-723 (Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017; Vol. 2017-January).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - Sleep/wake state prediction and sleep parameter estimation using unsupervised classification via clustering

AU - Elmanzalawi, Yasser

AU - Buxton, Orfeu M.

AU - Honavar, Vasant Gajanan

PY - 2017/12/15

Y1 - 2017/12/15

N2 - Sleep quality impacts virtually all aspects of life, including health, mood, emotions, cognition, memory, behavior, and performance. Actigraphy offers a lower-cost alternative to conventional polysomnography (PSG), the gold standard for measuring sleep quality. Effective use of actigraphy for assessing sleep quality requires reliable methods for detecting sleep/wake states from actigraphy measurements. Machine learning offers a promising approach to building sleep/wake state detectors from actigraphy data. However, current machine learning approaches rely on expert labeled training data that can be expensive and laborious to acquire. In this work, we introduce a novel approach for integrating unsupervised learning algorithms and domain knowledge heuristics, based on statistical properties of clustered sleep and wake epochs, to develop reliable sleep/wake state prediction models using unlabeled wrist actigraphy data. Experimental results using a dataset of 37 participants and covering 282 sleeping periods demonstrate the viability of the proposed approach on developing sleep/wake state detection models from unlabeled actigraphy data with a predictive performance that is comparable with the performance of models developed using some state-of-the-art supervised learning algorithms applied to labeled actigraphy data. Our results lay the groundwork for developing fully automated machine learning models for sleep/wake state prediction and sleep parameters estimations by eliminating the need for costly and labor-intensive expert annotations of PSG recordings for labeling actigraphy data.

AB - Sleep quality impacts virtually all aspects of life, including health, mood, emotions, cognition, memory, behavior, and performance. Actigraphy offers a lower-cost alternative to conventional polysomnography (PSG), the gold standard for measuring sleep quality. Effective use of actigraphy for assessing sleep quality requires reliable methods for detecting sleep/wake states from actigraphy measurements. Machine learning offers a promising approach to building sleep/wake state detectors from actigraphy data. However, current machine learning approaches rely on expert labeled training data that can be expensive and laborious to acquire. In this work, we introduce a novel approach for integrating unsupervised learning algorithms and domain knowledge heuristics, based on statistical properties of clustered sleep and wake epochs, to develop reliable sleep/wake state prediction models using unlabeled wrist actigraphy data. Experimental results using a dataset of 37 participants and covering 282 sleeping periods demonstrate the viability of the proposed approach on developing sleep/wake state detection models from unlabeled actigraphy data with a predictive performance that is comparable with the performance of models developed using some state-of-the-art supervised learning algorithms applied to labeled actigraphy data. Our results lay the groundwork for developing fully automated machine learning models for sleep/wake state prediction and sleep parameters estimations by eliminating the need for costly and labor-intensive expert annotations of PSG recordings for labeling actigraphy data.

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SP - 718

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BT - Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017

A2 - Yoo, Illhoi

A2 - Zheng, Jane Huiru

A2 - Gong, Yang

A2 - Hu, Xiaohua Tony

A2 - Shyu, Chi-Ren

A2 - Bromberg, Yana

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PB - Institute of Electrical and Electronics Engineers Inc.

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Elmanzalawi Y, Buxton OM, Honavar VG. Sleep/wake state prediction and sleep parameter estimation using unsupervised classification via clustering. In Yoo I, Zheng JH, Gong Y, Hu XT, Shyu C-R, Bromberg Y, Gao J, Korkin D, editors, Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 718-723. (Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017). https://doi.org/10.1109/BIBM.2017.8217742