article

A Bayesian approach for sleep and wake classification based on dynamic time warping method

Authors:

Zhihan LvAuthors Info & Claims

Multimedia Tools and Applications, Volume 76, Issue 17

Pages 17765 - 17784

https://doi.org/10.1007/s11042-015-3053-z

Published: 01 September 2017 Publication History

Abstract

Sleep plays a significant role in human' smental and physical health. Recently, the associations between lack of sleep and weight gain, development of cancer and many other health problems have been recognized. Then monitoring the sleep and wake state all night is becoming a hotspot issue. Traditionally it classified by a PSG recording which is very costly and uncomfortable. Nowadays, with the advance of internet of things, many convenient wearable devices are being used for medical use, like measuring the heart rate (HR), blood pressure and other signals. With the sleep quality monitor problem, the key question is how to discriminate the sleep and weak stage from these signals. This paper proposed a Bayesian approach based on dynamic time warping (DTW) method for sleep and wake classification. It used HR and surplus pulse O2 (SPO2) signals to analyze the sleep states and the occurrence of some sleep-related problems. DTW is an algorithm that searches an optimal alignment between time series with scaling and shifting and Bayesian methods have been successfully used for object classification in many study. In this paper, a three-step process is used for sleep and wake classification. In the first step, the DTW is used to extract features of the original HR and SPO2 signals. Then a probabilistic model is introduced for using the Bayesian classification for uncertain data. And in the classification step, the DTW features are used as the training dataset in the Bayesian approach for sleep and wake classification. Finally, a case study form a real-word applications, collected from the website of the Sleep Heart Health Study, is presented to shown the feasibility and advantages of the DTW-based Bayesian approach.

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Cited By

Sun JYou YGe BTan YYang K(2022)Optimization framework and applications of training multi-state influence netsApplied Intelligence10.1007/s10489-021-02514-z52:2(1477-1492)Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1007/s10489-021-02514-z
Comito CFalcone DForestiero AKlamma RO'Halloran SRokne J(2021)Diagnosis prediction based on similarity of patients physiological parametersProceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining10.1145/3487351.3490962(487-494)Online publication date: 8-Nov-2021
https://dl.acm.org/doi/10.1145/3487351.3490962
Zhang JJiang X(2020)Improved Computation of Affine Dynamic Time WarpingProceedings of the 3rd International Conference on Applications of Intelligent Systems10.1145/3378184.3378226(1-5)Online publication date: 7-Jan-2020
https://dl.acm.org/doi/10.1145/3378184.3378226

A Bayesian approach for sleep and wake classification based on dynamic time warping method
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning

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cover image Multimedia Tools and Applications

Multimedia Tools and Applications Volume 76, Issue 17

Sep 2017

561 pages

ISSN:1380-7501

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Copyright © Copyright © 2017 Springer Science+Business Media, LLC.

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Kluwer Academic Publishers

United States

Publication History

Published: 01 September 2017

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Cited By

Sun JYou YGe BTan YYang K(2022)Optimization framework and applications of training multi-state influence netsApplied Intelligence10.1007/s10489-021-02514-z52:2(1477-1492)Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1007/s10489-021-02514-z
Comito CFalcone DForestiero AKlamma RO'Halloran SRokne J(2021)Diagnosis prediction based on similarity of patients physiological parametersProceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining10.1145/3487351.3490962(487-494)Online publication date: 8-Nov-2021
https://dl.acm.org/doi/10.1145/3487351.3490962
Zhang JJiang X(2020)Improved Computation of Affine Dynamic Time WarpingProceedings of the 3rd International Conference on Applications of Intelligent Systems10.1145/3378184.3378226(1-5)Online publication date: 7-Jan-2020
https://dl.acm.org/doi/10.1145/3378184.3378226

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