Unobtrusive classification of sleep and wakefulness using load cells under the bed

Daniel Austin, Zachary T. Beattie, Thomas Riley, Adriana M. Adami, Chad C. Hagen, Tamara L. Hayes

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

14 Scopus citations

Abstract

Poor quality of sleep increases the risk of many adverse health outcomes. Some measures of sleep, such as sleep efficiency or sleep duration, are calculated from periods of time when a patient is asleep and awake. The current method for assessing sleep and wakefulness is based on polysomnography, an expensive and inconvenient method of measuring sleep in a clinical setting. In this paper, we suggest an alternative method of detecting periods of sleep and wake that can be obtained unobtrusively in a patient's own home by placing load cells under the supports of their bed. Specifically, we use a support vector machine to classify periods of sleep and wake in a cohort of patients admitted to a sleep lab. The inputs to the classifier are subject demographic information, a statistical characterization of the load cell derived signals, and several sleep parameters estimated from the load cell data that are related to movement and respiration. Our proposed classifier achieves an average sensitivity of 0.808 and specificity of 0.812 with 90% confidence intervals of (0.790, 0.821) and (0.798, 0.826), respectively, when compared to the gold-standard sleep/wake annotations during polysomnography. As this performance is over 27 sleep patients with a wide variety of diagnosis levels of sleep disordered breathing, age, body mass index, and other demographics, our method is robust and works well in clinical practice.

Original languageEnglish (US)
Title of host publication2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2012
Pages5254-5257
Number of pages4
DOIs
StatePublished - Dec 14 2012
Event34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2012 - San Diego, CA, United States
Duration: Aug 28 2012Sep 1 2012

Publication series

NameProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
ISSN (Print)1557-170X

Other

Other34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2012
CountryUnited States
CitySan Diego, CA
Period8/28/129/1/12

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ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

Cite this

Austin, D., Beattie, Z. T., Riley, T., Adami, A. M., Hagen, C. C., & Hayes, T. L. (2012). Unobtrusive classification of sleep and wakefulness using load cells under the bed. In 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2012 (pp. 5254-5257). [6347179] (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS). https://doi.org/10.1109/EMBC.2012.6347179