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

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

Research output: Contribution to journalArticle

14 Citations (Scopus)

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.

Fingerprint

Wakefulness
Sleep
Polysomnography
Classifiers
Demography
Sleep Apnea Syndromes
Gold
Respiration
Support vector machines
Body Mass Index
Confidence Intervals

ASJC Scopus subject areas

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

Cite this

Unobtrusive classification of sleep and wakefulness using load cells under the bed. / Austin, Daniel; Beattie, Zachary T.; Riley, Thomas; Adami, Adriana M.; Hagen, Chad; Hayes, Tamara L.

In: Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference, Vol. 2012, 2012, p. 5254-5257.

Research output: Contribution to journalArticle

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