@inproceedings{0621a90fc3784229ad0317e5ceed0b33,
title = "A Gaussian model for movement detection during Sleep",
abstract = "Quality of sleep is an important attribute of an individual's health state and its assessment is therefore a useful diagnostic feature. Changes in the patterns of mobility in bed during sleep can be a disease marker or can reflect various abnormal physiological and neurological conditions. This paper describes a method for detection of movement in bed that is evaluated on data collected from patients admitted for regular polysomnography. The system is based on load cells installed at the supports of a bed. Since the load cell signal varies the most during movement, the approach uses a weighted combination of the short-term mean-square differences of each load cell signal to capture the variations in the signal caused by movement. We use a single univariate Gaussian model to represent each class: movement versus non-movement. We assess the performance of the method against manual annotation performed by a sleep clinic technician from seventeen patients. The proposed detection method achieved an overall sensitivity of 97.9% and specificity of 98.7%.",
author = "Adami, {Adriana M.} and Adami, {Andre G.} and Hayes, {Tamara L.} and Misha Pavel and Beattie, {Zachary T.}",
year = "2012",
doi = "10.1109/EMBC.2012.6346413",
language = "English (US)",
isbn = "9781424441198",
series = "Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS",
pages = "2263--2266",
booktitle = "2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2012",
note = "34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2012 ; Conference date: 28-08-2012 Through 01-09-2012",
}