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http://dx.doi.org/10.9718/JBER.2014.35.6.211

Automatic Detection of Slow-Wave Sleep Based on Electrocardiogram  

Yoon, Hee Nam (Interdisciplinary Program in Bioengineering, Seoul National University)
Hwang, Su Hwan (Interdisciplinary Program in Bioengineering, Seoul National University)
Jung, Da Woon (Interdisciplinary Program in Bioengineering, Seoul National University)
Lee, Yu Jin (Department of Neuropsychiatry and the Center for Sleep and Chronobiology, Seoul National University Hospital)
Jeong, Do-Un (Department of Neuropsychiatry and the Center for Sleep and Chronobiology, Seoul National University Hospital)
Park, Kwang Suk (Department of Biomedical Engineering, Seoul National University College of Medicine)
Publication Information
Journal of Biomedical Engineering Research / v.35, no.6, 2014 , pp. 211-218 More about this Journal
Abstract
The objective of this research is to develop an automatic algorithm based on electrocardiogram (ECG) to estimate slow-wave sleep (SWS). An algorithm is based on 7 indices extracted from heart rate on ECG which simultaneously recorded with standard full night polysomnography from 31 subjects. Those 7 indices were then applied to independent component analysis to extract a feature that discriminates SWS and other sleep stages. Overall Cohen's kappa, accuracy, sensitivity and specificity of the algorithm to detect 30s epochs of SWS were 0.52, 0.87, 0.70 and 0.90, respectively. The automatic SWS detection algorithm could be useful combining with existing REM and wake estimation technique on unattended home-based sleep monitoring.
Keywords
Slow-wave sleep; Electrocardiogram; Heart rate;
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