Observer based on body movement information in sleeping and estimation of sleep stage appearance probability

2008 ◽  
Vol 3 (6) ◽  
pp. 688-695 ◽  
Author(s):  
Yosuke Kurihara ◽  
Kajiro Watanabe ◽  
Kazuyuki Kobayashi ◽  
Hiroshi Tanaka
Keyword(s):  
Sleep Stage ◽  
Body Movement

Sleep Stage Estimation By Evolutionary Computation Using Heartbeat Data and Body-Movement

2012 ◽  
Vol 4 (22) ◽  
pp. 281-290 ◽  
Author(s):  
Hiroyasu MAtsushima ◽  
Kazuyuki Hirose ◽  
Kiyohiko Hattori ◽  
Hiroyuki Sato ◽  
Keiki Takadama
Keyword(s):  
Evolutionary Computation ◽  
Sleep Stage ◽  
Body Movement ◽  
Stage Estimation

Auditory Awakening Thresholds in Rem and Nrem Sleep Stages

1966 ◽  
Vol 22 (3) ◽  
pp. 927-942 ◽  
Author(s):  
Allan Rechtschaffen ◽  
Peter Hauri ◽  
Maurice Zeitlin
Keyword(s):  
Low Voltage ◽  
Narrow Range ◽  
Sleep Stage ◽  
Body Movement ◽  
Nrem Sleep ◽  
Sleep Stages ◽  
The Awakening ◽  
Delta Sleep ◽  
Significant Stage ◽  
Experimental Trials

The auditory awakening thresholds of the major electroencephalographically defined sleep stages were compared. A modification of the method of constant stimuli was used in an apparently successful attempt to minimize the incorporation of the experimental stimuli into the mental activity of the sleeper. A total of 319 experimental trials were distributed among seven human Ss who served for about six experimental nights each. The sequence and timing of experimental trials were counterbalanced to control for nights, habituation, amount of accumulated sleep, and amount of sleep since last awakening. The results showed approximately equal awakening thresholds during REM periods (the rapid eye movement stage of sleep) and stage 2 (low voltage EEG and 12 to 14 cps “sleep spindles”). Both these stages had lower awakening thresholds than delta sleep (large slow EEG waves). Awakening thresholds became lower with accumulated sleep, independent of sleep stage. There were no significant stage independent relationships between awakening threshold and time since last awakening or time since last body movement, although the latter were varied over a relatively narrow range which limits the generality of these findings. There was no stage independent relationship between heart rate and awakening threshold. The possible physiological determinants of the awakening response were discussed.

Novel Sleep Stage Estimation Method for Children Using Body Movement

2013 ◽  
Vol 2 (2) ◽  
pp. 61-68 ◽  
Author(s):  
Shima Okada ◽  
Sachiko Shimizu ◽  
Yuko Ohno ◽  
Ikuko Mohri ◽  
Masako Taniike ◽  
...  
Keyword(s):  
Sleep Stage ◽  
Body Movement ◽  
Estimation Method ◽  
Stage Estimation

Book project: Body, movement and mental health - a physiotherapy perspective

2012 ◽  
Author(s):  
Riitta Keskinen Rosenqvist ◽  
Gabriele Biguet ◽  
Adrienne Levy-Berg
Keyword(s):  
Mental Health ◽  
Body Movement

Causal Probabilistic Network and Power Spectral Estimation Used in Sleep Stage Classification

1997 ◽  
Vol 36 (04/05) ◽  
pp. 41-46
Author(s):  
A. Kjaer ◽  
W. Jensen ◽  
T. Dyrby ◽  
L. Andreasen ◽  
J. Andersen ◽  
...  
Keyword(s):  
Sleep Stage ◽  
Spectral Estimation ◽  
Interrater Agreement ◽  
Probabilistic Networks ◽  
Probabilistic Network ◽  
Spectral Bands ◽  
Power Spectral ◽  
Stage Classification ◽  
Sleep Stage Classification ◽  
The Brain

Abstract.A new method for sleep-stage classification using a causal probabilistic network as automatic classifier has been implemented and validated. The system uses features from the primary sleep signals from the brain (EEG) and the eyes (AOG) as input. From the EEG, features are derived containing spectral information which is used to classify power in the classical spectral bands, sleep spindles and K-complexes. From AOG, information on rapid eye movements is derived. Features are extracted every 2 seconds. The CPN-based sleep classifier was implemented using the HUGIN system, an application tool to handle causal probabilistic networks. The results obtained using different training approaches show agreements ranging from 68.7 to 70.7% between the system and the two experts when a pooled agreement is computed over the six subjects. As a comparison, the interrater agreement between the two experts was found to be 71.4%, measured also over the six subjects.

Sleep stage related heart rate variability distinguishes between insomnia and normal sleep

2019 ◽  
Author(s):  
T Mikoteit ◽  
M De Witte ◽  
E Holsboer-Trachsler ◽  
M Hatzinger ◽  
J Beck ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Sleep Stage ◽  
Normal Sleep
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