Literature DB >> 2428585

Automatic analysis overcomes limitations of sleep stage scoring.

W Haustein, J Pilcher, J Klink, H Schulz.   

Abstract

A computer programme for the automatic analysis of the sleep EEG and EMG is presented. An EEG parameter is derived which is based on the joint frequency-amplitude distribution of the EEG activity. This newly developed parameter stresses the dynamic development of sleep, composed of alternating phases of EEG synchronization and desynchronization. While synchronization develops slowly, the opposite phase of EEG activity, desynchronization, is more rapid. A particular advantage of the EEG parameter is its continuous scale which results in high resolution. Thus, the parameter reflects gradual EEG changes which a visual analyser would have to classify into the same sleep stage. In addition to the EEG, the EMG is analysed and two parameters are extracted, representing the mean muscle tone and transient EMG activation, respectively. There is a close temporal relationship between the EEG and the EMG with a maximum of transient EMG activity during the phase of EEG desynchronization. The properties of the automatic analysis were compared to the visual analysis on a sample of 12 all-night sleep records. The results show that the EEG parameter also agrees sufficiently with the traditional sleep scoring method and therefore is a valid descriptor of the time course of sleep.

Mesh:

Year:  1986        PMID: 2428585     DOI: 10.1016/0013-4694(86)90161-6

Source DB:  PubMed          Journal:  Electroencephalogr Clin Neurophysiol        ISSN: 0013-4694


  7 in total

1.  Chebyshev filter bank for estimation of frequency band powers in EEG.

Authors:  I N Bankman; I Gath
Journal:  Med Biol Eng Comput       Date:  1991-01       Impact factor: 2.602

Review 2.  Rethinking sleep analysis.

Authors:  Hartmut Schulz
Journal:  J Clin Sleep Med       Date:  2008-04-15       Impact factor: 4.062

3.  Automatic analysis of sleep using two parameters based on principal component analysis of electroencephalography spectral data.

Authors:  M Jobert; H Escola; E Poiseau; P Gaillard
Journal:  Biol Cybern       Date:  1994       Impact factor: 2.086

4.  Human heart rate variability and sleep stages.

Authors:  L Toscani; P F Gangemi; A Parigi; R Silipo; P Ragghianti; E Sirabella; M Morelli; L Bagnoli; R Vergassola; G Zaccara
Journal:  Ital J Neurol Sci       Date:  1996-12

5.  Behavioral state classification in epileptic brain using intracranial electrophysiology.

Authors:  Vaclav Kremen; Juliano J Duque; Benjamin H Brinkmann; Brent M Berry; Michal T Kucewicz; Fatemeh Khadjevand; Jamie Van Gompel; Matt Stead; Erik K St Louis; Gregory A Worrell
Journal:  J Neural Eng       Date:  2017-01-04       Impact factor: 5.379

6.  Quasi-supervised scoring of human sleep in polysomnograms using augmented input variables.

Authors:  Farid Yaghouby; Sridhar Sunderam
Journal:  Comput Biol Med       Date:  2015-01-23       Impact factor: 4.589

Review 7.  Spindle Oscillations in Sleep Disorders: A Systematic Review.

Authors:  Oren M Weiner; Thien Thanh Dang-Vu
Journal:  Neural Plast       Date:  2016-03-10       Impact factor: 3.599

  7 in total

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