Literature DB >> 1293442

New method of automated sleep quantification.

S Roberts1, L Tarassenko.   

Abstract

Since its discovery some 50 years ago, the electro-encephalogram (EEG) has formed the basis for classification of sleep into several stages, either laboriously performed by visual examination of the EEG and related signals or, more recently, by automated techniques. Both visual scoring and most automated analyses are highly subjective and rely on application of a predefined set of rules. A method of analysing the EEG which requires no such application of rules and aims to give some indication of the dynamics of sleep in humans is proposed in the paper.

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Year:  1992        PMID: 1293442     DOI: 10.1007/bf02457830

Source DB:  PubMed          Journal:  Med Biol Eng Comput        ISSN: 0140-0118            Impact factor:   2.602


  10 in total

1.  Proposed supplements and amendments to 'A Manual of Standardized Terminology, Techniques and Scoring System for Sleep Stages of Human Subjects', the Rechtschaffen & Kales (1968) standard.

Authors:  T Hori; Y Sugita; E Koga; S Shirakawa; K Inoue; S Uchida; H Kuwahara; M Kousaka; T Kobayashi; Y Tsuji; M Terashima; K Fukuda; N Fukuda
Journal:  Psychiatry Clin Neurosci       Date:  2001-06       Impact factor: 5.188

2.  Stationarity of the human electroendephalogram.

Authors:  B A Cohen; A Sances
Journal:  Med Biol Eng Comput       Date:  1977-09       Impact factor: 2.602

3.  A model-based monitor of human sleep stages.

Authors:  B Kemp; E W Gröneveld; A J Janssen; J M Franzen
Journal:  Biol Cybern       Date:  1987       Impact factor: 2.086

4.  Estimation of running frequency spectra using a Kalman filter algorithm.

Authors:  D W Skagen
Journal:  J Biomed Eng       Date:  1988-05

Review 5.  Methods of analysis of nonstationary EEGs, with emphasis on segmentation techniques: a comparative review.

Authors:  J S Barlow
Journal:  J Clin Neurophysiol       Date:  1985-07       Impact factor: 2.177

6.  Demonstration of segmentation techniques for EEG records.

Authors:  A Hasman; B H Jansen; G H Landeweerd; A W van Blokland-Vogelesang
Journal:  Int J Biomed Comput       Date:  1978-07

7.  A modified method for scoring slow wave sleep of older subjects.

Authors:  W B Webb; L M Dreblow
Journal:  Sleep       Date:  1982       Impact factor: 5.849

8.  Autoregressive estimation of short segment spectra for computerized EEG analysis.

Authors:  B H Jansen; J R Bourne; J W Ward
Journal:  IEEE Trans Biomed Eng       Date:  1981-09       Impact factor: 4.538

9.  Computerized method for scoring of polygraphic sleep recordings.

Authors:  I Gath; E Bar-on
Journal:  Comput Programs Biomed       Date:  1980-06

10.  Differentiation of normal and disturbed sleep by automatic analysis.

Authors:  J Hasan
Journal:  Acta Physiol Scand Suppl       Date:  1983
  10 in total
  2 in total

Review 1.  A review of signals used in sleep analysis.

Authors:  A Roebuck; V Monasterio; E Gederi; M Osipov; J Behar; A Malhotra; T Penzel; G D Clifford
Journal:  Physiol Meas       Date:  2013-12-17       Impact factor: 2.833

2.  Comparison of manual sleep staging with automated neural network-based analysis in clinical practice.

Authors:  Jennifer Caffarel; G John Gibson; J Phil Harrison; Clive J Griffiths; Michael J Drinnan
Journal:  Med Biol Eng Comput       Date:  2006-03       Impact factor: 2.602

  2 in total

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