Literature DB >> 8186305

AI-based approach to automatic sleep classification.

M Kubat1, G Pfurtscheller, D Flotzinger.   

Abstract

The primary goal of this paper is to introduce the potential of artificial intelligence (AI) methods to researchers in sleep classification. AI provides learning procedures for the construction of a sleep classifier, prescribing how to combine the observed parameters and how to derive the corresponding decision thresholds. A case study reporting a successful application of an automatic induction of decision trees and of a learning vector quantizer to this domain is presented.

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Mesh：

Year:  1994        PMID： 8186305     DOI： 10.1007/bf00203237

Source DB:  PubMed          Journal:  Biol Cybern        ISSN： 0340-1200            Impact factor:   2.086


  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.  A model-based detector of vertex waves and K complexes in sleep electroencephalogram.

Authors:  A C Da Rosa; B Kemp; T Paiva; F H Lopes da Silva; H A Kamphuisen
Journal:  Electroencephalogr Clin Neurophysiol       Date:  1991-01

Review 3.  [Pattern recognition techniques in sleep polygraphy].

Authors:  M Jobert; W Scheuler; W Röske; E Poiseau; S Kubicki
Journal:  EEG EMG Z Elektroenzephalogr Elektromyogr Verwandte Geb       Date:  1991-09

4.  [The effect of age on sleep spindle and K complex density].

Authors:  S Kubicki; W Scheuler; M Jobert; C Pastelak-Price
Journal:  EEG EMG Z Elektroenzephalogr Elektromyogr Verwandte Geb       Date:  1989-03

5.  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

6.  [Automatic analysis of sleep by a hybrid system: new results].

Authors:  J M Gaillard; M Krassoïevitch; R Tissot
Journal:  Electroencephalogr Clin Neurophysiol       Date:  1972-10

7.  EEG sleep stage scoring by an automatic hybrid system.

Authors:  J R Smith; I Karacan
Journal:  Electroencephalogr Clin Neurophysiol       Date:  1971-09

8.  Towards automated sleep classification in infants using symbolic and subsymbolic approaches.

Authors:  M Kubat; D Flotzinger; G Pfurtscheller
Journal:  Biomed Tech (Berl)       Date:  1993-04       Impact factor: 1.411

9.  Sleep classification in infants based on artificial neural networks.

Authors:  G Pfurtscheller; D Flotzinger; K Matuschik
Journal:  Biomed Tech (Berl)       Date:  1992-06       Impact factor: 1.411

10.  Automated sleep scoring: a comparative reliability study of two algorithms.

Authors:  E Stanus; B Lacroix; M Kerkhofs; J Mendlewicz
Journal:  Electroencephalogr Clin Neurophysiol       Date:  1987-04
  10 in total
  2 in total

1.  Fully parametric sleep staging compatible with the classical criteria.

Authors:  Urszula Malinowska; Hubert Klekowicz; Andrzej Wakarow; Szymon Niemcewicz; Piotr J Durka
Journal:  Neuroinformatics       Date:  2009-12

Review 2.  Clinical applications of artificial intelligence in sleep medicine: a sleep clinician's perspective.

Authors:  Anuja Bandyopadhyay; Cathy Goldstein
Journal:  Sleep Breath       Date:  2022-03-09       Impact factor: 2.816
  2 in total

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