Literature DB >> 1765030

[Pattern recognition techniques in sleep polygraphy].

M Jobert1, W Scheuler, W Röske, E Poiseau, S Kubicki.   

Abstract

The evaluation of EEG-patterns is usually accomplished by visual analysis. Nowadays however, even personal computers are fast enough for an efficient pattern recognition of EEG signals. Using sleep spindles and K-complexes as examples, our aim was to demonstrate how patterns can be detected in an EEG signal with a high degree of accuracy. Furthermore, recognition of K-complexes has been improved by applying an additional "adaptive algorithm" allowing individual adjustments to the signal's form and amplitude.

Mesh:

Year:  1991        PMID: 1765030

Source DB:  PubMed          Journal:  EEG EMG Z Elektroenzephalogr Elektromyogr Verwandte Geb        ISSN: 0012-7590


  1 in total

1.  AI-based approach to automatic sleep classification.

Authors:  M Kubat; G Pfurtscheller; D Flotzinger
Journal:  Biol Cybern       Date:  1994       Impact factor: 2.086

  1 in total

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