[Pattern recognition techniques in sleep polygraphy]
- PMID: 1765030
[Pattern recognition techniques in sleep polygraphy]
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.
Similar articles
-
[A system for continuous digitizing and evaluation of 32 biosignals from all-night sleep leads].EEG EMG Z Elektroenzephalogr Elektromyogr Verwandte Geb. 1989 Sep;20(3):178-84. EEG EMG Z Elektroenzephalogr Elektromyogr Verwandte Geb. 1989. PMID: 2507282 German.
-
The visual scoring of sleep and arousal in infants and children.J Clin Sleep Med. 2007 Mar 15;3(2):201-40. J Clin Sleep Med. 2007. PMID: 17557427 Review.
-
Sleep spindles and spike-wave discharges in EEG: Their generic features, similarities and distinctions disclosed with Fourier transform and continuous wavelet analysis.J Neurosci Methods. 2009 Jun 15;180(2):304-16. doi: 10.1016/j.jneumeth.2009.04.006. Epub 2009 Apr 19. J Neurosci Methods. 2009. PMID: 19383511
-
[Automatic determination system of human sleep stages on an experimental basis].J UOEH. 1986 Mar 20;8 Suppl:169-71. J UOEH. 1986. PMID: 3726298 Japanese.
-
Automated analysis and trending of the raw EEG signal.Am J Electroneurodiagnostic Technol. 2008 Sep;48(3):166-91. Am J Electroneurodiagnostic Technol. 2008. PMID: 18998476 Review.
Cited by
-
AI-based approach to automatic sleep classification.Biol Cybern. 1994;70(5):443-8. doi: 10.1007/BF00203237. Biol Cybern. 1994. PMID: 8186305