ISSN 0869-6632 (Print)
ISSN 2542-1905 (Online)


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Grubov V. V., Ovchinnikov A. A., Sitnikova E. Y., Koronovskii A. A., Hramov A. E. Wavelet analysis of sleep spindles on EEG and development of method for their automatic diagnostic. Izvestiya VUZ. Applied Nonlinear Dynamics, 2011, vol. 19, iss. 4, pp. 91-108. DOI: 10.18500/0869-6632-2011-19-4-91-108

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Wavelet analysis of sleep spindles on EEG and development of method for their automatic diagnostic

Autors: 
Grubov Vadim Valerevich, Immanuel Kant Baltic Federal University
Ovchinnikov Aleksej Aleksandrovich, Saratov State University
Sitnikova Evgenia Yurievna, Federal State Budgetary Institution of Science "Institute of Higher Nervous Activity and Neurophysiology RAS"
Koronovskii Aleksei Aleksandrovich, Saratov State University
Hramov Aleksandr Evgenevich, Immanuel Kant Baltic Federal University
Abstract: 

The detailed wavelet analysis of sleep electric brain activity, obtained from rats with genetic predisposition to absence-epilepsy, has been performed. Characteristic features of time-and-frequency structure of sleep spindles (oscillatory pattern, that serve as electroencephalographic correlate for slow-wave sleep) have been discovered in long-term electroencephalographic data. Operation has been performed using continuous wavelet transform. Few common wavelet bases have been tested and complex Morlet-wavelet turned out to be the most effective for detection of time-and-frequency features of sleep spindles on EEG. Morlet-wavelet has been used for development of system for automatic diagnostic of sleep spindles on EEG. As a result of analysis two types of sleep spindles, that have the same time dynamics, but different frequency structure, have been discovered. Complex dynamics of main frequency during the sleep spindle has been revealed. The method for automatic diagnostic of sleep spindles, based on computation of wavelet transform energy in two frequency ranges for two types of sleep spindles, has been proposed according to obtained data. The testing of method revealed high accuracy of automatic diagnostic for investigating events on EEG. The method can be used in routine EEG researches, related to detection and classification of different oscillatory patterns.

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Received: 
14.03.2011
Accepted: 
25.05.2011
Published: 
30.09.2011
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