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

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Koronovskii A. A., van Luijtelaar G. ., Ovchinnikov A. A., Sitnikova E. Y., Hramov A. E. Diagnostics and analysis of oscillatory neuronal network activity of brain with continuous wavelet analysis. Izvestiya VUZ. Applied Nonlinear Dynamics, 2011, vol. 19, iss. 1, pp. 86-108. DOI: 10.18500/0869-6632-2011-19-1-86-108

This is an open access article distributed under the terms of Creative Commons Attribution 4.0 International License (CC-BY 4.0).
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Russian
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Journal in the journal
Article type: 
Review
UDC: 
537.86; 612.825.3+612.08+612.82
DOI: 
10.18500/0869-6632-2011-19-1-86-108

Diagnostics and analysis of oscillatory neuronal network activity of brain with continuous wavelet analysis

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

In the article we present an overview of a number of continuous wavelet transformbased techniques for analysis and diagnostic of oscillatory neuronal network activity of brain in experimentally obtained electroencephalographic data. We describe a technique for automatic detection of characteristic patterns for paroxysmal activity (spike-wave discharges) in epileptic electroencephalogram (EEG) based on wavelet spectrum power analysis, obtained with continuous wavelet transform with complex mother wavelet (Morlet) in specific frequency ranges. An effective approach to sleep spindles detection and classification based on special adaptive wavelet-basis construction (spindle-wavelets) is proposed. Proposed techniques are shaped for real time EEG signals study and can be used for building systems for monitoring activity of a brain challenged with epilepsy. A study of spectral and temporal structure of EEG before spike-wave discharges is carried out and characteristic predecessors of paroxysmal activity are found, which can be used for detecting brain transition state. Such diagnostics can be used to predict epileptic seizures in clinical practice.

Key words: 
signal processing
wavelet analysis
epilepsy
pattern recognition
brain-computer interface
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Received: 
16.10.2010
Accepted: 
16.10.2010
Published: 
29.04.2011
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