DIAGNOSTICS AND ANALYSIS OF OSCILLATORY NEURONAL NETWORK ACTIVITY OF BRAIN WITH CONTINUOUS WAVELET ANALYSIS
Cite this article as:
Koronovskii A. A., Ovchinnikov А. А., Sitnikova Е. 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: https://doi.org/10.18500/0869-6632-2011-19-1-86-108
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 (spikewave 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 waveletbasis construction (spindlewavelets) 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 spikewave 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.
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BibTeX
author = {A. A. Koronovskii and А. А. Ovchinnikov and Е. Yu. Sitnikova and A. E. Hramov},
title = {DIAGNOSTICS AND ANALYSIS OF OSCILLATORY NEURONAL NETWORK ACTIVITY OF BRAIN WITH CONTINUOUS WAVELET ANALYSIS},
year = {2011},
journal = {Izvestiya VUZ. Applied Nonlinear Dynamics},
volume = {19},number = {1},
url = {https://old-andjournal.sgu.ru/en/articles/diagnostics-and-analysis-of-oscillatory-neuronal-network-activity-of-brain-with-continuous},
address = {Саратов},
language = {russian},
doi = {10.18500/0869-6632-2011-19-1-86-108},pages = {86--108},issn = {0869-6632},
keywords = {Signal processing,wavelet analysis,epilepsy,pattern recognition,braincomputer interface.},
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 (spikewave 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 waveletbasis construction (spindlewavelets) 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 spikewave 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. }}