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Journal of Automation and Information Sciences
SJR: 0.232 SNIP: 0.464 CiteScore™: 0.27

ISSN Imprimir: 1064-2315
ISSN On-line: 2163-9337

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Journal of Automation and Information Sciences

DOI: 10.1615/JAutomatInfScien.v36.i8.30
pages 35-45

Nonparametric On-Line Detection of Changes in Signal Spectral Characteristics for Early Prediction of Epilepsy Seizure Onset

Tatyana I. Aksenova
Zhozef Fourier University, Grenoble, France
Vladimir V. Volkovich
International Research and Training Center of Information Technologies and Systems of National Academy of Sciences of Ukraine and Ministry of Education and Science of Ukraine, Kiev, Ukraine
Alexandro Villa
Institute of Physiology of Lausanne University, Switzerland

RESUMO

The present study introduces the method for solving the problem on early prediction of epilepsy seizure onset based on analysis of multi-channel electroencephalogram (EEG). This problem is considered as the problem of on-line detection of multiple abrupt changes in spectral characteristics of the process under consideration. With EEG characteristics not being changed abruptly, to describe growing changes the use was made of multiple disharmony model. The quantity to characterize the degree of spectral instability is applied as a detector. The nonparametric sequential method of detecting disharmony is realized in computational algorithms effective enough to be used in real time for 32-channel EEG and to open possibilities for creating the system of automatic prediction.


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