"JOURNAL OF RADIO ELECTRONICS" (Zhurnal Radioelektroniki ISSN 1684-1719, N 11, 2016

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Method of automated epileptic seizure detection in rats' EEG

V. E. Antsiperov, Y.V. Obukhov
Kotelnikov Institute of Radioengineering and Electronics of RAS

The paper is received on November 8, 2016


Abstract. The focus of the paper is the problem of developing effective methods for automated segmentation of epileptic seizures in EEG recordings intended for the diagnosis of epilepsy. As a solution to the problem the new method is proposed. This method is based on the analysis of non-stationary, containing quasi-periodic fragments signal, comprising local period estimation procedure followed by identification of period dynamics characteristics in order to segment fragments with repeating oscillations. The method proposed combines two basic ideas of existing approaches – multiscale (wavelet) analysis and synthesis of the quadratic time–frequency representations. The union of these approaches could be carried out in the framework of the previously proposed methodology of multiscale correlation analysis (MCA) and the analytical spectra technique.

A hallmark of the main tools of the method – special MCA representations – is that they, in contrast to classical approaches, are focused on the time domain, rather than on frequency domain. The paper discusses in detail the successive steps of synthesis of such representations in the time domain, from MCA estimation of the autocorrelation function to linear procedures such as window weighting and matched filtering with a special sliding window. The fact that the effective realization of the synthesized representations is based on shifted analytical spectra, should be considered nothing more than a convenient technique.

All MCA synthesis steps and segmentation procedure characteristics are illustrated by real rat EEG recording marked by a neurophysiologist–expert. Taking into account the results obtained by a real recording, we conclude that the synthesized method will be one of the most promising tools for the automatic segmentation of epileptic seizures.

Key words: time−frequency EEG analysis, automated epileptic seizure detection methods, multiscale correlational analysis, analytic spectra.


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