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