In this study, we investigated
the electroencephalogram (EEG) dynamics in normal and epileptic subjects using
three newly defined quantifiers adapted from nonlinear dynamics and Hilbert
transform scatter plots (HTSPs): dispersion entropy (DispEntropy), dispersion
complexity (Disp Comp), and forbidden count (FC), hypothesizing that analysis
of electroencephalogram (EEG) signals using nonlinear and deterministic chaos
theory may provide clinicians with information for medical diagnosis and
assessment of the applied therapy. DispEntropy evaluates irregularity of the
EEG time series. DispComp and FC quantify degree of variability of the time
series. Receiver operating characteristic (ROC) analysis reveals that all the
three quantifiers can discriminate between seizure and non-seizure states with
very high accuracy. The application of such a technique is justified by
ascertaining the presence of nonlinearity in the EEG time series through the
use of surrogate test. The false positive rejection of the null hypothesis is
eliminated by employing Welch window before the computation of the Fourier transform
and randomizing the phases, in the generation of the surrogate data. Paired
t-test revealed significant differences between the measures of the original
time series and those of their respective surrogated time series, indicating
the presence of deterministic chaos in the original EEG time series.
Cite this paper
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