In this study we attempted to design, develop and substantiate a modern contemporary biomarker for epileptic (epilepsy) subjects (patients) neuronal-instability. Initial study is done on 91 subjects through the application of neuronal-unpredictability and/or variability of the marked e-SoZ as a metric to envisage and foresee the epileptic operational (surgical) outcome. the neural-instability predict (42/45) subjects unsuccess with surgery, by a total accuracy of 75% (predictive) when matched with subjective-clinicians accuracy at 49%(results-effective). We differentiate instable zonal areas (zones) which were not diagnosed in unsuccessful cases (i.e., unsuccessful outcomes). While compared with EEG features, the neural-variability outpaced in prognosis strength and, also construal, which support that neuronal delicacy as a bio-marker for the electro encephalography e-SoZ.
In this study, we apply the non-linear dynamical systems theory for the assessment built on recurrence-quantification analysis technique for characterizing—differentiating non-linear electro encephalograph (EEG) signals dynamics. The technique offers convenient quantifiable data plus information over normal, tumultuous, or probability and statistical stochastic properties of inherent systems dynamics theory. The R.Q.A-established processes as the quantifiable mathematical features of non-linear electroencephalograph signals dynamics. Average amount of mutual information (AAMI) applied to compute highly applicable feature-manifestation sub-sets out of R.Q.A-built centered-features. The chosen features were then fed into the computer using artificial intelligence based neural net-works for clustering the data of encephalograph-signals to identify ictic(i.e.,ictal), inter ictal, followed by state of controls. The study is implemented by validating R.Q.A with a data base for various issues of categorization. Results showed that the combination of five selected features created on AAMI attained the precision of100% and proves dominance of R.Q.A. Nonlinear dynamical control systems theory and analysis techniques centered on R.Q.A can be used as an appropriate methodology for distinguishing the non-linear systems dynamics of encephalograph signals data also epileptic seizures tracing.
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