Nonlinear dynamical systems to study epileptic seizures and extract average amount of mutual information from encephalographs – Part I

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Original Article

Author Details : V Rama Raju*, V Malsoru, K Srinivas, B. Kavitha Rani, G Madhukar

Volume : 8, Issue : 4, Year : 2022

Article Page : 254-259

https://doi.org/10.18231/j.ijn.2022.050

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Abstract

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.
 

Keywords: Epileptic­seizures, Nonlinear dynamical systems, Features, Average amount of mutual information, Recurrence

How to cite : Raju V R, Malsoru V, Srinivas K, Rani B K, Madhukar G, Nonlinear dynamical systems to study epileptic seizures and extract average amount of mutual information from encephalographs – Part I. IP Indian J Neurosci 2022;8(4):254-259

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