In this study, we have reinvestigated the chaotic features and sub-band energies of EEG and
its ability for aiding neurologists in detecting epileptic seizures. The study was done on the …

A wavelet-chaos methodology is presented for analysis of EEGs and delta, theta, alpha,
beta, and gamma subbands of EEGs for detection of seizure and epilepsy. The nonlinear …

Detection of seizures in EEG can be challenging because of myogenic artifacts and might be
time-consuming when reviewing long term EEG recordings. In this study, we propose a …

An artificial neural network (ANN)-based method, using a combination of statistical and
chaotic features, is proposed to discriminate electroencephalogram (EEG) signals for …

Epilepsy is a chronic brain disorder which manifests as recurrent seizures.
Electroencephalogram (EEG) signals are generally analyzed to study the characteristics of …

A novel wavelet-chaos-neural network methodology is presented for classification of
electroencephalograms (EEGs) into healthy, ictal, and interictal EEGs. Wavelet analysis is …

A multi-paradigm approach integrating three novel computational paradigms: wavelet
transforms, chaos theory, and artificial neural networks is developed for EEG-based epilepsy …

Traditionally, detection of epileptic seizures based on the visual inspection of neurologists is
tedious, laborious and subjective. To overcome such disadvantages, numerous seizure …

The feature analysis of epileptic EEG is very significant in diagnosis of epilepsy. This paper
introduces two nonlinear features derived from fractal geometry for epileptic EEG analysis …

A novel combination of chaotic features and Adaptive Neuro-Fuzzy Inference System
(ANFIS) is proposed for epileptic seizure recognition. The non-linear dynamics of the …