Mixed-Band Wavelet-Chaos-Neural Network Methodology for Epilepsy and Epileptic Seizure Detection

TLDR

Accurate discrimination among healthy, ictal, and interictal EEGs requires judicious combinations of parameters and classifiers. The study introduces a wavelet‑chaos‑neural network approach to classify EEGs into healthy, ictal, and interictal states while reducing computational load through a two‑phase band‑specific and mixed‑band analysis. The method decomposes EEGs into delta, theta, alpha, beta, and gamma sub‑bands, represents them with standard deviation, correlation dimension, and largest Lyapunov exponent, and compares classification performance across k‑means, LDA, QDA, RBF neural network, and Levenberg‑Marquardt backpropagation neural network, exploring over 500 mixed‑band feature combinations in the second phase. The combined wavelet‑chaos‑neural network framework enhances classification accuracy, with a nine‑parameter mixed‑band feature space and LMBPNN achieving a peak accuracy of 96.7 %.

Abstract

A novel wavelet-chaos-neural network methodology is presented for classification of electroencephalograms (EEGs) into healthy, ictal, and interictal EEGs. Wavelet analysis is used to decompose the EEG into delta, theta, alpha, beta, and gamma sub-bands. Three parameters are employed for EEG representation: standard deviation (quantifying the signal variance), correlation dimension, and largest Lyapunov exponent (quantifying the non-linear chaotic dynamics of the signal). The classification accuracies of the following techniques are compared: (1) unsupervised k-means clustering; (2) linear and quadratic discriminant analysis; (3) radial basis function neural network; (4) Levenberg-Marquardt backpropagation neural network (LMBPNN). To reduce the computing time and output analysis, the research was performed in two phases: band-specific analysis and mixed-band analysis. In phase two, over 500 different combinations of mixed-band feature spaces consisting of promising parameters from phase one of the research were investigated. It is concluded that all three key components of the wavelet-chaos-neural network methodology are important for improving the EEG classification accuracy. Judicious combinations of parameters and classifiers are needed to accurately discriminate between the three types of EEGs. It was discovered that a particular mixed-band feature space consisting of nine parameters and LMBPNN result in the highest classification accuracy, a high value of 96.7%.