cid 371fda6859f42d217ba66a124110ca4c76f46c4d31dc7e1b6a97553c6d61fdea · original · via openalex
Title: AUTOMATED DIAGNOSIS OF EPILEPSY USING CWT, HOS AND TEXTURE PARAMETERS Authors: U. Rajendra Acharya; Ratna Yanti; JIA WEI ZHENG; M. Muthu Rama Krishnan; Jen Hong Tan; Roshan Joy Martis; Choo Min Lim Venue: International Journal of Neural Systems Year: 2013 Canonical: doi:10.1142/s0129065713500093 URL: https://doi.org/10.1142/s0129065713500093 Collector: openalex OA: false ABSTRACT Epilepsy is a chronic brain disorder which manifests as recurrent seizures. Electroencephalogram (EEG) signals are generally analyzed to study the characteristics of epileptic seizures. In this work, we propose a method for the automated classification of EEG signals into normal, interictal and ictal classes using Continuous Wavelet Transform (CWT), Higher Order Spectra (HOS) and textures. First the CWT plot was obtained for the EEG signals and then the HOS and texture features were extracted from these plots. Then the statistically significant features were fed to four classifiers namely Decision Tree (DT), K-Nearest Neighbor (KNN), Probabilistic Neural Network (PNN) and Support Vector Machine (SVM) to select the best classifier. We observed that the SVM classifier with Radial Basis Function (RBF) kernel function yielded the best results with an average accuracy of 96%, average sensitivity of 96.9% and average specificity of 97% for 23.6 s duration of EEG data. Our proposed technique can be used as an automatic seizure monitoring software. It can also assist the doctors to cross check the efficacy of their prescribed drugs.