Paper Info
Title
Automated Diagnosis Of Epilepsy Using Cwt, Hos And Texture Parameters
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 23.6s 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.
Year
DOI
Venue
2013
10.1142/S0129065713500093
INTERNATIONAL JOURNAL OF NEURAL SYSTEMS
Keywords
Field
DocType
Electrocardiogram, higher order statistics, epilepsy, ictal, interictal, discrete wavelet transform, classifier
Computer science,Continuous wavelet transform,Discrete wavelet transform,Artificial intelligence,Classifier (linguistics),Electroencephalography,Pattern recognition,Support vector machine,Higher-order statistics,Speech recognition,Probabilistic neural network,Machine learning,Ictal
Journal
Volume
Issue
ISSN
23
3
0129-0657
Citations 
PageRank 
References 
38
1.01
26
Authors
7
Name
Order
Citations
PageRank
Rajendra Acharya U14666296.34
Ratna Yanti21816.21
Jia Wei Zheng3381.01
M. Muthu Rama Krishnan41307.14
Jen-Hong Tan574532.04
Roshan Joy Martis673029.90
Choo Min Lim744628.35