Delivered by Ingenta to: Nanyang Technological University IP: 46.148.31.12 On: Fri, 17 Jun 2016 11:32:52 Copyright: American Scientific Publishers Copyright © 2016 American Scientific Publishers All rights reserved Printed in the United States of America RESEARCH ARTICLE Journal of Medical Imaging and Health Informatics Vol. 6, 526–531, 2016 Automatic Detection of Epileptic Seizures Using Permutation Entropy, Tsallis Entropy and Kolmogorov Complexity N. Arunkumar 1 ∗ , K. Ram Kumar 1 , and V. Venkataraman 2 1 Department of Electronics and Instrumentation, SASTRA University, Thanjavur 613401, India 2 Department of Mathematics, SASTRA University, Thanjavur 613401, India Epilepsy is a neurological disorder which is characterized by repeated seizures. Although these can be recorded using Electroencephalogram (EEG), it is very difficult task to classify the different states such as normal, preictal and ictal by visual inspection due to the non-linear and non stationary property of the EEG signals and also due to its long term recordings. Thus automatic detection of these states using various techniques has been in the literature for a very long time. We used novel features such as permutation entropy (PE), Tsallis entropy (TsE), and Kolmogorov complexity (KC) along with five different classifiers. We achieved a highest accuracy of 89.33% with Decision tree classifier. Our method being very simple and has fast computation time in comparison with other features in the literature and thus can form as a software tool that can be installed easily and also opens future opportunities towards real time detection and prediction of epileptic seizures. Keywords: Epilepsy, Entropy, Ictal, Preictal, EEG. 1. INTRODUCTION Epilepsy is a neurological disorder in which a person experi- ences sudden seizures resulting in convolutions of his muscles and experiences even loss of consciousness at certain condition. About 50 million people i.e., roughly 1% of population in this world have epilepsy. 1 2 Socially, a person with this disorder is not easily received even to the degree of prohibiting marriages. Although by the disease itself, it does not lead to dangerous con- dition, but it can be dangerous if the person is swimming or driving due to loss of consciousness. Seizures are of different types. They are classified primarily based on the source of the seizure within the brain namely local- ized or distributed. The localized seizures are named as partial or focal seizures. Partial seizures are further classified as sim- ple partial seizure and complex partial seizure. If ones aware- ness is unaffected then it is called as simple partial seizure and if ones awareness is affected it is called as complex par- tial seizure. And generally seizures are classified according to the effect on the body but all of them involve loss of con- sciousness. These include absence (petit mal), myoclonic, clonic, tonic, tonic-clonic (grand mal), and atonic seizures. In general they have an ictal period during which the patient experiences the seizures and a period preceding that called pre ictal period. ∗ Author to whom correspondence should be addressed. Epileptic seizures can be recorded along with recording of the Electroencephalogram (EEG). A lot of works are done in the automatic detection of epileptic seizures. Broadly speaking, researchers have used long term EEG data or EEG data segments for detecting the epileptic seizures. Bonn University database 38 has EEG segments of a fixed duration of three different categories namely normal, ictal and preictal. Many researchers have employed different algorithms and have got various accuracy levels as shown in Table V. In terms of techniques, starting from the 1970’s different meth- ods has been employed. Initially heuristic and descriptive meth- ods were used for the detection of epileptic seizures. 4 Later time domain methods, frequency domain methods, time frequency domain methods and other nonlinear methods were all attempted for seizure detection. 5 Linear discriminant analysis (LDA), his- togram methods were also used for automatically detecting the epileptic seizures. 6 7 Seizure termination was identified using sample entropy. 8 Differential operation was also used to iden- tify the seizures. 9 Recurrence quantification Analysis (RQA), 10 Higher order spectra (HOS), 11 Hurst exponent (H), 12 different entropies 1 were all employed for the identification of epileptic seizures. There are also works where they have employed wavelet transforms with single level analysis for detecting the epileptic seizures. 13 14 Multilevel wavelet approach was also employed by Indiradevi et al. 15 for automatically detect the epileptic spikes. 526 J. Med. Imaging Health Inf. Vol. 6, No. 2, 2016 2156-7018/2016/6/526/006 doi:10.1166/jmihi.2016.1710