Please use this identifier to cite or link to this item:
Title: Entropies for detection of epilepsy in EEG
Authors: Kannathal, N.
Choo, M.L.
Acharya, U.R.
Sadasivan, P.K. 
Keywords: ANFIS classifier
Approximate entropy
Kolmogorov entropy
Renyi entropy
Spectral entropy
Issue Date: Dec-2005
Citation: Kannathal, N., Choo, M.L., Acharya, U.R., Sadasivan, P.K. (2005-12). Entropies for detection of epilepsy in EEG. Computer Methods and Programs in Biomedicine 80 (3) : 187-194. ScholarBank@NUS Repository.
Abstract: The electroencephalogram (EEG) is a representative signal containing information about the condition of the brain. The shape of the wave may contain useful information about the state of the brain. However, the human observer cannot directly monitor these subtle details. Besides, since bio-signals are highly subjective, the symptoms may appear at random in the time scale. Therefore, the EEG signal parameters, extracted and analyzed using computers, are highly useful in diagnostics. The aim of this work is to compare the different entropy estimators when applied to EEG data from normal and epileptic subjects. The results obtained indicate that entropy estimators can distinguish normal and epileptic EEG data with more than 95% confidence (using t-test). The classification ability of the entropy measures is tested using ANFIS classifier. The results are promising and a classification accuracy of about 90% is achieved. © 2005 Elsevier Ireland Ltd. All rights reserved.
Source Title: Computer Methods and Programs in Biomedicine
ISSN: 01692607
DOI: 10.1016/j.cmpb.2005.06.012
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.


checked on Feb 18, 2019


checked on Feb 11, 2019

Page view(s)

checked on Feb 9, 2019

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.