Entropies for detection of epilepsy in EEG

• Published in: Computer methods and programs in biomedicine Vol. 80; no. 3; pp. 187 - 194
• Main Authors: Kannathal, N., Choo, Min Lim, Acharya, U. Rajendra, Sadasivan, P.K.
• Format: Journal Article
• Language: English
• Published: Ireland Elsevier Ireland Ltd 01.12.2005
• Subjects: Algorithms; ANFIS classifier; Approximate entropy; Artificial Intelligence; Computer Simulation; Diagnosis, Computer-Assisted - methods; Electroencephalogram; Electroencephalography - methods; Entropy; Epilepsy; Epilepsy - diagnosis; Humans; Kolmogorov entropy; Models, Neurological; Models, Statistical; Nerve Net; Pattern Recognition, Automated - methods; Renyi entropy; Reproducibility of Results; Retrospective Studies; Sensitivity and Specificity; Signal Processing, Computer-Assisted; Spectral entropy; Spectral entropy; ANFIS classifier; Electroencephalogram; Epilepsy; Approximate entropy; Kolmogorov entropy; Renyi entropy
• ISSN: 0169-2607; 1872-7565
• DOI: 10.1016/j.cmpb.2005.06.012

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.