title: Entropies for detection of epilepsy in EEG.
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.
authors.affiliation: Department of ECE, National University of Singapore, Singapore 119260, Singapore. kna2@np.edu.sg
authors.forename: N
authors.initials: N
authors.lastname: Kannathal
authors.forename: Min Lim
authors.initials: ML
authors.lastname: Choo
authors.forename: U Rajendra
authors.initials: UR
authors.lastname: Acharya
authors.forename: P K
authors.initials: PK
authors.lastname: Sadasivan
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004569:Electroencephalography / Q000379:methods*; D019277:Entropy; D004827:Epilepsy / Q000175:diagnosis*; D006801:Humans; D008959:Models, Neurological; D015233:Models, Statistical; D009415:Nerve Net; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012189:Retrospective Studies; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted