Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/99007
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dc.contributor.authorMolinari, Filippoen
dc.contributor.authorSree, Subbhuraam Vinithaen
dc.contributor.authorAcharya, U. Rajendraen
dc.contributor.authorSuri, Jasjit S.en
dc.contributor.authorChattopadhyay, Subhagataen
dc.contributor.authorNg, Kwan-Hoongen
dc.date.accessioned2013-08-02T03:31:55Zen
dc.date.accessioned2019-12-06T20:02:16Z-
dc.date.available2013-08-02T03:31:55Zen
dc.date.available2019-12-06T20:02:16Z-
dc.date.copyright2011en
dc.date.issued2011en
dc.identifier.citationAcharya, U. R., Molinari, F., Sree, S. V., Chattopadhyay, S., Ng, K. H.,& Suri, J. S. (2012). Automated diagnosis of epileptic EEG using entropies. Biomedical signal processing and control, 7(4), 401-408.en
dc.identifier.issn1746-8094en
dc.identifier.urihttps://hdl.handle.net/10356/99007-
dc.description.abstractEpilepsy is a neurological disorder characterized by the presence of recurring seizures. Like many other neurological disorders, epilepsy can be assessed by the electroencephalogram (EEG). The EEG signal is highly non-linear and non-stationary, and hence, it is difficult to characterize and interpret it. However, it is a well-established clinical technique with low associated costs. In this work, we propose a methodology for the automatic detection of normal, pre-ictal, and ictal conditions from recorded EEG signals. Four entropy features namely Approximate Entropy (ApEn), Sample Entropy (SampEn), Phase Entropy 1 (S1), and Phase Entropy 2 (S2) were extracted from the collected EEG signals. These features were fed to seven different classifiers: Fuzzy Sugeno Classifier (FSC), Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Probabilistic Neural Network (PNN), Decision Tree (DT), Gaussian Mixture Model (GMM), and Naive Bayes Classifier (NBC). Our results show that the Fuzzy classifier was able to differentiate the three classes with a high accuracy of 98.1%. Overall, compared to previous techniques, our proposed strategy is more suitable for diagnosis of epilepsy with higher accuracy.en
dc.language.isoenen
dc.relation.ispartofseriesBiomedical signal processing and controlen
dc.titleAutomated diagnosis of epileptic EEG using entropiesen
dc.typeJournal Articleen
dc.contributor.schoolSchool of Mechanical and Aerospace Engineeringen
dc.identifier.doi10.1016/j.bspc.2011.07.007en
item.fulltextNo Fulltext-
item.grantfulltextnone-
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