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Title: Automated diagnosis of epileptic EEG using entropies
Authors: Molinari, Filippo
Sree, Subbhuraam Vinitha
Acharya, U. Rajendra
Suri, Jasjit S.
Chattopadhyay, Subhagata
Ng, Kwan-Hoong
Issue Date: 2011
Source: Acharya, 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.
Series/Report no.: Biomedical signal processing and control
Abstract: Epilepsy 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.
ISSN: 1746-8094
DOI: 10.1016/j.bspc.2011.07.007
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:MAE Journal Articles

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