Entropies based detection of epileptic seizures with artificial neural network classifiers

Authors:
S. Pravin Kumar;N. Sriraam;P. G. Benakop;B. C. Jinaga
Affiliations:
Center for Biomedical Informatics and Signal Processing, Department of Biomedical Engineering, SSN College of Engineering, Chennai 603110, India;Center for Biomedical Informatics and Signal Processing, Department of Biomedical Engineering, SSN College of Engineering, Chennai 603110, India;Department of ECE, Aurora's Engineering College, Bhongir 508116, AP, India;School of Information Technology, Jawaharlal Nehru Technological University, Kukatpally, Hyderabad, India
Venue:
Expert Systems with Applications: An International Journal
Year:
2010

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Abstract

Computer assisted automated detection is highly inevitable for recognizing neurological disorders, as it involves continuous monitoring of Electroencephalogram (EEG) signal. Being a non-stationary signal, suitable analysis is essential for EEG to differentiate the normal EEG and epileptic seizures. This paper highlights the importance of entropy based features to recognize the normal EEGs, and ictal as well as interictal epileptic seizures. Three non-linear features, such as, wavelet entropy, sample entropy, and spectral entropy are used to extract quantitative entropy features from the given EEG time using two neural network models, namely, recurrent Elman network (REN) and radial basis network (RBN) are then incorporated for the purpose of classification. The stationary properties of the EEG are exploited by estimating entropies at various time frames and the performance of the proposed scheme is evaluated using specificity, sensitivity and classification accuracy. From the experimental results, it is found that among the different entropies applied, the wavelet entropy features with recurrent Elman networks yields 99.75% and 94.5% accuracy for detecting normal vs. epileptic seizures and interictal focal seizures respectively.