Title of article :
Entropies based detection of epileptic seizures with artificial neural network classifiers
Author/Authors :
Pravin Kumar، نويسنده , , S. and Sriraam، نويسنده , , N. and Benakop، نويسنده , , P.G. and Jinaga، نويسنده , , B.C.، نويسنده ,
Issue Information :
روزنامه با شماره پیاپی سال 2010
Pages :
8
From page :
3284
To page :
3291
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.
Keywords :
EEG , Epilepsy , Recurrent neural network , Sample Entropy , Classification , Spectral entropy , Wavelet entropy
Journal title :
Expert Systems with Applications
Serial Year :
2010
Journal title :
Expert Systems with Applications
Record number :
2347706
Link To Document :
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