Abstract
Epilepsy is a common neurological disorder that is characterized by recurrent unprovoked seizures. About 40 to 50 million people worldwide are reported to have epilepsy. In this paper the authors present clinical decision support system (DSS) for the diagnosis of epilepsy. The DSS is developed by using Multilayer Perceptron (MLP), Generalized Feed Forward Neural Network (GFF-NN) and Elman Neural Network (E-NN). The validity of neural networks to diagnose the epilepsy is checked and the most suitable neural network is recommended for the diagnosis of epilepsy. Also the different feature enhancement techniques like principal component analysis (PCA), FFT and statistical parameters are used for the input dimensionality reduction. Epilepsy diagnosis is modeled as the classification of normal EEG, interictal EEG and ictal EEG. With the different input dimensionality reduction methods performance parameters of MLP, GFF-NN and E-NN are measured and compared. For the GFF-NN, number of free parameter is reduced up to 92.22% when PCA is used for input dimensionality reduction with the overall accuracy of 98.61%.
Highlights
Epilepsy is a brain disorder in which clusters of nerve cell, or neurons in the brain sometimes signal abnormally
Result and Conclusion The effects of input dimensionality reduction on the performance of automated epileptic diagnosis of epilepsy based on Multilayer Perceptron (MLP), Generalized Feed Forward Neural Network (GFF-NN) and Elman Neural Network (E-NN) are explored in this paper
The performance parameters of these neural networks with different input dimensionality reduction methods are shown in Table-2, Table-4, and Table-5
Summary
Epilepsy is a brain disorder in which clusters of nerve cell, or neurons in the brain sometimes signal abnormally. Anything that disturbs the normal pattern of neurons activity from illness to brain damage to abnormal brain development may cause epilepsy. Neural network based detection system for epileptic diagnosis has been proposed by several authors [10]-[17]. Sriraam [10]-[11] use Recurrent neural network classifier with wavelet entropy and spectral entropy features as the input for the automated detection of epilepsy. This paper explores methods by which a Neural Network can diagnose epilepsy with the help of EEG signal. The Artificial neural network used can help real genuine patients, which will reduce the time and cost required for diagnosis. Such a system is very useful to assist the doctor.
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