Abstract
Epilepsy is a common chronic neurological disorder that is characterized by recurrent unprovoked seizures. About 50 million people worldwide have epilepsy at any one time. This paper presents an Intelligent Diagnostic System for Epilepsy using Artificial Neural Networks (ANNs). In this approach the feed-forward neural network has been trained using three ANN algorithms, the Back propagation algorithm (BPA,), the Radial Basis Function (RBF) and the Learning Vector Quantization (LVQ). The simulator has been developed using MATLAB and performance is compared by considering the metrics like accuracy of diagnosis, training time, number of neurons, number of epochs etc. The results obtained clearly shows that the presented methods have improved the inference procedures and are advantageous over the conventional architectures on both efficiency and accuracy.
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Shukla, A., Tiwari, R., Kaur, P. (2009). Diagnosis of Epilepsy Disorders Using Artificial Neural Networks. In: Wang, H., Shen, Y., Huang, T., Zeng, Z. (eds) The Sixth International Symposium on Neural Networks (ISNN 2009). Advances in Intelligent and Soft Computing, vol 56. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01216-7_86
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DOI: https://doi.org/10.1007/978-3-642-01216-7_86
Publisher Name: Springer, Berlin, Heidelberg
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