Epilepsy classification using optimized artificial neural network

Neurological Research
Jagriti Saini, Maitreyee Dutta

Abstract

An Electroencephalogram (EEG) is the result of co-operative actions performed by brain cells. In other words, it can be defined as the time course of extracellular field potentials that are generated due to the synchronous action of cells. It is widely used for the analysis and diagnosis of several conditions. But this clinical data use to be multi-dimensional, context-dependent, complex, and it causes a concoction of various computing related research challenges. The objective of this study was to develop a computer-aided diagnosis system for epilepsy detection using EEG signals to ease the diagnosis process. In this study, EEG datasets for epilepsy disease detection were taken from a public domain (Bonn University, Germany). These EEG recordings contain 100 single-channel EEG signals with maximum duration of 23.6 seconds. This data set was recorded intra-cranially and extra-cranially with the help of a 128-channel amplifier system using a common reference point. For a unique set of EEG signal features, the Optimized Artificial Neural Network model for classification and validation was developed with optimum neurons in the hidden layer. Results were tested on the basis of accuracy, sensitivity, precision, and specificity for a...Continue Reading

References

Jul 1, 1977·Electroencephalography and Clinical Neurophysiology·F H Lopes da SilvaW Vliegenthart
Feb 24, 2004·Neurological Research·Vivek Prakash Nigam, Daniel Graupe
Feb 7, 2007·IEEE Transactions on Bio-medical Engineering·Hiram FirpiJavier Echauz
May 26, 2017·Network : Computation in Neural Systems·Jagriti Saini, Maitreyee Dutta

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Citations

Jan 20, 2022·Computer Methods in Biomechanics and Biomedical Engineering·Tian LuoYi Wang

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