Adaptive filter theory (3rd ed.)
Adaptive filter theory (3rd ed.)
Detection of spikes with artificial neural networks using raw EEG
Computers and Biomedical Research
Introduction to Artificial Neural Systems
Introduction to Artificial Neural Systems
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Optimizing the performance of an MLP classifier for the automatic detection of epileptic spikes
Expert Systems with Applications: An International Journal
Epileptic EEG detection using the linear prediction error energy
Expert Systems with Applications: An International Journal
A machine learning approach to classify vigilance states in rats
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
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In this paper, we present a two-stage system based on a modified radial basis function network (RBFN) classifier for an automated detection of epileptiform pattern (EP) in an electroencephalographic signal. In the first stage, a discrete perceptron fed by six features are used to classify the peaks into two subgroups: (i) definite non-EPs and (ii) definite EPs and EP-like non-EPs. In the second stage, the peaks falling into the second group are aimed to be separated from each other by a modified RBFN designed by a perturbation method that would function as a post-classifier. If there exist redundant data components in training data set, they can be discarded by analyzing the total disturbance of the RBFN output corresponding to the perturbed inputs. Thus, input dimension size is reduced and network becomes smaller. The classification performance of the system is comparatively evaluated for three different feature sets such as raw EEG data, discrete Fourier transform coefficients, and discrete wavelet transform coefficients.