Epileptic EEG detection using neural networks and post-classification
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2008
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Abstract
Electroencephalogram (EEG) has established itself as an important means of identifying and analyzing epileptic seizure activity in humans. In most cases, identification of the epileptic EEG signal is done manually by skilled professionals, who are small in number. In this paper, we try to automate the detection process. We use wavelet transform for feature extraction and obtain statistical parameters from the decomposed wavelet co-efficients. A feed-forward backpropagating artificial neural network (ANN) is used for the classification. We use genetic algorithm for choosing the training set and also implement a post-classification stage using harmonic weights to increase the accuracy. Average specificity of 99.19%, sensitivity of 91.29% and selectivity of 91.14% are obtained. © 2008 Elsevier Ireland Ltd. All rights reserved.
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Discrete wavelet transforms, Feature extraction, Genetic algorithms, Neural networks, Resilient backpropagation, Skilled professionals, Electroencephalography, accuracy, article, artificial neural network, automation, data extraction, disease classification, electroencephalogram, epilepsy, genetic algorithm, sensitivity and specificity, statistical parameters, Algorithms, Diagnosis, Computer-Assisted, Epilepsy, Humans, Neural Networks (Computer), Pattern Recognition, Automated, Reproducibility of Results, Sensitivity and Specificity
Citation
Computer Methods and Programs in Biomedicine, 2008, 91, 2, pp. 100-109