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• W2079304444 abstract "This paper introduces different classification systems based on artificial neural networks for the automatic detection of epileptic spikes in electroencephalogram records. Different multilayer perceptron networks are constructed and trained with different algorithms. The inputs of the networks consist of either raw data or extracted features. To improve the generalization performance of the classifiers, “training with noise” method is used whereby new training data is constructed by adding uncorrelated Gaussian noise to real data. The performances of the constructed classifiers are examined and compared both with each other and with other similar systems found in literature based on sensitivity, specificity and selectivity measures." @default.
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• W2079304444 date "2009-05-01" @default.
• W2079304444 modified "2023-09-26" @default.
• W2079304444 title "Optimizing the performance of an MLP classifier for the automatic detection of epileptic spikes" @default.
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• W2079304444 doi "https://doi.org/10.1016/j.eswa.2008.09.052" @default.
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