A new approach for epileptic seizure detection using adaptive

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dc.contributor.authorTezel, Guelay
dc.contributor.authorOzbay, Yuksel
dc.date.accessioned2020-03-26T17:37:43Z
dc.date.available2020-03-26T17:37:43Z
dc.date.issued2009
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractThis paper presents new neural network models with adaptive activation function (NNAAF) to detect epileptic seizure. Our NNAAF models included three types named as NNAAF-1, NNAAF-2 and NNAAF-3. The activation function of hidden neuron in the model of NNAAF-1 is sigmoid function with free parameters. In the second model, NNAAF-2, activation function of hidden neuron is sum of sigmoid function with free parameters and sinusoidal function with free parameters. In the third model, NNAAF-3, hidden neurons' activation function is Morlet Wavelet function with free parameters. In addition, we implemented traditional multilayer perceptron (MLP) neural network (NN) model with. fixed sigmoid activation function in the hidden layer to compare NNAAF models. The proposed models were trained and tested using 5-fold cross-validation to prove robustness of these models and to. find the best model. We achieved 100% average sensitivity, average specificity, and approximately 100% average classification rate in all the models. It was seen that their speeds and the number of maximum iteration were changed for each model. The training time and the number of maximum iteration were reduced on about 50% using NNAAF-3 model. Hence it can be remarkable that NNAAF-3 is more suitable than the other models for real-time application. (C) 2007 Elsevier Ltd. All rights reserved.en_US
dc.description.sponsorshipCoordinatorship of Selcuk University's Scientific Research ProjectsSelcuk Universityen_US
dc.description.sponsorshipThis work is supported by the Coordinatorship of Selcuk University's Scientific Research Projects.en_US
dc.identifier.doi10.1016/j.eswa.2007.09.007en_US
dc.identifier.endpage180en_US
dc.identifier.issn0957-4174en_US
dc.identifier.issn1873-6793en_US
dc.identifier.issue1en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage172en_US
dc.identifier.urihttps://dx.doi.org/10.1016/j.eswa.2007.09.007
dc.identifier.urihttps://hdl.handle.net/20.500.12395/23212
dc.identifier.volume36en_US
dc.identifier.wosWOS:000264182800018en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherPERGAMON-ELSEVIER SCIENCE LTDen_US
dc.relation.ispartofEXPERT SYSTEMS WITH APPLICATIONSen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectAdaptive neural networken_US
dc.subjectAdaptive activation functionen_US
dc.subjectMLPen_US
dc.subjectEpileptic seizureen_US
dc.subjectDetectionen_US
dc.subjectEEGen_US
dc.titleA new approach for epileptic seizure detection using adaptiveen_US
dc.typeArticleen_US

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