Please use this identifier to cite or link to this item: https://idr.l3.nitk.ac.in/jspui/handle/123456789/11510
Full metadata record
DC FieldValueLanguage
dc.contributor.authorPatnaik, L.M.-
dc.contributor.authorManyam, O.K.-
dc.date.accessioned2020-03-31T08:31:35Z-
dc.date.available2020-03-31T08:31:35Z-
dc.date.issued2008-
dc.identifier.citationComputer Methods and Programs in Biomedicine, 2008, Vol.91, 2, pp.100-109en_US
dc.identifier.urihttp://idr.nitk.ac.in/jspui/handle/123456789/11510-
dc.description.abstractElectroencephalogram (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.en_US
dc.titleEpileptic EEG detection using neural networks and post-classificationen_US
dc.typeArticleen_US
Appears in Collections:1. Journal Articles

Files in This Item:
File Description SizeFormat 
11510.pdf867.84 kBAdobe PDFThumbnail
View/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.