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Cross-validated classification of Intracranial Sources extracted by BET-ART method

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dc.contributor.author Vasios, CE en
dc.contributor.author Matsopoulos, GK en
dc.contributor.author Ventouras, EM en
dc.contributor.author Papageorgiou, C en
dc.contributor.author Kontaxakis, VP en
dc.contributor.author Nikita, KS en
dc.contributor.author Uzunoglu, N en
dc.date.accessioned 2014-03-01T02:43:11Z
dc.date.available 2014-03-01T02:43:11Z
dc.date.issued 2005 en
dc.identifier.uri http://hdl.handle.net/123456789/31287
dc.subject Algebraic Reconstruction Technique en
dc.subject Artificial Neural Network en
dc.subject Autoregressive Model en
dc.subject Back Propagation Algorithm en
dc.subject Cross Validation en
dc.subject Feature Extraction en
dc.subject Simulated Annealing en
dc.subject Leave One Out Cross Validation en
dc.subject Multi Layer Perceptron en
dc.subject Normal Control en
dc.subject.other Algebra en
dc.subject.other Classification (of information) en
dc.subject.other Feature extraction en
dc.subject.other Mathematical models en
dc.subject.other Regression analysis en
dc.subject.other Simulated annealing en
dc.subject.other Artificial Neural Network (ANN) en
dc.subject.other Back-propagation algorithm en
dc.subject.other Multivariate Autoregressive model en
dc.subject.other Schizophrenic patients en
dc.subject.other Patient treatment en
dc.title Cross-validated classification of Intracranial Sources extracted by BET-ART method en
heal.type conferenceItem en
heal.identifier.primary 10.1109/CNE.2005.1419573 en
heal.identifier.secondary http://dx.doi.org/10.1109/CNE.2005.1419573 en
heal.identifier.secondary 1419573 en
heal.publicationDate 2005 en
heal.abstract In the present paper, a new methodological approach, for the classification of first episode schizophrenic patients (FES) against normal controls, is proposed. The first step of the methodology applied is the feature extraction, which is based on the combination of the Multivariate Autoregressive model with the Simulated Annealing technique, in order to extract optimum features, in terms of classification rate. The classification, as the second step of the methodology, is implemented by means of an Artificial Neural Network (ANN) trained with the back-propagation algorithm under ""leave-one-out cross-validation"". The ANN is a multi-layer perceptron, the architecture of which, is selected after a detailed search. The proposed methodology has been applied for the classification of FES patients and normal controls using as input signals the intracranial current sources obtained by the inversion of ERPs using an Algebraic Reconstruction Technique. Results by implementing the proposed methodology provide classification rates of up to 93.1%. © 2005 IEEE. en
heal.journalName 2nd International IEEE EMBS Conference on Neural Engineering en
dc.identifier.doi 10.1109/CNE.2005.1419573 en
dc.identifier.volume 2005 en
dc.identifier.spage 140 en
dc.identifier.epage 143 en


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