Intracranial current signals classification using multivariate autoregressive modeling and simulated annealing technique

Vasios, CE; Matsopoulos, GK; Ventouras, EM; Papageorgiou, CC; Nikita, KS; Kontaxakis, VP; Christodoulou, GN; Uzunoglu, NK

dc.contributor.author	Vasios, CE	en
dc.contributor.author	Matsopoulos, GK	en
dc.contributor.author	Ventouras, EM	en
dc.contributor.author	Papageorgiou, CC	en
dc.contributor.author	Nikita, KS	en
dc.contributor.author	Kontaxakis, VP	en
dc.contributor.author	Christodoulou, GN	en
dc.contributor.author	Uzunoglu, NK	en
dc.date.accessioned	2014-03-01T02:49:25Z
dc.date.available	2014-03-01T02:49:25Z
dc.date.issued	2003	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/34591
dc.relation.uri	http://www.scopus.com/inward/record.url?eid=2-s2.0-2642555691&partnerID=40&md5=594066293da95cb6ce984ebef6f669bc	en
dc.subject	Classification	en
dc.subject	ERPs	en
dc.subject	Intracranial Current Source	en
dc.subject	Multivariate Autoregression	en
dc.subject	Neural Network	en
dc.subject	Simulated Annealing	en
dc.subject.other	Algorithms	en
dc.subject.other	Brain	en
dc.subject.other	Diseases	en
dc.subject.other	Neurology	en
dc.subject.other	Pathology	en
dc.subject.other	Patient monitoring	en
dc.subject.other	Regression analysis	en
dc.subject.other	Signal processing	en
dc.subject.other	Simulated annealing	en
dc.subject.other	Classification	en
dc.subject.other	ERPs	en
dc.subject.other	Intracranial current source	en
dc.subject.other	Multivariate autoregression	en
dc.subject.other	Bioelectric phenomena	en
dc.title	Intracranial current signals classification using multivariate autoregressive modeling and simulated annealing technique	en
heal.type	conferenceItem	en
heal.publicationDate	2003	en
heal.abstract	Intracranial currents computed by the scalp-recorded ERPs, provide information on the non-observable electrical phenomena taking place in the brain, related to the cognitive mechanisms induced by the experimental task used in the ERP recording procedure. The use of current source waveforms, as input in classification systems, may provide robust classifiers due to the immediate relationship of the current sources to the brain electrical activity related to cognitive mechanisms. In the present work, a new method for the classification of intracranial current sources is proposed, combining the Multivariate Autoregressive model with the Simulated Annealing technique, in order to extract optimum features, in terms of the classification rate. The classification is implemented using a three-layer neural network (NN) trained with the back-propagation algorithm. The system was applied in the classification of normal controls and schizophrenic patients, providing classification rates of up to 100%. Furthermore, the clustering of intracranial source locations providing best classification performance may indicate relationships between the brain areas corresponding to these locations and pathological mechanisms.	en
heal.journalName	Proceedings of the IASTED International Conference on Biomedical Engineering	en
dc.identifier.spage	33	en
dc.identifier.epage	38	en