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dc.contributor.authorMagdolen, J.
dc.contributor.authorZidek, F.
dc.contributor.authorMokran, V.
dc.date.accessioned2016-05-06T11:46:03Z
dc.date.available2016-05-06T11:46:03Z
dc.date.issued1995-06cs
dc.identifier.citationRadioengineering. 1995, vol. 4, č. 2, s. 12-17. ISSN 1210-2512cs
dc.identifier.issn1210-2512
dc.identifier.urihttp://hdl.handle.net/11012/58454
dc.description.abstractAutomatic detection of epileptiform patterns is highly desirable during continuous monitoring of patients with epilepsy. This paper describes an unconvential system for automatic off-line recognition of epileptic sharp transients in the human electroencephalogram (EEG), based on a standard neural network architecture - multi-layer perceptron (MLP), and implemented on a Silicon Graphics Indigo workstation. The system makes comprehensive use of wide spatial contextual information available on 12 channels of EEG and takes advantage of discrete dyadic wavelet transform (DDWT) for efficient parameterisation of EEG data. The EEG database consists of 12 patients, 7 of which are used in the process of training of MLP. The resulting MLP is presented with the testing data set consisting of all data vectors from all 12 patients, and is shown to be capable to recognise a wide variety of epileptic signals.en
dc.formattextcs
dc.format.extent12-17cs
dc.format.mimetypeapplication/pdfen
dc.language.isoencs
dc.publisherSpolečnost pro radioelektronické inženýrstvícs
dc.relation.ispartofRadioengineeringcs
dc.relation.urihttp://www.radioeng.cz/fulltexts/1995/95_02_03.pdfcs
dc.rightsCreative Commons Attribution 3.0 Unported Licenseen
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/en
dc.subjectepilepsyen
dc.subjectmulti-channel EEGen
dc.subjectwavelet transformen
dc.subjectmulti-layer perceptronen
dc.titleRecognition of Epileptiform Patterns in the Human Electroencephalogram Using Multi-Layer Perceptronen
eprints.affiliatedInstitution.facultyFakulta eletrotechniky a komunikačních technologiícs
dc.coverage.issue2cs
dc.coverage.volume4cs
dc.rights.accessopenAccessen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen


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Except where otherwise noted, this item's license is described as Creative Commons Attribution 3.0 Unported License