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dc.contributor.advisorBang, Børre
dc.contributor.authorLin, Jiaxin
dc.date.accessioned2017-08-25T06:37:31Z
dc.date.available2017-08-25T06:37:31Z
dc.date.issued2017-06-13
dc.description.abstractThis thesis presents a brief introduction to epilepsy diagnosis use convolution neutral network. The datasets create through Electroencephao-graph(EEG) signal.Electroencephao-graph(EEG) is one of the most commonly signals recorded from humans like electrocardiogram(ECG). In this thesis give a short introduce about the EEG signal. Because EEG recordings are important in the diagnosis of epilepsy. But there is not a good way to classification these EEG signal. This paper aims to develop epileptic EEG signal classification method.en_US
dc.identifier.urihttps://hdl.handle.net/10037/11382
dc.language.isoengen_US
dc.publisherUiT Norges arktiske universiteten_US
dc.publisherUiT The Arctic University of Norwayen_US
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2017 The Author(s)
dc.subject.courseIDSHO6264
dc.subjectVDP::Teknologi: 500::Informasjons- og kommunikasjonsteknologi: 550en_US
dc.subjectVDP::Technology: 500::Information and communication technology: 550en_US
dc.subjectCNN EEG-signalen_US
dc.titleClassification of electroncephao-graph signals by convolutional neural networken_US
dc.typeMaster thesisen_US
dc.typeMastergradsoppgaveen_US


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