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A classification on brain wave patterns for Parkinson�s patients using WEKA

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dc.Conferencecode154059
dc.Conferencedate8 December 2014 through 11 December 2014
dc.Conferencename4th World Congress on Information and Communication Technologies, WICT 2014
dc.FundingDetailsMinistry of Higher Education, Malaysia,�MOHE
dc.citedby4
dc.contributor.affiliationsUniversiti Sains Islam Malaysia (USIM)
dc.contributor.affiliationsUniversiti Kuala Lumpur (UNIKL)
dc.contributor.affiliationsUniversiti Kebangsaan Malaysia (UKM)
dc.contributor.authorMahfuz N.en_US
dc.contributor.authorIsmail W.en_US
dc.contributor.authorNoh N.A.en_US
dc.contributor.authorJali M.Z.en_US
dc.contributor.authorAbdullah D.en_US
dc.contributor.authorNordin M.J.B.en_US
dc.date.accessioned2024-05-29T01:58:42Z
dc.date.available2024-05-29T01:58:42Z
dc.date.issued2015
dc.description.abstractIn this paper, classification of brain wave using real-world data from Parkinson�s patients in producing an emotional model is presented. Electroencephalograph (EEG) signal is recorded on eleven Parkinson�s patients. This paper aims to find the �best� classification for brain wave patterns in patients with Parkinson�s disease. This work performed is based on the four phases, which are first phase is raw data and after data processing using statistical features such as mean and standard deviation. The second phase is the sum of hertz, the third is the sum of hertz divided by the number of hertz, and last is the sum of hertz divided by total hertz. We are using five attributes that are patients, class, domain, location, and hertz. The data were classified using WEKA. The results showed that BayesNet gave a consistent result for all the phases from multilayer perceptron and K-Means. However, K-Mean gave the highest result in the first phase. Our results are based on a real-world data from Parkinson�s patients. � Springer International Publishing Switzerland 2015.
dc.description.natureFinalen_US
dc.editorAbraham A.Muda A.K.Choo Y.-H.en_US
dc.identifier.doi10.1007/978-3-319-17398-6_3
dc.identifier.epage33
dc.identifier.isbn9783320000000
dc.identifier.issn21945357
dc.identifier.scopus2-s2.0-84946434153
dc.identifier.spage21
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84946434153&doi=10.1007%2f978-3-319-17398-6_3&partnerID=40&md5=2c79d9c3d516810c36bde1af11c9f8ce
dc.identifier.urihttps://oarep.usim.edu.my/handle/123456789/10016
dc.identifier.volume355
dc.languageEnglish
dc.language.isoen_US
dc.publisherSpringer Verlagen_US
dc.relation.ispartofAdvances in Intelligent Systems and Computing
dc.sourceScopus
dc.subjectElectroencephalographyen_US
dc.subjectBrain waveen_US
dc.subjectEmotional modelsen_US
dc.subjectK-meansen_US
dc.subjectMean and standard deviationsen_US
dc.subjectParkinsonen_US
dc.subjectReal-worlden_US
dc.subjectSecond phaseen_US
dc.subjectStatistical featuresen_US
dc.subjectData handlingen_US
dc.titleA classification on brain wave patterns for Parkinson�s patients using WEKA
dc.typeConference Paperen_US
dspace.entity.typePublication

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