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dc.contributor.authorMucha, Ján
dc.date.accessioned2020-05-07T09:40:33Z
dc.date.available2020-05-07T09:40:33Z
dc.date.issued2017cs
dc.identifier.citationProceedings of the 23st Conference STUDENT EEICT 2017. s. 619-623. ISBN 978-80-214-5496-5cs
dc.identifier.isbn978-80-214-5496-5
dc.identifier.urihttp://hdl.handle.net/11012/187177
dc.description.abstractParkinson’s disease (PD) is the second most frequent neurodegenerative disorder. It is estimated that 60–90% of PD patients suffer from speech disorder called hypokinetic dysarthria (HD). The goal of this work is to reveal influence of poem recitation on acoustic analysis of speech and propose concept of Parkinson’s disease identification based on this analysis. Classification methods used in this work are Random Forests and Support Vector Machine. The best achieved accuracy of disease identification is 70.66% with 59.25% sensitivity for Random Forests classifier fed mainly with articulation features. These results demonstrate a high potential of research in this area.en
dc.formattextcs
dc.format.extent619-623cs
dc.format.mimetypeapplication/pdfen
dc.language.isoencs
dc.publisherVysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologiícs
dc.relation.ispartofProceedings of the 23st Conference STUDENT EEICT 2017en
dc.relation.urihttp://www.feec.vutbr.cz/EEICT/cs
dc.rights© Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologiícs
dc.subjectpoem recitationen
dc.subjectacoustic analysisen
dc.subjectbinary classificationen
dc.subjectParkinson’s diseaseen
dc.subjecthypokinetic dysarthriaen
dc.titleIdentification Of Parkinson’S Disease Using Acousticanalysis Of Poem Recitationen
eprints.affiliatedInstitution.departmentFakulta elektrotechniky a komunikačních technologiícs
but.event.date27.04.2017cs
but.event.titleStudent EEICT 2017cs
dc.rights.accessopenAccessen
dc.type.driverconferenceObjecten
dc.type.statusPeer-revieweden
dc.type.versionPublishers's versionen


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