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dc.contributor.authorMucha, Ján
dc.date.accessioned2019-03-04T10:05:53Z
dc.date.available2019-03-04T10:05:53Z
dc.date.issued2018cs
dc.identifier.citationProceedings of the 24th Conference STUDENT EEICT 2018. s. 398-402. ISBN 978-80-214-5614-3cs
dc.identifier.isbn978-80-214-5614-3
dc.identifier.urihttp://hdl.handle.net/11012/138264
dc.description.abstractParkinson’s disease (PD) is the second most frequent neurodegenerative disorder. One typical hallmark of PD is disruption in execution of practised skills such as handwriting. This paper introduces a new methodology of kinematic features calculation based on fractional derivatives applied on PD handwriting. Discrimination power of basic kinematic features (velocity, acceleration, jerk) was evaluated by classification analysis (using support vector machines and random forests). For this purpose, 37 PD patients and 38 healthy controls were enrolled. In comparison to results reported in other works, we proved that FDE in online handwriting analysis brings promising improvements. The best result of multivariate analysis was achieved with 83:89% classification accuracy in combination with 5 features using only one handwriting task (overlapped circles). This study reveals an impact of fractional derivatives based features in analysis of Parkinsonic dysgraphia.en
dc.formattextcs
dc.format.extent398-402cs
dc.format.mimetypeapplication/pdfen
dc.language.isoencs
dc.publisherVysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologiícs
dc.relation.ispartofProceedings of the 24th Conference STUDENT EEICT 2018en
dc.relation.urihttp://www.feec.vutbr.cz/EEICT/cs
dc.rights© Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologiícs
dc.subjectBinary classificationen
dc.subjectfractal calculusen
dc.subjectfractional derivativeen
dc.subjectonline handwritingen
dc.subjectoverlapped circlesen
dc.subjectParkinson’s diseaseen
dc.titleNew Methodology Of Parkinsonic Dysgraphia Analysis By Online Handwriting Using Fractional Derivativesen
eprints.affiliatedInstitution.departmentFakulta elektrotechniky a komunikačních technologiícs
but.event.date26.04.2018cs
but.event.titleStudent EEICT 2018cs
dc.rights.accessopenAccessen
dc.type.driverconferenceObjecten
dc.type.statusPeer-revieweden
dc.type.versionPublishers's versionen


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