Identification and Monitoring of Parkinson’s Disease Dysgraphia Based on Fractional-Order Derivatives of Online Handwriting

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dc.contributor.authorMucha, Jáncs
dc.contributor.authorMekyska, Jiřícs
dc.contributor.authorGaláž, Zoltáncs
dc.contributor.authorFaúndez Zanuy, Marcoscs
dc.contributor.authorLopez-de-Ipina, Karmelecs
dc.contributor.authorZvončák, Vojtěchcs
dc.contributor.authorKiska, Tomášcs
dc.contributor.authorSmékal, Zdeněkcs
dc.contributor.authorBrabenec, Lubošcs
dc.contributor.authorRektorová, Irenacs
dc.coverage.issue12cs
dc.coverage.volume8cs
dc.date.accessioned2020-08-04T11:00:57Z
dc.date.available2020-08-04T11:00:57Z
dc.date.issued2019-01-11cs
dc.description.abstractParkinson’s disease dysgraphia affects the majority of Parkinson’s disease (PD) patients and is the result of handwriting abnormalities mainly caused by motor dysfunctions. Several effective approaches to quantitative PD dysgraphia analysis, such as online handwriting processing, have been utilized. In this study, we aim to deeply explore the impact of advanced online handwriting parameterization based on fractional-order derivatives (FD) on the PD dysgraphia diagnosis and its monitoring. For this purpose, we used 33 PD patients and 36 healthy controls from the PaHaW (PD handwriting database). Partial correlation analysis (Spearman’s and Pearson’s) was performed to investigate the relationship between the newly designed features and patients’ clinical data. Next, the discrimination power of the FD features was evaluated by a binary classification analysis. Finally, regression models were trained to explore the new features’ ability to assess the progress and severity of PD. These results were compared to a baseline, which is based on conventional online handwriting features. In comparison with the conventional parameters, the FD handwriting features correlated more significantly with the patients’ clinical characteristics and provided a more accurate assessment of PD severity (error around 12%). On the other hand, the highest classification accuracy (ACC = 97.14%) was obtained by the conventional parameters. The results of this study suggest that utilization of FD in combination with properly selected tasks (continuous and/or repetitive, such as the Archimedean spiral) could improve computerized PD severity assessmenten
dc.formattextcs
dc.format.extent1-18cs
dc.format.mimetypeapplication/pdfcs
dc.identifier.citationApplied Sciences - Basel. 2019, vol. 8, issue 12, p. 1-18.en
dc.identifier.doi10.3390/app8122566cs
dc.identifier.issn2076-3417cs
dc.identifier.other151716cs
dc.identifier.urihttp://hdl.handle.net/11012/137218
dc.language.isoencs
dc.publisherMDPIcs
dc.relation.ispartofApplied Sciences - Baselcs
dc.relation.urihttps://www.mdpi.com/2076-3417/8/12/2566cs
dc.rightsCreative Commons Attribution 4.0 Internationalcs
dc.rights.accessopenAccesscs
dc.rights.sherpahttp://www.sherpa.ac.uk/romeo/issn/2076-3417/cs
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/cs
dc.subjectParkinson’s disease dysgraphiaen
dc.subjectmicrographiaen
dc.subjectonline handwritingen
dc.subjectkinematic analysisen
dc.subjectfractional-order derivativeen
dc.subjectfractional calculusen
dc.titleIdentification and Monitoring of Parkinson’s Disease Dysgraphia Based on Fractional-Order Derivatives of Online Handwritingen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
sync.item.dbidVAV-151716en
sync.item.dbtypeVAVen
sync.item.insts2020.08.04 13:00:57en
sync.item.modts2020.08.04 12:25:02en
thesis.grantorVysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií. Ústav telekomunikacícs
thesis.grantorVysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií. oddělení-TKO-SIXcs
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