Please use this identifier to cite or link to this item: http://doi.org/10.25358/openscience-8593
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dc.contributor.authorHossen, Abdulnasir-
dc.contributor.authorAnwar, Abdul Rauf-
dc.contributor.authorKoirala, Nabin-
dc.contributor.authorDing, Hao-
dc.contributor.authorBudker, Dmitry-
dc.contributor.authorWickenbrock, Arne-
dc.contributor.authorHeute, Ulrich-
dc.contributor.authorDeuschl, Günther-
dc.contributor.authorGroppa, Sergiu-
dc.contributor.authorMuthuraman, Muthuraman-
dc.date.accessioned2023-01-20T08:18:00Z-
dc.date.available2023-01-20T08:18:00Z-
dc.date.issued2022-
dc.identifier.urihttps://openscience.ub.uni-mainz.de/handle/20.500.12030/8609-
dc.description.abstractBackground Tremors are frequent and disabling in people with multiple sclerosis (MS). Characteristic tremor frequencies in MS have a broad distribution range (1–10 Hz), which confounds the diagnostic from other forms of tremors. In this study, we propose a classification method for distinguishing MS tremors from other forms of cerebellar tremors. Methods Electromyogram (EMG), accelerometer and clinical data were obtained from a total of 120 [40 MS, 41 essential tremor (ET) and 39 Parkinson's disease (PD)] subjects. The proposed method - Soft Decision Wavelet Decomposition (SDWD) - was used to compute power spectral densities and receiver operating characteristic (ROC) analysis was performed for the automatic classification of the tremors. Association between the spectral features and clinical features (FTM - Fahn-Tolosa-Marin scale, UPDRS - Unified Parkinson's Disease Rating Scale), was assessed using a support vector regression (SVR) model. Findings Our developed analytical framework achieved an accuracy of up to 91.67% using accelerometer data and up to 91.60% using EMG signals for the differentiation of MS tremors and the tremors from ET and PD. In addition, SVR further revealed strong significant correlations between the selected discriminators and the clinical scores. Interpretation The proposed method, with high classification accuracy and strong correlations of these features to clinical outcomes, has clearly demonstrated the potential to complement the existing tremor-diagnostic approach in MS patients.en_GB
dc.description.sponsorshipGefördert durch die Deutsche Forschungsgemeinschaft (DFG) - Projektnummer 491381577de
dc.language.isoengde
dc.rightsCC BY-NC-ND*
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subject.ddc610 Medizinde_DE
dc.subject.ddc610 Medical sciencesen_GB
dc.titleMachine learning aided classification of tremor in multiple sclerosisen_GB
dc.typeZeitschriftenaufsatzde
dc.identifier.doihttp://doi.org/10.25358/openscience-8593-
jgu.type.contenttypeScientific articlede
jgu.type.dinitypearticleen_GB
jgu.type.versionPublished versionde
jgu.type.resourceTextde
jgu.organisation.departmentFB 04 Medizinde
jgu.organisation.number2700-
jgu.organisation.nameJohannes Gutenberg-Universität Mainz-
jgu.rights.accessrightsopenAccess-
jgu.journal.titleeBioMedicinede
jgu.journal.volume82de
jgu.pages.alternative104152de
jgu.publisher.year2022-
jgu.publisher.nameElsevierde
jgu.publisher.placeAmsterdamde
jgu.publisher.issn2352-3964de
jgu.organisation.placeMainz-
jgu.subject.ddccode610de
jgu.publisher.doi10.1016/j.ebiom.2022.104152de
jgu.organisation.rorhttps://ror.org/023b0x485-
jgu.subject.dfgLebenswissenschaftende
Appears in collections:DFG-491381577-G

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