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Authors: Hossen, Abdulnasir
Anwar, Abdul Rauf
Koirala, Nabin
Ding, Hao
Budker, Dmitry
Wickenbrock, Arne
Heute, Ulrich
Deuschl, Günther
Groppa, Sergiu
Muthuraman, Muthuraman
Title: Machine learning aided classification of tremor in multiple sclerosis
Online publication date: 20-Jan-2023
Year of first publication: 2022
Language: english
Abstract: Background 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.
DDC: 610 Medizin
610 Medical sciences
Institution: Johannes Gutenberg-Universität Mainz
Department: FB 04 Medizin
Place: Mainz
Version: Published version
Publication type: Zeitschriftenaufsatz
Document type specification: Scientific article
License: CC BY-NC-ND
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Journal: eBioMedicine
Pages or article number: 104152
Publisher: Elsevier
Publisher place: Amsterdam
Issue date: 2022
ISSN: 2352-3964
Publisher DOI: 10.1016/j.ebiom.2022.104152
Appears in collections:DFG-491381577-G

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