Please use this identifier to cite or link to this item:
http://doi.org/10.25358/openscience-8593
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 |
ROR: | https://ror.org/023b0x485 |
DOI: | http://doi.org/10.25358/openscience-8593 |
Version: | Published version |
Publication type: | Zeitschriftenaufsatz |
Document type specification: | Scientific article |
License: | CC BY-NC-ND |
Information on rights of use: | https://creativecommons.org/licenses/by-nc-nd/4.0/ |
Journal: | eBioMedicine 82 |
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 |
Files in This Item:
File | Description | Size | Format | ||
---|---|---|---|---|---|
![]() | machine_learning_aided_classi-20230119101143154.pdf | 625.57 kB | Adobe PDF | View/Open |