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Title: | Machine learning analysis of motor evoked potential time series to predict disability progression in multiple sclerosis | Authors: | YPERMAN, Jan BECKER, Thijs VALKENBORG, Dirk POPESCU, Veronica HELLINGS, Niels VAN WIJMEERSCH, Bart PEETERS, Liesbet |
Issue Date: | 2020 | Publisher: | BMC | Source: | BMC Neurology, 20 (1) (Art N° 105) | Abstract: | Background Evoked potentials (EPs) are a measure of the conductivity of the central nervous system. They are used to monitor disease progression of multiple sclerosis patients. Previous studies only extracted a few variables from the EPs, which are often further condensed into a single variable: the EP score. We perform a machine learning analysis of motor EP that uses the whole time series, instead of a few variables, to predict disability progression after two years. Obtaining realistic performance estimates of this task has been difficult because of small data set sizes. We recently extracted a dataset of EPs from the Rehabiliation & MS Center in Overpelt, Belgium. Our data set is large enough to obtain, for the first time, a performance estimate on an independent test set containing different patients. Methods We extracted a large number of time series features from the motor EPs with the highly comparative time series analysis software package. Mutual information with the target and the Boruta method are used to find features which contain information not included in the features studied in the literature. We use random forests (RF) and logistic regression (LR) classifiers to predict disability progression after two years. Statistical significance of the performance increase when adding extra features is checked. Results Including extra time series features in motor EPs leads to a statistically significant improvement compared to using only the known features, although the effect is limited in magnitude (Delta AUC = 0.02 for RF and Delta AUC = 0.05 for LR). RF with extra time series features obtains the best performance (AUC = 0.75 +/- 0.07 (mean and standard deviation)), which is good considering the limited number of biomarkers in the model. RF (a nonlinear classifier) outperforms LR (a linear classifier). Conclusions Using machine learning methods on EPs shows promising predictive performance. Using additional EP time series features beyond those already in use leads to a modest increase in performance. Larger datasets, preferably multi-center, are needed for further research. Given a large enough dataset, these models may be used to support clinicians in their decision making process regarding future treatment. | Keywords: | Evoked potentials;Multiple sclerosis;Machine learning;Disease prognosis;Feature extraction | Document URI: | http://hdl.handle.net/1942/33300 | e-ISSN: | 1471-2377 | DOI: | 10.1186/s12883-020-01672-w | ISI #: | 000522029700001 | Rights: | The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. | Category: | A1 | Type: | Journal Contribution | Validations: | ecoom 2021 |
Appears in Collections: | Research publications |
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s12883-020-01672-w.pdf | Published version | 1.69 MB | Adobe PDF | View/Open |
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