Machine learning analysis of motor evoked potential time series to predict disability progression in multiple sclerosis.

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 di

SUBMITTER: Yperman J 

PROVIDER: S-EPMC7085864 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature