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Motor evoked potentials for multiple sclerosis, a multiyear follow-up dataset.

Jan Yperman1,2,3,4, Veronica Popescu1,3,5, Bart Van Wijmeersch1,3,5

  • 1Biomedical Research Institute (BIOMED), Hasselt University, 3500, Hasselt, Belgium.

Scientific Data
|May 16, 2022
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Summary

This study introduces a large dataset of motor evoked potential (MEP) measurements for multiple sclerosis (MS) patients. The data aids in understanding MS progression and developing predictive models for clinical care.

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Area of Science:

  • Neuroscience
  • Clinical Neurology
  • Biomedical Data Science

Background:

  • Multiple sclerosis (MS) is a chronic neurological disorder impacting central nervous system signal conduction.
  • Demyelination and axonal damage in MS disrupt neural pathways, necessitating effective monitoring tools.
  • Evoked potential measurements offer a method for assessing neurological function and supporting clinical decisions.

Purpose of the Study:

  • To present a comprehensive dataset of motor evoked potential (MEP) measurements from multiple sclerosis patients.
  • To facilitate research into the role of evoked potentials in MS patient care and disease monitoring.
  • To provide a benchmark dataset for time series analysis and predictive modeling in a clinical context.

Main Methods:

  • Collected a dataset of 5586 patient visits (963 individuals) over 6 years, capturing approximately 100,000 motor evoked potential (MEP) measurements.
  • MEP data involved stimulating the brain and recording signals from hands and feet, resulting in 100-millisecond time series.
  • Integrated clinical metadata, including expanded disability status scale (EDSS), sex, and age, with the MEP time series data.

Main Results:

  • The dataset comprises extensive longitudinal MEP data from a large cohort of MS patients.
  • Includes detailed clinical metadata crucial for correlating physiological measurements with disease status.
  • The dataset is structured for advanced time series analysis and the development of predictive algorithms.

Conclusions:

  • The released MEP dataset offers a valuable resource for advancing multiple sclerosis research.
  • It supports the exploration of evoked potentials for improved patient monitoring and decision support in MS care.
  • The dataset serves as a robust benchmark for developing and validating novel time series analysis techniques.