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- # Metadata for DOI registration according to DataCite Metadata Schema 4.1.
- # For detailed schema description see https://doi.org/10.5438/0014
- ## Required fields
- # The main researchers involved. Include digital identifier (e.g., ORCID)
- # if possible, including the prefix to indicate its type.
- authors:
- -
- firstname: "Jan"
- lastname: "Yperman"
- affiliation: "Hasselt University, Belgium"
- id: "0000-0002-7632-2001"
- -
- firstname: "Veronica"
- lastname: "Popescu"
- affiliation: "Hasselt University, Belgium"
- -
- firstname: "Bart"
- lastname: "Van Wijmeersch"
- affiliation: "Hasselt University, Belgium"
- -
- firstname: "Thijs"
- lastname: "Becker"
- affiliation: "Hasselt University, Belgium"
- -
- firstname: "Liesbet"
- lastname: "Peeters"
- affiliation: "Hasselt University, Belgium"
- # A title to describe the published resource.
- title: "Motor evoked potentials for multiple sclerosis: A multiyear follow-up dataset."
- # Additional information about the resource, e.g., a brief abstract.
- description: |
- Multiple sclerosis (MS) is a chronic disease affecting millions of people worldwide. The signal conduction through the central
- nervous system of MS patients deteriorates. Evoked potential measurements allow clinicians to monitor the degree of
- deterioration and are used for decision support. We share a dataset that contains motor evoked potential (MEP) measurements,
- in which the brain is stimulated and the resulting signal is measured in the hands and feet. This results in time series of 100
- milliseconds long. Typically, both hands and feet are measured in one hospital visit. The dataset consists of 5586 visits of
- 963 patients, performed in day-to-day clinical care over a period of 6 years. The dataset consists of approximately 100,000
- MEP. Clinical metadata such as the expanded disability status scale, sex, and age is also available. This dataset can be used
- to explore the role of evoked potentials in MS research and patient care. It may also be used as a real-world benchmark for
- machine learning techniques for time series analysis and predictive modelling.
- # Lit of keywords the resource should be associated with.
- # Give as many keywords as possible, to make the resource findable.
- keywords:
- - Multiple Sclerosis
- - Prognosis
- - Time series
- # License information for this resource. Please provide the license name and/or a link to the license.
- # Please add also a corresponding LICENSE file to the repository.
- license:
- name: "CC-BY"
- url: "https://creativecommons.org/licenses/by/4.0/"
- ## Optional Fields
- # Funding information for this resource.
- # Separate funder name and grant number by comma.
- funding:
- # - "DFG, AB1234/5-6"
- # - "EU, EU.12345"
- # Related publications. reftype might be: IsSupplementTo, IsDescribedBy, IsReferencedBy.
- # Please provide digital identifier (e.g., DOI) if possible.
- # Add a prefix to the ID, separated by a colon, to indicate the source.
- # Supported sources are: DOI, arXiv, PMID
- # In the citation field, please provide the full reference, including title, authors, journal etc.
- references:
- -
- id: "doi:10.1186/s12883-020-01672-w"
- reftype: "IsSupplementTo"
- citation: "Yperman, J., Becker, T., Valkenborg, D., Popescu, V., Hellings, N., Wijmeersch, B. V., & Peeters, L. M. (2020). Machine learning analysis of motor evoked potential time series to predict disability progression in multiple sclerosis. BMC Neurology, 20(1)."
- -
- id: "doi:10.3389/fninf.2020.00028"
- reftype: "IsSupplementTo"
- citation: "Yperman, J., Becker, T., Valkenborg, D., Hellings, N., Cambron, M., Dive, D., Laureys, G., Popescu, V., Van Wijmeersch, B., & Peeters, L. M. (2020). Deciphering the Morphology of Motor Evoked Potentials. Frontiers in Neuroinformatics, 14."
- # Resource type. Default is Dataset, other possible values are Software, DataPaper, Image, Text.
- resourcetype: Dataset
- # Do not edit or remove the following line
- templateversion: 1.2
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