Motor evoked potentials for multiple sclerosis: A multiyear follow-up dataset.
Introduction
This dataset contains Motor Evoked Potential measurements, performed on Multiple Sclerosis patients. The dataset descriptor can be found at:
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Usage
Structure
The dataset itself is stored in mep_dataset.zip. The general structures is as follows:
- patient.csv: Contains the records for the various patients.
- visit.csv: Contains the records for the various visits.
- test.csv: Contains the records for the various tests.
- measurement.csv: Contains the records for the various measurements.
- edss.csv: Contains the records for the various edss measurements.
Besides these files the dataset also contains textfiles for each of the actual time series. The filenames of these files contain a unique identifier which can be used to link back to the column "timeseries" in the measurement.csv file. Some code to automate this linking (in Python) is included in create_df_from_portable_dataset.py.
More details about specifics fields can be found in the dataset descriptor.
Getting started
It is highly recommended to have a look at the included jupyter notebook to familiarize oneself with the dataset.
It includes a sample use case and goes over how to work with the dataset.
To run the jupyter notebook a few Python packages are required:
- Pandas
- Numpy
- Matplotlib
- Scipy
- Scikit-learn
- Tqdm
- Jupyter
For example in anaconda this could be achieved using:
conda create --name mep python=3 pandas numpy matplotlib scipy scikit-learn tqdm jupyter
which creates an environment called "mep" that contains the required packages.
