motor_evoked_potentials/README.md

Motor evoked potentials for multiple sclerosis: A multiyear follow-up dataset.

Introduction

This dataset contains Motor Evoked Potential measurements, performed on Multiple Sclerosis patients.

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.