# 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:
[TODO]().

# Usage

## Structure
The dataset itself is stored in [mep_dataset.zip](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](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](sample_use_case.ipynb) 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:
```bash
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.