We provide certain tools to encourage reproducibility and consistency of results reported in the field of automated seizure detection algorithm

Library for measuring performance of seizure detection algorithms

We built a library that provides different scoring methodologies to compare a reference time series with binary annotation (ground-truth annotations of the neurologist) to hypothesis binary annotations (provided by a machine learning pipeline). These different scoring methodologies provide a count of correctly identified events (True Positives) as well as missed events (False Negatives) and wrongly marked events (False positions)

In more details, we measures performance on the level of:

• Samples : Performance metric that threats every label sample independently.
  
• Events (e.g. epileptic seizure) : Classifies each event in both reference and hypothesis based on overlap of both.

Both methods are illustrated in the following figures :

Seizure validation Framework

This library provides script to work with the framework for the validation of EEG based automated seizure detection algorithms.

The library provides code to :

1. Convert EDF files from most open scalp EEG datasets of people with epilepsy to a standardized format
2. Convert seizure annotations from these datasets to a standardized format.
3. Evaluate the performance of seizure detection algorithm.