We provide certain tools to encourage reproducibility and consistency of results reported in the field of automated seizure detection algorithm.
timescoring#
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 measure performance on the level of:
- Samples : Performance metric that threats every label sample independently.
- Events (e.g. cough) : Classifies each event in both reference and hypothesis based on overlap of both.
Both methods are illustrated in the following figures :
Cough signal manipulation tools#
To accompany the cough counting dataset, we developed the following Git repository: https://github.com/esl-epfl/edge-ai-cough-count/
The repository contains code for:
- Easily iterating through the file structures of the dataset
- Data segmentation
- Data augmentation
- Applying the event-based framework to test model predictions
Cough-E#
The Cough-E model is an open-source example of a cough detection model that was trained and evaluated using our cough counting dataset and evaluation framework. The model training code and embedded C implementation can be found here: https://github.com/esl-epfl/Cough-E