This repository contains the code of the Biomed Irregulars submission to the Neureka Challenge 2020. The Biomed irregulars team consists of PhD students from the the ESAT-STADIUS research group at KU Leuven: C. Chatzichristos, J. Dan, A.M. Narayanan, N. Seeuws, K. Vandecasteele.

The seizure detection algorithm is based on the fusion of multiple attention U-nets, each operating on a distinct view of the EEG data. The outputs of the different U-nets are fused by an LSTM network. More information about the methods and results can be found in the preliminary version of the paper neureka_ieee_spmb.pdf.

1. library/ - This folder contains the general functions used accross modules: data loading, re-referencing, resampling and filtering.
2. training/ - Contains the code to train the Wiener filters, U-nets and LSTM models.
3. evaluate/ - Contains the code to run the seizure detection pipeline on unlabelled data.

The codebase uses a mix of Python 3 and Matlab.

The dataset used is the TUH EEG Seizure dataset.

Matlab requires the EEGlab toolbox.

Python requires the libraries listed in python_requirements.txt.

While the intent of the code is to allow deceminatation and re-use of our pipeline and model architecture. We realize the code is not click & run and documentation is sometimes lacking. We do invite you to contact us through email or as a github issue to improve quality and understanding of the code.

The code is release under the GNU GPLv3 license.