From Epilepsy Seizures Classification to Detection: A Deep Learning-based Approach for Raw EEG Signals
<|reference_start|>From Epilepsy Seizures Classification to Detection: A Deep Learning-based Approach for Raw EEG Signals: Epilepsy represents the most prevalent neurological disease in the world. One-third of people suffering from mesial temporal lobe epilepsy (MTLE) exhibit drug resistance, urging the need to develop new treatments. A key part in anti-seizure medication (ASM) development is the capability of detecting and quantifying epileptic seizures occurring in electroencephalogram (EEG) signals, which is crucial for treatment efficacy evaluation. In this study, we introduced a seizure detection pipeline based on deep learning models applied to raw EEG signals. This pipeline integrates: a new pre-processing technique which segments continuous raw EEG signals without prior distinction between seizure and seizure-free activities; a post-processing algorithm developed to reassemble EEG segments and allow the identification of seizures start/end; and finally, a new evaluation procedure based on a strict seizure events comparison between predicted and real labels. Models training have been performed using a data splitting strategy which addresses the potential for data leakage. We demonstrated the fundamental differences between a seizure classification and a seizure detection task and showed the differences in performance between the two tasks. Finally, we demonstrated the generalization capabilities across species of our best architecture, combining a Convolutional Neural Network and a Transformer encoder. The model was trained on animal EEGs and tested on human EEGs with a F1-score of 93% on a balanced Bonn dataset.<|reference_end|>
@article{darankoum2024from,
    title={From Epilepsy Seizures Classification to Detection: A Deep
  Learning-based Approach for Raw EEG Signals},
    author={Davy Darankoum, Manon Villalba, Clelia Allioux, Baptiste Caraballo,
  Carine Dumont, Eloise Gronlier, Corinne Roucard, Yann Roche, Chloe
  Habermacher, Sergei Grudinin and Julien Volle},
    journal={arXiv preprint arXiv:2410.03385},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.03385},
    primaryClass={cs.LG q-bio.NC}
}