From Epilepsy Seizures Classification to Detection: A Deep Learning-based Approach for Raw EEG Signals
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arXiv
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| Auteurs principaux: | , , , , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866910055435075584 |
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| author | Darankoum, Davy Villalba, Manon Allioux, Clelia Caraballo, Baptiste Dumont, Carine Gronlier, Eloise Roucard, Corinne Roche, Yann Habermacher, Chloe Grudinin, Sergei Volle, Julien |
| author_facet | Darankoum, Davy Villalba, Manon Allioux, Clelia Caraballo, Baptiste Dumont, Carine Gronlier, Eloise Roucard, Corinne Roche, Yann Habermacher, Chloe Grudinin, Sergei Volle, Julien |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_03385 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | From Epilepsy Seizures Classification to Detection: A Deep Learning-based Approach for Raw EEG Signals Darankoum, Davy Villalba, Manon Allioux, Clelia Caraballo, Baptiste Dumont, Carine Gronlier, Eloise Roucard, Corinne Roche, Yann Habermacher, Chloe Grudinin, Sergei Volle, Julien Machine Learning Neurons and Cognition 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. |
| title | From Epilepsy Seizures Classification to Detection: A Deep Learning-based Approach for Raw EEG Signals |
| topic | Machine Learning Neurons and Cognition |
| url | https://arxiv.org/abs/2410.03385 |