A self-attention model for cross-subject seizure detection

• Published in: Computers in biology and medicine Vol. 165; p. 107427
• Main Authors: Abdallah, Tala, Jrad, Nisrine, Abdallah, Fahed, Humeau-Heurtier, Anne, Van Bogaert, Patrick
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.10.2023; Elsevier Limited; Elsevier
• Subjects: Artificial neural networks; Coders; Convolutional neural network (CNN); Convulsions & seizures; Deep learning; Deep learning (DL); EEG; Electroencephalography; Electroencephalography (EEG); Engineering Sciences; Epilepsy; Epileptic seizure recognition; Internal Medicine; Long short-term memory; Long short-term memory (LSTM); Machine learning; Neural networks; Neurological diseases; Other; Seizures; Self-attention (SAT); Signal and Image processing; Signal processing; Self-attention (SAT); Electroencephalography (EEG); Convolutional neural network (CNN); Epileptic seizure recognition; Deep learning (DL); Long short-term memory (LSTM)

Epilepsy is a neurological disorder characterized by recurring seizures, detected by electroencephalography (EEG). EEG signals can be detected by manual time-consuming analysis and recently by automatic detection. The latter poses a significant challenge due to the high dimensional and non-stationar...