Generalized tonic-clonic seizures detection using deep learning techniques
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Data
2025-03-31
Autores
Mesa, Juan Sebastian Campos
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Universidade Federal do Espírito Santo
Resumo
Generalized Tonic-Clonic Seizure (GTCS) poses serious health risks, including an increased likelihood of sudden unexpected death in epilepsy (SUDEP), postictal pulmonary edema (PPE), and traumatic injuries from falls or jerking movements. However, GTCS detection remains challenging due to the complex and variable nature of EEG signals. Traditional methods struggle with these variations, while deep learning remains a gold standard for seizure detection, making it a powerful tool for GTCS detection. This study explores the detection of GTCS using EEG data from the Temple University Seizure (TUSZ) dataset. To achieve this, three deep learning architectures were employed: Diffusion Convolutional Recurrent Neural Network (DCRNN), Long Short-Term Memory (LSTM), and Convolutional Densely Connected Gated Recurrent Neural Network (C- DRNN). The research evaluated the impact of loss functions and data augmentation on model performance. Dice Entropy loss (DE) proved to be the most effective for DCRNN, while Cross-Entropy loss (CE) optimally enhanced LSTM and C-DRNN. Data augmentation played a crucial role in improving generalization and robustness across all models, improving their performance. As a consequence, DCRNN achieved the best performance, with AUC-ROC of 0.781, F1-score of 67.74%, and Sensitivity of 82.84%. The results obtained were close to or better than those found in the literature. This study highlights the potential for further research into alternative data augmentation techniques, deep learning architectures, and advanced methods to detect GTCS in EEG signals.
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EEG , Detecção de GTCS , Aprendizagem profunda , LSTM , C-DRNN