Deep Learning Approaches for EEG Signal Analysis in Epilepsy Detection
Keywords:
Epilepsy, EEG signal analysis, Deep learning, Convolutional Neural Networks, Recurrent Neural Networks, Seizure detection, Seizure prediction, Transfer learning, Ensemble methods, Generative Adversarial Networks.Abstract
Epilepsy, a neurological disorder characterized by recurrent seizures, poses significant challenges in diagnosis and management. Electroencephalogram (EEG) signals play a pivotal role in understanding epileptic activities, offering valuable insights for detection and monitoring. In recent years, deep learning techniques have emerged as powerful tools for EEG signal analysis, revolutionizing the field of epilepsy detection. This paper provides a comprehensive review of deep learning approaches for EEG signal analysis in epilepsy detection. We discuss various deep learning architectures, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), hybrid architectures, attention mechanisms, transfer learning, Generative Adversarial Networks (GANs), and ensemble methods. We explore how these techniques are utilized for tasks such as seizure detection, seizure prediction, classification of interictal and ictal states, and localization of epileptic regions. Furthermore, we discuss challenges and future directions in leveraging deep learning for EEG-based epilepsy detection, including data scarcity, model interpretability, and clinical deployment. Deep learning approaches offer promising avenues for enhancing the accuracy and efficiency of epilepsy diagnosis and management, paving the way for personalized treatment strategies and improved patient outcomes.
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