Detecting Seizures from EEG Data: The Potential of Deep Learning Techniques

Seizure identification in EEG data is difficult because brain activity is complicated and constantly changing. Conventional methods often fall short when recording complex patterns during seizures. On the other hand, deep learning methods have shown impressive results in learning and feature extraction from complex data. This study uses CNNs, RNNs, and a hybrid model to identify seizures. By adding convolutional filters to the EEG data, CNNs can effectively capture spatial characteristics, bringing to light patterns and structures at varying scales. In contrast, recurrent neural networks (RNNs) make excellent use of their recurrent connections to simulate temporal relationships, allowing them to capture sequential information in EEG data effectively. Various performance metrics were used to evaluate deep learning models. The outcomes prove the viability of the suggested deep learning approach, which outperforms conventional machine learning techniques. The model's success in separating seizure-related data from background noise in the EEG data reveals an accuracy of 94%.