Detection And Classification Of Adult Epilepsy Using Hybrid Deep Learning Approach

This article discusses the use of digital EEG analysis to detect seizures and other brain conditions from EEG data. It explains the three main steps of pre-processing, feature extraction, and classification that are used to identify patterns related to seizures. It also introduces the 3D deep convolution auto-encoder (3D-DCAE) which is used to detect epileptic seizures from EEG recordings. The auto-encoder is trained in a supervised way to learn the best features from the EEG signals and summarize them into a simple, low-dimensional representation.