Epileptic EEG classification via deep learning-based strange attractor

Electroencephalography is commonly exploited in recording brains’ electrical activities for revealing the symptoms of various neurological diseases , e.g., epileptic seizures . We hypothesize that its capability primarily comes from the information it captures from the brain, which is supposed to be a chaotic dynamical system . Therefore, seizure detection in clinical practice could benefit from strange attractor reconstruction from time series in classifying non-ictal and ictal epileptic seizures . We propose a deep learning architecture and the loss function in a latent space to implement the reconstruction. By leveraging the proposed deep model, the hidden coordinates can be derived from a low-dimensional time series or univariate time series (e.g., Electroencephalography). Furthermore, added variables can extract more features from the reconstructed dynamical system . The proposed pipeline is evaluated by using a publicly available epileptic seizure database. Experimental results demonstrated the promising outcome of the proposed approach.