  Epilepsy which is characterized by seizures is studied using EEG signals by
recording the electrical activity of the brain. Different types of
communication between different parts of the brain are characterized by many
state of the art connectivity measures which can be directed and undirected. We
propose to employ a set of undirected (spectral matrix, the inverse of the
spectral matrix, coherence, partial coherence, and phaselocking value) and
directed features (directed coherence, the partial directed coherence) to learn
a deep neural network that detects whether a particular data window belongs to
a seizure or not, which is a new approach to standard seizure classification.
Taking our data as a sequence of ten sub-windows, we aim at designing an
optimal deep learning model using attention, CNN, BiLstm, and fully connected
layers. We also compute the relevance using the weights of the learned model
based on the activation values of the receptive fields at a particular layer.
Our best model architecture resulted in 97.03% accuracy using balanced MITBIH
data subset. Also, we were able to explain the relevance of each feature across
all patients. We were able to experimentally validate some of the scientific
facts concerning seizures by studying the impact of the contributions of the
activations on the decision.
