  Epilepsy is a neurological disorder and for its detection, encephalography
(EEG) is a commonly used clinical approach. Manual inspection of EEG brain
signals is a time-consuming and laborious process, which puts heavy burden on
neurologists and affects their performance. Several automatic techniques have
been proposed using traditional approaches to assist neurologists in detecting
binary epilepsy scenarios e.g. seizure vs. non-seizure or normal vs. ictal.
These methods do not perform well when classifying ternary case e.g. ictal vs.
normal vs. inter-ictal; the maximum accuracy for this case by the
state-of-the-art-methods is 97+-1%. To overcome this problem, we propose a
system based on deep learning, which is an ensemble of pyramidal
one-dimensional convolutional neural network (P-1D-CNN) models. In a CNN model,
the bottleneck is the large number of learnable parameters. P-1D-CNN works on
the concept of refinement approach and it results in 60% fewer parameters
compared to traditional CNN models. Further to overcome the limitations of
small amount of data, we proposed augmentation schemes for learning P-1D-CNN
model. In almost all the cases concerning epilepsy detection, the proposed
system gives an accuracy of 99.1+-0.9% on the University of Bonn dataset.
