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Epilepsy is a neurological disorder and for its detection, encephalography(EEG) is a commonly used clinical approach. Manual inspection of EEG brainsignals is a time-consuming and laborious process, which puts heavy burden onneurologists and affects their performance. Several automatic techniques havebeen proposed using traditional approaches to assist neurologists in detectingbinary 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 thestate-of-the-art-methods is 97+-1%. To overcome this problem, we propose asystem based on deep learning, which is an ensemble of pyramidalone-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 onthe concept of refinement approach and it results in 60% fewer parameterscompared to traditional CNN models. Further to overcome the limitations ofsmall amount of data, we proposed augmentation schemes for learning P-1D-CNNmodel. In almost all the cases concerning epilepsy detection, the proposedsystem gives an accuracy of 99.1+-0.9% on the University of Bonn dataset.

An Automated System for Epilepsy Detection using EEG Brain Signals based on Deep Learning Approach