  Timely diagnosis is important for saving the life of epileptic patients. In
past few years, a lot of treatments are available for epilepsy. These
treatments require use of anti-seizure drugs but are not effective in
controlling frequency of seizure. There is need of removal of an affected
region using surgery. Electroencephalogram (EEG) is a widely used technique for
monitoring the brain activity and widely popular for seizure region detection.
It is used before surgery for locating affected region. This manual process,
using EEG graphs, is time consuming and requires deep expertise. In the present
paper, a model has been proposed that preserves the true nature of an EEG
signal in form of textual one-dimensional vector. The proposed model achieves a
state of art performance for Bonn University dataset giving an average
sensitivity, specificity of 81% and 81.4% respectively for classification of
EEG data among all five classes. Also for binary classification achieving
99.9%, 99.5% score value for specificity and sensitivity instead of 2D models
used by other researchers. Thus, developed system will significantly help
neurosurgeons in the increase of their performance.
