  Epilepsy is a well-known neuronal disorder that can be identified by
interpretation of the electroencephalogram (EEG) signal. Usually, the length of
an EEG signal is quite long which is challenging to interpret manually. In this
work, we propose an automated epileptic seizure detection method by applying a
two-step minimization technique: first, we reduce the data points using a
statistical sampling technique and then, we minimize the number of features
using our novel feature selection algorithm. We then apply different machine
learning algorithms for performance measurement of the proposed feature
selection algorithm. The experimental results outperform some of the
state-of-the-art methods for seizure detection using the reduced data points
and the least number of features.
