  Epilepsy is a neurological brain disorder which life threatening and gives
rise to recurrent seizures that are unprovoked. It occurs due to the abnormal
chemical changes in our brain. Over the course of many years, studies have been
conducted to support automatic diagnosis of epileptic seizures for the ease of
clinicians. For that, several studies entail the use of machine learning
methods for the early prediction of epileptic seizures. Mainly, feature
extraction methods have been used to extract the right features from the EEG
data generated by the EEG machine and then various machine learning classifiers
are used for the classification process. This study provides a systematic
literature review of feature selection process as well as the classification
performance. This study was limited to the finding of most used feature
extraction methods and the classifiers used for accurate classification of
normal to epileptic seizures. The existing literature was examined from
well-known repositories such as MPDI, IEEEXplore, Wiley, Elsevier, ACM,
Springerlink and others. Furthermore, a taxonomy was created that recapitulates
the state-of-the-art used solutions for this problem. We also studied the
nature of different benchmark and unbiased datasets and gave a rigorous
analysis of the working of classifiers. Finally, we concluded the research by
presenting the gaps, challenges and opportunities which can further help
researchers in prediction of epileptic seizure
