  Epilepsy affects 50 million people worldwide and is one of the most common
serious neurological disorders. Seizure detection and classification is a
valuable tool for diagnosing and maintaining the condition. An automated
classification algorithm will allow for accurate diagnosis. Utilising the
Temple University Hospital (TUH) Seizure Corpus, six seizure types are
compared; absence, complex partial, myoclonic, simple partial, tonic and tonic-
clonic models. This study proposes a method that utilises unique features with
a novel parallel classifier - Parallel Genetic Naive Bayes (NB) Seizure
Classifier (PGNBSC). The PGNBSC algorithm searches through the features and by
reclassifying the data each time, the algorithm will create a matrix for
optimum search criteria. Ictal states from the EEGs are segmented into 1.8 s
windows, where the epochs are then further decomposed into 13 different
features from the first intrinsic mode function (IMF). The features are
compared using an original NB classifier in the first model. This is improved
upon in a second model by using a genetic algorithm (Binary Grey Wolf
Optimisation, Option 1) with a NB classifier. The third model uses a
combination of the simple partial and complex partial seizures to provide the
highest classification accuracy for each of the six seizures amongst the three
models (20%, 53%, and 85% for first, second, and third model, respectively).
