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Accurate classification of seizure types plays a crucial role in thetreatment and disease management of epileptic patients. Epileptic seizure typesnot only impact the choice of drugs but also the range of activities a patientcan safely engage in. With recent advances being made towards artificialintelligence enabled automatic seizure detection, the next frontier is theautomatic classification of seizure types. On that note, in this paper, weexplore the application of machine learning algorithms for multi-class seizuretype classification. We used the recently released TUH EEG seizure corpus(V1.4.0 and V1.5.2) and conducted a thorough search space exploration toevaluate the performance of a combination of various pre-processing techniques,machine learning algorithms, and corresponding hyperparameters on this task. Weshow that our algorithms can reach a weighted $F1$ score of up to 0.901 forseizure-wise cross validation and 0.561 for patient-wise cross validationthereby setting a benchmark for scalp EEG based multi-class seizure typeclassification.

Seizure Type Classification using EEG signals and Machine Learning: Setting a benchmark