Seizure Type Classification Using EEG Signals and Machine Learning: Setting a Benchmark

Abstract

Accurate classification of seizure types plays a crucial role in the treatment and disease management of epileptic patients. Epileptic seizure types not only impact the choice of drugs but also the range of activities a patient can safely engage in. With recent advances being made towards artificial intelligence enabled automatic seizure detection, the next frontier is the automatic classification of seizure types. On that note, in this paper, we explore the application of machine learning algorithms for multiclass seizure type classification. We used the recently released TUH EEG seizure corpus (v1.4.0 and v1.5.2) and conducted a thorough search space exploration to evaluate the performance of a combination of various preprocessing techniques, machine learning algorithms, and corresponding hyperparameters on this task. We show that our algorithms can reach a weighted F1 score of up to 0.901 for seizure-wise cross validation and 0.561 for patient-wise cross validation thereby setting a benchmark for scalp EEG based multi-class seizure type classification.