Rebalancing Techniques for Asynchronously Distributed EEG Data to Improve Automatic Seizure Type Classification

Epilepsy, a nervous system disorder, is characterised by unprovoked, unpredictable, and recurrent seizures. To diagnose epileptic seizures, electroencephalography (EEG) is frequently used in medical settings. Effective automated detection and classification strategies are needed because visual analysis and interpretation of EEG signals consume time and call for specialised expertise. The main objective of this paper is to examine the effectiveness of multiple rebalancing techniques to address the problem of asynchronously distributed data, specifically employing random resampling, synthetic minority oversampling technique (SMOTE), and adaptive synthetic sampling approach for imbalanced learning (ADASYN), for seizure type classification. The model utilises both frequency information using variational mode decomposition (VMD), and phase information by extracting the phase locking value (PLV) across 19 common EEG channels found in the Temple University Hospital EEG Seizure Corpus (TUSZ) v1.5.2 dataset. The random subspace k-nearest neighbour (RSkNN) ensemble classifier is used for seizure type classification of five classes - complex partial seizures (CPSZ), simple partial seizures (SPSZ), absence seizures (ABSZ), tonic clonic seizures (TCSZ), and tonic seizures (TNSZ) - to determine the performance of each rebalancing techniques, with the highest accuracy and weighted F 1 score of 96.28% and 0.964, respectively using SMOTE with two nearest neighbours.