Classification of seizure types based on multi-class specific bands common spatial pattern and penalized ensemble model.

Seizures are induced by sudden abnormal discharge of brain electrical activity. Accurate judgment of seizure type is a prerequisite for correct treatment, rational use of drugs and prognosis. In this paper, a novel feature extraction algorithm based on multi-class specific bands common spatial pattern (MSBCSP), which applies the original CSP algorithm to multi-classification tasks with the help of joint approximation diagonalization (JAD), is presented. Complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is applied to extract the energy of intrinsic mode functions (IMFs). Next, this study employs linear discriminant analysis (LDA) and random forest (RF) for feature selection. A penalized ensemble method was established to address imbalanced data and improve model generalization. Specifically, this study first introduces penalized strategy to handle imbalanced data, which punishes the majority classes by giving them lower weights while giving minority classes higher weights. Furthermore, this study introduces weighted voting strategy to combine logistic regression (LR) algorithm with LightGBM (LGB) algorithm. For evaluating the performance of proposed model, Temple University Hospital EEG dataset v1.5.0 is adopted, accuracy, kappa score, precision, recall and f1-score are computed with the value of 96.14%, 0.9335, 97.24%, 96.36% and 0.9679, respectively. [ABSTRACT FROM AUTHOR]