Multi-domain feature fusion with variational mode decomposition and hybrid LightGBM-Logistic Regression for multi-class seizure classification.

Abstract

Objective: The problem of seizure classification using multi-seizure types based on reduced-channel EEG recordings is difficult due to the complexity and nonlinearity of seizure patterns and their high class imbalance.

Methods: In this paper, a reduced-channel EEG-based method is proposed to perform automated multi-seizure classification using the Temple University Seizure Corpus (TUSZ) data set. The proposed approach uses the T3 and T4 EEG temporal channels and performs Variational Mode Decomposition (VMD) to decompose EEG signals into several intrinsic mode functions to extract the relevant temporal and spectral features of seizures. Next, multi-domain features, including statistical, spectral, entropy, and nonlinear features, are extracted from each VMD mode. To avoid the redundancy of the extracted features and preserve seizure-related features, an MI-RFE feature selection scheme is used. The extracted features were then classified using a stacked ensemble model incorporating LightGBM and logistic regression. For the purpose of ensuring unbiased testing and avoiding any information leakage, the Fold-Wise SMOTE technique was used in the process of stratified K-fold cross-validation.

Results: In experimental analysis of the developed technique with nine types of seizures, positive outcomes were obtained in terms of classification accuracy that included high classification accuracy, high weighted F1 measure, and multiclass ROC-AUC characteristics, resulting in classification accuracy of 92.16%. Also, when comparing the developed feature selection approach with standard techniques, the approach's efficiency was demonstrated.

Significance: The attained results show that the suggested approach for reducing the number of channels used in analyzing brain dynamics is effective.

Clinical trial number: Not applicable.