Seizure Onset Zone Identification Using Phase-Amplitude Coupling and Multiple Machine Learning Approaches for Interictal Electrocorticogram

Abstract

AbstractAutomatic seizure onset zone (SOZ) localization using interictal electrocorticogram (ECoG) improves the diagnosis and treatment of patients with medically refractory epilepsy. This study aimed to investigate the characteristics of phase-amplitude coupling (PAC) extracted from interictal ECoG and the feasibility of PAC served as a promising biomarker for SOZ identification. We employed the mean vector length modulation index approach on the 20-s ECoG window to calculate PAC features between low-frequency rhythms (0.5–24 Hz) and high frequency oscillations (HFOs) (80–560 Hz). We used statistical measures to test the significant difference in PAC between SOZ and non-seizure onset zone (NSOZ). To overcome the drawback of handcraft feature engineering, we established novel machine learning models to automatically learn the characteristics of PAC features obtained and classify them to identify SOZ. Besides, to conquer the imbalance of datasets, we introduced novel feature-wise/class-wise re-weighting strategies in conjunction with classifiers. In addition, we proposed the time-series nest cross-validation to provide more accurate and unbiased evaluations for this model. Seven patients with focal cortical dysplasia were included in this study. The experiment results not only illustrate that the significant coupling at band pairs of slow waves and HFOs exists in the SOZ when compared with the NSOZ but also indicate the effectiveness of PAC features and the proposed models with better classification performance.