RSBagging: An ensemble classifier detecting the after-effects of ischemic stroke through EEG connectivity and microstates.

Abstract

Stroke can lead to significant after-effects, including motor function impairments, language impairments (aphasia), disorders of consciousness (DoC), and cognitive deficits. Computer-aided analysis of EEG connectivity matrices and microstates from bedside EEG monitoring can replace traditional clinical observation methods, offering an automatic approach to monitoring the progression of these after-effects. This EEG-based method also enables quicker and more efficient assessments for medical practitioners. In this study, we employed Functional Connectivity features that extract spatial representation and Microstate features that focus on the time domain representation to monitor the after-effects of ischemic stroke patients. As the dataset from stroke patients is heavily imbalanced across various clinical after-effects conditions, we designed an ensemble classifier, RSBagging, to address the issue of classifiers often favoring the majority classes in the classification of imbalanced datasets. The experimental results demonstrate that different connectivity matrices are effective for three classification tasks: consciousness level, motor disturbance, and stroke location. Using our RSBagging model, all three tasks achieve over 98% accuracy, sensitivity, specificity, and F1-score, significantly outperforming the existing classifiers SVM, XGBoost, and Random Forest. Therefore, the RSBagging classifier based on connectivity matrices offers an effective method for monitoring the after-effects in stroke patients.