Objective Assessment of Disorders of Consciousness Based on EEG Temporal and Spectral Features

Most existing studies analyzed the resting-state electroencephalogram (EEG) of DOC patients, and recent research demonstrated that the passive auditory paradigm was helpful for bedside detection of DOC and better captured sensory and cognitive responses. However, further studies of classification algorithms were needed for consciousness assessment in DOC based on task-state EEG data. In this study, EEG data from minimally conscious state (MCS) patients, vegetative state (VS) patients, and a healthy control group (HC) were collected using an auditory oddball paradigm. First, compared to the fragmented features adopted by most studies, multiple effective biomarkers for consciousness assessment in the time-frequency domains, connectivity and nonlinear dynamics were identified. Event-related potentials (ERP) results showed that MCS and VS patients exhibited lower N100 and MMN amplitudes than the HC group. Spectral analysis results indicated that VS patients had higher Delta power, and lower Alpha and Beta power than the MCS and HC groups. Second, different from insufficient classifiers in previous studies, this study systematically compared the performance of multiple machine learning and deep learning (DL) classifiers, including support vector machine (SVM), linear discriminant analysis (LDA), random forest (RF), eXtreme Gradient Boosting (XGBoost), decision tree (DT), EEGNet and ShallowConvNet. For machine learning methods, SVM and RF had an advantage in binary classification, and SVM had better performance in three-class classification. Among all individual classifiers, Shallow ConvNet had the best performance for binary and three-class classification. Moreover, an ensemble model incorporating all seven classifiers was proposed using a voting strategy, and further improved classification performance that was superior to existing studies. In addition, the importance of each feature was analyzed, identifying N100, MMN, Delta, Alpha, and Beta power as significant biomarkers of consciousness assessment.