Identifying Features of Electroencephalography Associated with Improved Awareness in Persistent Vegetative State via Multiscale Entropy: A Machine Learning Modeling Study.

Abstract

Accurate differentiation between persistent vegetative state (PVS) and minimally conscious state and estimation of recovery likelihood in patients in PVS are crucial. This study analyzed electroencephalography (EEG) metrics to investigate their relationship with consciousness improvements in patients in PVS and developed a machine learning prediction model. We retrospectively evaluated 19 patients in PVS, categorizing them into two groups: those with improved consciousness (n = 7) and those without improvement (n = 12). Spectral and complexity analyses were performed on patients' EEG data to obtain spectral power and multiscale entropy (MSE) values. These metrics served as features in developing an EEG-based prediction model for consciousness improvement. Spectral power and MSE values were used as features in six machine learning models-support vector machine (SVM), Classification and Regression Tree, chi-squared automatic interaction detector, neural network, C5.0, and logistic regression-to perform classification via data mining methods. The dataset, containing data of 19 cases, was divided into training and test sets at a 50% ratio. The SVM model using MSE features yielded the best classification results, with prediction accuracies of 95.18% (training set) and 92.93% (test set). The logistic regression model achieved 93.25% and 84.51% accuracy, respectively. In the test set, the MSE-based SVM model demonstrated a 27.67% improvement in classification accuracy compared with models using spectral analysis features, indicating that MSE achieves better classification performance. This study demonstrates that MSE is a promising predictor of prognosis in patients in EEG-confirmed vegetative states.