Abstract
Anesthesia plays a pivotal role in modern surgery by facilitating controlled states of unconsciousness. Precise control is crucial for safe and pain-free surgeries. Monitoring anesthesia depth accurately is essential to guide anesthesiologists, optimize drug usage, and mitigate postoperative complications. This study focuses on enhancing the classification performance of anesthesia-induced transitions between wakefulness and deep sleep into eight classes by leveraging advanced graph neural network (GNN). The research combines seven datasets into a single dataset comprising 290 samples and investigates key brain regions, to develop a robust classification framework. Initially, the dataset is augmented using the Synthetic Minority Over-sampling Technique (SMOTE) to expand the sample size to 1197. A graph-based approach is employed to get the intricate relationships between features, constructing a graph dataset with 1197 nodes and 714,610 edges, where nodes represent data samples and edges are the connections between the nodes. The connection (edge weight) is calculated using Spearman correlation coefficient matrix. An optimized GNN model is developed through an ablation study of eight hyperparameters, achieving an accuracy of 92.8%. The model's performance is further evaluated against one-dimensional (1D) CNN, and six machine learning models, demonstrating superior classification capabilities for small and imbalanced datasets. Additionally, we evaluated the proposed model on six different anesthesia datasets, observing no decline in performance. This work advances the understanding and classification of anesthesia states, providing a valuable tool for improved anesthesia management.