AnesFormer: An End-to-End Framework for EEG-Based Anesthetic State Classification

To determine the real-time changes in brain arousal introduced by anesthetics, Electroencephalogram (EEG) is often used as an objective neuroimaging evidence to link the neurobehavioral states of patients. However, EEG signals often suffer from a low signal-to-noise ratio due to environmental noise and artifacts, which limits its application for a reliable estimation of depth of anesthesia (DoA), especially under high cross-subject variability. In this study, we propose an end-to-end deep learning based framework, termed as AnesFormer, which contains a data selection model, a self-attention based classification model, and a baseline update mechanism. These three components are integrated in a dynamic and seamless manner to achieve the goal of improving the effectiveness and robustness of DoA estimation in a leave-one-out setting. In the experiment, we apply the proposed framework to an office-based dataset and a hospital-based dataset, and use seven existing models as benchmarks. In addition, we conduct an ablation experiment to show the significance of each component in AnesFormer. Our main results indicate that 1) the proposed framework generally performs better than the existing methods for DoA estimation in terms of effectiveness and robustness; 2) each designed component in AnesFormer is likely to contribute to the DoA classification improvement.