EEG-Based Detection for Depth of Sedation Using Spectro-Temporal Information in ICU Patients

In the intensive care unit (LCU), critically ill patients commonly receive sedative medications to alleviate pain and facilitate clinical care. These sedatives are administered based on the assessment of sedation levels and clinical experience. A periodic evaluation of behavioral responses to stimuli is commonly used to monitor sedation levels in the ICU. It is, however, difficult to monitor sedation levels in critically ill patients due to various factors, including comas or altered consciousness. As a non-invasive monitoring method, electroencephalography (EEG) has been shown to track patients' consciousness levels effectively. This assists medical personnel in mitigating the adverse effects of inappropriate medication on patients. In this study, we utilize both time-domain and frequency-domain information from EEG of 85 critically ill patients to train a multi-feature fusion model for predicting the Richmond Agitation-Sedation Scale (RASS) scores, which range from -5 = comatose, -4, … to 0 = awake.The proposed model achieves average accuracies of 65 %, and 85% average accuracies with tolerance of one level difference. The results demonstrate that the proposed model can effectively predict the sedation levels of critically ill patients in the ICU. Moreover, we show spectrograms of EEG signals corresponding to different sedation levels, which provide interpretability through the analysis of spectro-temporal information. Future enhancements involve leveraging diverse bedside monitoring data in the ICU to improve the accuracy of patient consciousness level monitoring.