Detecting the depth of sedation in the intensive care unit using a 2-channel electroencephalogram: An analysis with 2 machine learning models

Existing methods to detect depth of sedation do not fully adjust to the characteristics of the ICU population. The aim of this study is to evaluate the performance of a two-channel EEG in predicting the depth of sedation in ICU patients. The electroencephalographic signal of 21 patients admitted to the ICU were analyzed, and EEG features were calculated. These served as inputs in 2 machine learning models: Random Forest Classifier (RFC) and Support Vector Machine (SVM). The depth of sedation was assessed using the Richmond Agitation-Sedation Scale (RASS). Patients with RASS scores of −4/−5 were classified as “Deeply Sedated”, otherwise they were classified as “Not Deeply Sedated”. In the general models, all EEG features were used, after which sequential feature selection was conducted to improve performance and reduce the number of variables (reduced models). The general models showed a moderate ability to discriminate between sedation categories (RFC: average F1-score=0.60, SVM: average F1-score=0.59). This ability was improved in the reduced models (RFC: average F1-score=0.65, SVM: average F1-score=0.72). It was observed that decreasing the number of features in the reduced SVM model from 6 to 3 features could achieve a similar performance while simplifying the model (SVM: average F1-score=0.72). An exploratory analysis showed that the individual feature with the best performance was Beta Power–EEG2. Overall, the 2-channel EEG has a moderate power to discriminate between different states of sedation and may not be useful in this purpose if used as a single predictor.