Frontal electroencephalogram based drug, sex, and age independent sedation level prediction using non-linear machine learning algorithms.

Brain monitors which track quantitative electroencephalogram (EEG) signatures to monitor sedation levels are drug and patient specific. There is a need for robust sedation level monitoring systems to accurately track sedation levels across all drug classes, sex and age groups. Forty-four quantitative features estimated from a pooled dataset of 204 EEG recordings from 66 healthy adult volunteers who received either propofol, dexmedetomidine, or sevoflurane (all with and without remifentanil) were used in a machine learning based automated system to estimate the depth of sedation. Model training and evaluation were performed using leave-one-out cross validation methodology. We trained four machine learning models to predict sedation levels and evaluated the influence of remifentanil, age, and sex on the prediction performance. The area under the receiver-operator characteristic curve (AUC) was used to assess the performance of the prediction model. The ensemble tree with bagging outperformed other machine learning models and predicted sedation levels with an AUC = 0.88 (0.81-0.90). There were significant differences in the prediction probability of the automated systems when trained and tested across different age groups and sex. The performance of the EEG based sedation level prediction system is drug, sex, and age specific. Nonlinear machine-learning models using quantitative EEG features can accurately predict sedation levels. The results obtained in this study may provide a useful reference for developing next generation EEG based sedation level prediction systems using advanced machine learning algorithms. Clinical trial registration: NCT02043938 and NCT03143972.

Test of neural inertia in humans during general anaesthesia.

BACKGROUND: Neural inertia is defined as the tendency of the central nervous system to resist transitions between arousal states. This phenomenon has been observed in mice and Drosophila anaesthetized with volatile anaesthetics: the effect-site concentration required to induce anaesthesia in 50% of the population (C50) was significantly higher than the effect-site concentration for 50% of the population to recover from anaesthesia. We evaluated this phenomenon in humans using propofol or sevoflurane (both with or without remifentanil) as anaesthetic agents. METHODS: Thirty-six healthy volunteers received four sessions of anaesthesia with different drug combinations in a step-up/step-down design. Propofol or sevoflurane was administered with or without remifentanil. Serum concentrations of propofol and remifentanil were measured from arterial blood samples. Loss and return of responsiveness (LOR-ROR), response to pain (PAIN), Patient State Index (PSI) and spectral edge frequency (SEF) were modeled with NONMEM®. RESULTS: For propofol, the C50 for induction and recovery of anaesthesia was not significantly different across the different endpoints. For sevoflurane, for all endpoints except SEF, significant differences were found. For some endpoints (LOR and PAIN) the difference was significant only when sevoflurane was combined with remifentanil. CONCLUSIONS: Our results nuance earlier findings with volatile anaesthetics in mice and Drosophila. Methodological aspects of the study, such as the measured endpoint, influence the detection of neural inertia. A more thorough definition of neural inertia, with a robust methodological framework for clinical studies is required to advance our knowledge of this phenomenon. CLINICAL TRIAL REGISTRATION: NCT 02043938.