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

Abstract

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: NCT 02043938 and NCT 03143972.

Do all sedatives promote biological sleep electroencephalogram patterns? A machine learning framework to identify biological sleep promoting sedatives using electroencephalogram

“…Detailed description of the subject recruitment and data collection methods for propofol, sevoflurane and dexmedetomidine have been described in our previous publications [ 15 , 16 , 18 ]. In short, using a Fresenius Base Primea docking station (Fresenius-Kabi, Bad Homburg, Germany) controlled by RUGLOOPII software (Demed, Temse, Belgium) to steer target-controlled infusion (TCI), propofol was administered through an intravenous line.…”

“…In this study, we utilized a set of 44 features that are commonly used in EEG based outcome prediction applications including automatic sleep-staging [ 34 ] and our previous work on a sedation level monitoring system [ 16 , 17 , 35 ]. Given their relevance and effectiveness in capturing dynamic changes of EEG signals related to sedation levels in time, frequency and entropy domains, we chose to incorporate the same set of features in this study.…”

Machine Learning-Guided Anesthesiology: A Review of Recent Advances and Clinical Applications

“…In studies based on EEG signals, researchers have developed monitoring systems using EEG-based criteria to evaluate the depth of anesthesia more accurately (3,(74)(75)(76)(77)(78)(79)(80)(81)(82)(83)(84)(85)(86)(87)(88). The bispectral index (BIS) is a common diagnostic index used to measure the depth of anesthesia based on EEG signals.…”

Necessity and Importance of Developing AI in Anesthesia from the Perspective of Clinical Safety and Information Security

“…Gu et al [ 33 ] evaluated DoA based on multiple EEG frequency domains, and entropy features combined with an ANN, with high classification accuracy for awake, shallow, and moderate anesthesia. Ramaswamy et al [ 34 , 35 ] extracted EEG spectrum features using clinical trial datasets, logistic regression, support vector machine, and random forest model training. The results showed that the EEG pattern trained by the RF model was similar to non-NREM sleep phase 3 and accurately predicted its sedation depth.…”