Potential EEG biomarkers of sedation doses in intensive care patients unveiled by using a machine learning approach. (26th February 2019)

• Record Type: Journal Article
• Authors: Sanz-García, Ancor; Pérez-Romero, Miriam; Pastor, Jesús; Sola, Rafael G; Vega-Zelaya, Lorena; Vega, Gema; Monasterio, Fernando; Torrecilla, Carmen; Pulido, Paloma; Ortega, Guillermo J
• Is Part Of: Journal of neural engineering. Volume 16:Number 2(2019:Apr.)
• Publication Date: 2019-02-26
• Subjects: EEG -- ICU -- sedation -- brain networks -- machine learning; Neurosciences -- Periodicals; Biomedical engineering -- Periodicals; 612.8
• DOI: 10.1088/1741-2552/ab039f
• Languages: English
• ISSNs: 1741-2560

Abstract

Abstract: Objective . Sedation of neurocritically ill patients is one of the most challenging situation in ICUs. Quantitative knowledge on the sedation effect on brain activity in that complex scenario could help to uncover new markers for sedation assessment. Hence, we aim to evaluate the existence of changes of diverse EEG-derived measures in deeply-sedated (RASS-Richmond agitation-sedation scale −4 and −5) neurocritically ill patients, and also whether sedation doses are related with those eventual changes. Approach . We performed an observational prospective cohort study in the intensive care unit of the Hospital de la Princesa. Twenty-six adult patients suffered from traumatic brain injury and subarachnoid hemorrhage were included in the present study. Long-term continuous electroencephalographic (EEG) recordings (2141 h) and hourly annotated information were used to determine the relationship between intravenous sedation infusion doses and network and spectral EEG measures. To do that, two different strategies were followed: assessment of the statistical dependence between both variables using the Spearman correlation rank and by performing an automatic classification method based on a machine learning algorithm. Main results . More than 60% of patients presented a correlation greater than 0.5 in at least one of the calculated EEG measures with the sedation dose. The automatic classification method presented an accuracy of 84.3% in discriminating between different sedation doses. In both cases the nodes' degree was the most relevant measurement. Significance . The results presented here provide evidences of brain activity changes during deep sedation linked to sedation doses. Particularly, the capability of network EEG-derived measures in discriminating between different sedation doses could be the framework for the development of accurate methods for sedation levels assessment.