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Investigation of optical neuro-monitoring technique for detection of maintenance and emergence states during general anesthesia
Journal article   Peer reviewed

Investigation of optical neuro-monitoring technique for detection of maintenance and emergence states during general anesthesia

Gabriela Hernandez-Meza, Meltem Izzetoglu, Mary Osbakken, Michael Green, Hawa Abubakar and Kurtulus Izzetoglu
Journal of clinical monitoring and computing, v 32(1), pp 147-163
Feb 2018
DOI: https://doi.org/10.1007/s10877-017-9998-x
PMID: 28214930
Featured in Collection :   UN Sustainable Development Goals @ Drexel

Abstract

Adult Algorithms Anesthesia - methods Anesthesia, General Brain - physiopathology Cerebrovascular Circulation - physiology Female Heart Rate Hemodynamics Hemoglobins Humans Machine Learning Male Middle Aged Monitoring, Intraoperative Oxygen - metabolism Prefrontal Cortex - drug effects Reproducibility of Results Sevoflurane - administration & dosage Societies, Medical Spectroscopy, Near-Infrared - methods Time Factors Young Adult
The American Society of Anesthesiologist recommends peripheral physiological monitoring during general anesthesia, which offers no information regarding the effects of anesthetics on the brain. Since no "gold standard" method exists for this evaluation, such a technique is needed to ensure patient comfort, procedure quality and safety. In this study we investigated functional near infrared spectroscopy (fNIRS) as possible monitor of anesthetic effects on the prefrontal cortex. Anesthetic drugs, such as sevoflurane, suppress the cerebral metabolism and alter the cerebral blood flow. We hypothesize that fNIRS derived features carry information on the effects of anesthetics on the prefrontal cortex (PFC) that can be used for the classification of the anesthetized state. In this study, patients were continuously monitored using fNIRS, BIS and standard monitoring during surgical procedures under sevoflurane general anesthesia. Maintenance and emergence states were identified and fNIRS features were identified and compared between states. Linear and non-linear machine learning algorithms were investigated as methods for the classification of maintenance/emergence. The results show that changes in oxygenated (HbO ) and deoxygenated hemoglobin (HHb) concentration and blood volume measured by fNIRS were associated with the transition between maintenance and emergence that occurs as a result of sevoflurane washout. We observed that during maintenance the signal is relatively more stable than during emergence. Maintenance and emergence states were classified with 94.7% accuracy with a non-linear model using the locally derived mean total hemoglobin, standard deviation of HbO , minimum and range of HbO and HHb as features. These features were found to be correlated with the effects of sevoflurane and to carry information that allows real time and automatic classification of the anesthetized state with high accuracy.

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16 citations in Web of Science
18 citations in Scopus

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Web of Science research areas
Anesthesiology
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