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Investigation of data-driven optical neuromonitoring approach during general anesthesia with sevoflurane
Journal article   Open access   Peer reviewed

Investigation of data-driven optical neuromonitoring approach during general anesthesia with sevoflurane

Gabriela Hernandez-Meza, Meltem Izzetoglu, Ahmet Sacan, Michael Green and Kurtulus Izzetoglu
Neurophotonics (Print), v 4(4), pp 041408-041408
01 Oct 2017
DOI: https://doi.org/10.1117/1.NPh.4.4.041408
PMID: 28840160
url
https://doi.org/10.1117/1.nph.4.4.041408View
Published, Version of Record (VoR)Maybe Open Access (Publisher Bronze),  Open
url
https://doi.org/10.1117/1.NPh.4.4.041408View
Published, Version of Record (VoR) Open

Abstract

Life Sciences & Biomedicine Neurosciences Neurosciences & Neurology Optics Physical Sciences Science & Technology
Anesthesia monitoring currently needs a reliable method to evaluate the effects of the anesthetics on its primary target, the brain. This study focuses on investigating the clinical usability of a functional near-infrared spectroscopy (fNIRS)-derived machine learning classifier to perform automated and real-time classification of maintenance and emergence states during sevoflurane anesthesia. For 19 surgical procedures, we examine the entire continuum of the maintenance-transition-emergence phases and evaluate the predictive capability of a support vector machine (SVM) classifier during these phases. We demonstrate the robustness of the predictions made by the SVM classifier and compare its performance with that of minimum alveolar concentration (MAC) and bispectral (BIS) index-based predictions. The fNIRS-SVM investigated in this study provides evidence to the usability of the fNIRS signal for anesthesia monitoring. The method presented enables classification of the signal as maintenance or emergence automatically as well as in real-time with high accuracy, sensitivity, and specificity. The features local mean HbTotal, std HbO(2), local min Hb and HbO(2), and range Hb and HbO(2) were found to be robust biomarkers of this binary classification task. Furthermore, fNIRS-SVM was capable of identifying emergence before movement in a larger number of patients than BIS and MAC. (C) 2017 Society of Photo-Optical Instrumentation Engineers (SPIE)

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Web of Science research areas
Neurosciences
Optics
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