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

Gabriela Hernandez-Meza, Meltem Izzetoglu, Ahmet Sacan, Michael Green, Kurtulus Izzetoglu

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


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 HbO2, local min Hb and HbO2, and range Hb and HbO2 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.

Original languageEnglish
Article number041408
Issue number4
StatePublished - 1 Oct 2017
Externally publishedYes


  • anesthesia monitoring
  • cerebral hemodynamics
  • depth of anesthesia
  • functional near-infrared spectroscopy
  • machine learning


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