Quantifying the depth of anesthesia based on brain activity signal modeling

Hyub Huh, Sang Hyun Park, Joon Ho Yu, Jisu Hong, Mee Ju Lee, Jang Eun Cho, Choon Hak Lim, Hye Won Lee, Jun Beom Kim, Kyung Sook Yang, Seung Zhoo Yoon

    Research output: Contribution to journalArticlepeer-review

    1 Citation (Scopus)


    Various methods of assessing the depth of anesthesia (DoA) and reducing intraoperative awareness during general anesthesia have been extensively studied in anesthesiology. However, most of the DoA monitors do not include brain activity signal modeling. Here, we propose a new algorithm termed the cortical activity index (CAI) based on the brain activity signals. In this study, we enrolled 32 patients who underwent laparoscopic cholecystectomy. Raw electroencephalography (EEG) signals were acquired at a sampling rate of 128 Hz using BIS-VISTA with standard bispectral index (BIS) sensors. All data were stored on a computer for further analysis. The similarities and difference among spectral entropy, the BIS, and CAI were analyzed. Pearson correlation coefficient between the BIS and CAI was 0.825. The result of fitting the semiparametric regression models is the method CAI estimate (-0.00995; P = .0341). It is the estimated difference in the mean of the dependent variable between method BIS and CAI. The CAI algorithm, a simple and intuitive algorithm based on brain activity signal modeling, suggests an intrinsic relationship between the DoA and the EEG waveform. We suggest that the CAI algorithm might be used to quantify the DoA.

    Original languageEnglish
    Pages (from-to)e18441
    Issue number5
    Publication statusPublished - 2020 Jan 1

    ASJC Scopus subject areas

    • Medicine(all)


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