Volume 12, Issue 2 (March & April 2021)
BCN 2021, 12(2): 269-280 |
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Sanjari N, Shalbaf A, Shalbaf R, Sleigh J. Assessment of Anesthesia Depth Using Effective Brain Connectivity Based on Transfer Entropy on EEG Signal. BCN 2021; 12 (2) :269-280
URL: http://bcn.iums.ac.ir/article-1-1844-en.html
URL: http://bcn.iums.ac.ir/article-1-1844-en.html
1- Department of Medical Physics and Biomedical Engineering, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
2- Institute for Cognitive Science Studies, Tehran, Iran.
3- Department of Anesthesia, Waikato Hospital, Hamilton, New Zealand.
2- Institute for Cognitive Science Studies, Tehran, Iran.
3- Department of Anesthesia, Waikato Hospital, Hamilton, New Zealand.
Abstract:
Introduction: Ensuring an adequate Depth of Anesthesia (DOA) during surgery is essential for anesthesiologists. Since the effect of anesthetic drugs is on the central nervous system, brain signals such as Electroencephalogram (EEG) can be used for DOA estimation. Anesthesia can interfere among brain regions, so the relationship among different areas can be a key factor in the anesthetic process.
Methods: In this paper, by combining the Wiener causality concept and the conditional mutual information, a nonlinear effective connectivity measure called Transfer Entropy (TE) is presented to describe the relationship between EEG signals at frontal and temporal regions from eight volunteers in three anesthetic states (awake, unconscious and recovery). This index is also compared with Granger causality and partial directional coherence methods as common effective connectivity indexes.
Results: Based on a statistical analysis of the probability predictive value and Kruskal-Wallis statistical method, TE can effectively fallow the effect-site concentration of propofol and distinguish the anesthetic states well, and perform better than the other effective connectivity indexes. This index is also better than Bispectral Index (BIS) as commercial DOA monitor because of the faster response and higher correlation with the drug concentration effect-site, less irregularity in the unconscious state and better ability to distinguish three states of anesthestesia.
Conclusion: TE index is a confident indicator for designing a new monitoring system of the two EEG channels for DOA estimation.
Methods: In this paper, by combining the Wiener causality concept and the conditional mutual information, a nonlinear effective connectivity measure called Transfer Entropy (TE) is presented to describe the relationship between EEG signals at frontal and temporal regions from eight volunteers in three anesthetic states (awake, unconscious and recovery). This index is also compared with Granger causality and partial directional coherence methods as common effective connectivity indexes.
Results: Based on a statistical analysis of the probability predictive value and Kruskal-Wallis statistical method, TE can effectively fallow the effect-site concentration of propofol and distinguish the anesthetic states well, and perform better than the other effective connectivity indexes. This index is also better than Bispectral Index (BIS) as commercial DOA monitor because of the faster response and higher correlation with the drug concentration effect-site, less irregularity in the unconscious state and better ability to distinguish three states of anesthestesia.
Conclusion: TE index is a confident indicator for designing a new monitoring system of the two EEG channels for DOA estimation.
Type of Study: Original |
Subject:
Computational Neuroscience
Received: 2020/07/1 | Accepted: 2020/12/12 | Published: 2021/03/1
Received: 2020/07/1 | Accepted: 2020/12/12 | Published: 2021/03/1
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