ArticleBasic and clinical neuroscience
Assessment of Anesthesia Depth Using Effective Brain Connectivity Based on Transfer Entropy on EEG Signal.
Article in Basic and clinical neuroscience. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 18 citations in OpenAlex.
- Depth of anesthesia monitoring: Current evidence, clinical impact, and future directions.The Journal of international medical research · 2026Review
- Comparative analysis of processed EEG indices and entropy-based metrics for assessing anesthetic depth: a scoping review - PRISMA-ScR.BMC biomedical engineering · 2026Article
- Criticality supports cross-frequency cortical-thalamic information transfer during conscious states.eLife · 2024Article
- Assessing rheoencephalography dynamics through analysis of the interactions among brain and cardiac networks during general anesthesia.Frontiers in network physiology · 2022Article
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Authors and funding
4 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionEnsuring 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.
methodsIn 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.
resultsBased 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.
conclusionTE index is a confident indicator for designing a new monitoring system of the two EEG channels for DOA estimation.
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