ArticleNeuroscience bulletin2025
Accurate Machine Learning-based Monitoring of Anesthesia Depth with EEG Recording.
Article in Neuroscience bulletin, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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The trial behind it
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Who cites it
8 citing papers in PubMed.
- LSD reconfigures cortical dynamics through faster brain rhythms and increased fractal dimension.Communications biology · 2026Article
- Mapping Brain Spatial Metabolic Atlas Reveals Anesthesia-Induced Region-Specific Alterations.Neuroscience bulletin · 2026Article
- Visual Perception and Gamma Oscillations in Cat V1 are Dynamically Correlated in Contrast Sensitivity Functions.Neuroscience bulletin · 2026Article
- The Perioperative Neurocognitive Disorder Prediction Based on AI-Assisted EEG Dynamic Features in Anesthetized Mice.Diagnostics (Basel, Switzerland) · 2026Article
- Propofol treatment correlates with changes in pain sensitivity and thalamocortical glutamate/GABA balance in sleep-deprived mice.Journal of anesthesia and translational medicine · 2026Article
- Iterative multiblock framework for high frequency EEG based neurological disorder detection.Scientific reports · 2026Article
- Explainable artificial intelligence in anesthesiology prediction models: bridging the gap from black-box algorithms to clinical decision support.Frontiers in medicine · 2026Review
- Artificial Intelligence in Anesthesia: Enhancing Precision, Safety, and Global Access Through Data-Driven Systems.Journal of clinical medicine · 2025Review
Corrections and comments
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
General anesthesia, pivotal for surgical procedures, requires precise depth monitoring to mitigate risks ranging from intraoperative awareness to postoperative cognitive impairments. Traditional assessment methods, relying on physiological indicators or behavioral responses, fall short of accurately capturing the nuanced states of unconsciousness. This study introduces a machine learning-based approach to decode anesthesia depth, leveraging EEG data across different anesthesia states induced by propofol and esketamine in rats. Our findings demonstrate the model's robust predictive accuracy, underscored by a novel intra-subject dataset partitioning and a 5-fold cross-validation method. The research diverges from conventional monitoring by utilizing anesthetic infusion rates as objective indicators of anesthesia states, highlighting distinct EEG patterns and enhancing prediction accuracy. Moreover, the model's ability to generalize across individuals suggests its potential for broad clinical application, distinguishing between anesthetic agents and their depths. Despite relying on rat EEG data, which poses questions about real-world applicability, our approach marks a significant advance in anesthesia monitoring.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.