ReviewIbrain2025
Research progress on the depth of anesthesia monitoring based on the electroencephalogram.
Review in Ibrain, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- Deep learning based depth of anaesthesia monitoring using EEG: a 4-layer CNN model with PSD and BSR correlation features.Physical and engineering sciences in medicine · 2026Article
- Transforming perioperative care: The current landscape and future trajectory of artificial intelligence in anesthesia-A narrative review.The Journal of international medical research · 2026Review
- Depth of anesthesia monitoring: Current evidence, clinical impact, and future directions.The Journal of international medical research · 2026Review
- Comments on "Forecasting anesthetic depth using an auto regressive transformer in propofol infusion during the induction phase" by Chi et al.Journal of anesthesia · 2026Article
- Research progress on the depth of anesthesia monitoring based on the electroencephalogram.Ibrain · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
General anesthesia typically involves three key components: amnesia, analgesia, and immobilization. Monitoring the depth of anesthesia (DOA) during surgery is crucial for personalizing anesthesia regimens and ensuring precise drug delivery. Since general anesthetics act primarily on the brain, this organ becomes the target for monitoring DOA. Electroencephalogram (EEG) can record the electrical activity generated by various brain tissues, enabling anesthesiologists to monitor the DOA from real-time changes in a patient's brain activity during surgery. This monitoring helps to optimize anesthesia medication, prevent intraoperative awareness, and reduce the incidence of cardiovascular and other adverse events, contributing to anesthesia safety. Different anesthetic drugs exert different effects on the EEG characteristics, which have been extensively studied in commonly used anesthetic drugs. However, due to the limited understanding of the biological basis of consciousness and the mechanisms of anesthetic drugs acting on the brain, combined with the effects of various factors on existing EEG monitors, DOA cannot be accurately expressed via EEG. The lack of patient reactivity during general anesthesia does not necessarily indicate unconsciousness, highlighting the importance of distinguishing the mechanisms of consciousness and conscious connectivity when monitoring perioperative anesthesia depth. Although EEG is an important means of monitoring DOA, continuous optimization is necessary to extract characteristic information from EEG to monitor DOA, and EEG monitoring technology based on artificial intelligence analysis is an emerging research direction.
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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.