Quantitative Variables Derived from the Electroencephalographic Signal to Assess Depth of Anaesthesia in Animals: A Narrative Review
Bibliographic record
Abstract
Accurately assessing the depth of anaesthesia in animals remains a challenge, as traditional monitoring methods fail to capture subtle changes in brain activity. This review aimed to systematically map and critically evaluate the range of quantitative variables derived from electroencephalography (EEG) used to monitor sedation or anaesthesia in live animals, excluding laboratory rodents, over the past 35 years. Studies were identified through comprehensive searches in major biomedical databases (PubMed, Embase, CAB Abstract). To be included, studies had to report EEG use in relation to anaesthesia or sedation in living animals. A total of 169 studies were selected after screening and data extraction. Information was charted by animal species and reported EEG-derived variables. The most frequently reported variables were spectral edge frequencies, spectral power metrics, suppression ratio, and proprietary indices, such as the Bispectral Index. Methodological variability was high, and no consensus emerged on optimal EEG measures across species. While EEG-derived quantitative variables provide valuable insights, their interpretation remains highly context-dependent. Further research is necessary to refine these methods, explore variable combinations, and improve their clinical relevance in veterinary medicine.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".