Spectral features of electroencephalogram in characterizing various brain states under anesthesia
Bibliographic record
Abstract
The administration of the anesthetic agents is known to alter the electroencephalogram (EEG) signal significantly with the brain being their primary target. In this study, we analyzed the EEG recorded from six ASA I/II patients undergoing a 1-2 hour surgery. The EEG was collected before and during induction, maintenance and recovery of anesthesia using the 10/20 lead-system. A combination of fentanyl and propofol (± rocuronium) was used for induction and a Sevoflurane in air/O(2) mixture was administered through an endotracheal tube to achieve the steady minimum alveolar concentration (MAC). This study showed that 0 to 4 Hz signal power was most sensitive to the changes associated with induction of anesthesia whereas the 4 to 12 Hz power was important in classifying states during maintenance of anesthesia. Anesthesia also promoted heightened phase coherence in 8 to 16 Hz and 16 to 30 Hz ranges during maintenance and induction of anesthesia, respectively. Additionally, strong cross-frequency coupling between 7 to 20 Hz and 10 to 40 Hz was observed during anesthesia suggesting alteration of neural coding.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".