Predicting the depth of anaesthesia with 40-Hz ASSR
Bibliographic record
Abstract
An algorithm for predicting the amplitude of 40-Hz auditory steady state response (ASSR), which is an indicative of depth of anaesthesia, is presented in this paper. The amplitude of 40-Hz ASSR is an indicative of the depth of anaesthesia. Predicting this amplitude will help anesthesiologists in choosing the dose of the anaesthetic agents and have an estimation of the patients' next depth of anaesthesia state. The method is applied to the 40-Hz ASSR signals recorded from 20 human subjects during surgical operation. The algorithm uses sequential feature selection and multi-linear regression for choosing the most informative features and estimating the amplitude of ASSR on the next cycle and at the end of induction stage, when the patient looses his/her eyelash reflex. The algorithm uses the dose of anaesthetic drug, some of the demographics and medical parameters of the patients and their previous ASSR cycles for prediction. As the system provided with more ASSR cycles, the error in prediction decreases and estimation gets more accurate. The method is applied on the induction phase where patients' depth of anaesthesia changes very fast but it is applicable to all other stages of anaesthesia as well.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".