A systematic review of electroencephalography in acute cerebral hypoxia: clinical and diving implications
Bibliographic record
Abstract
Introduction: Hypoxia can cause central nervous system dysfunction and injury. Hypoxia is a particular risk during rebreather diving. Given its subtle symptom profile and its catastrophic consequences there is a need for reliable hypoxia monitoring. Electroencephalography (EEG) is being investigated as a real time monitor for multiple diving problems related to inspired gas, including hypoxia. Methods: A systematic literature search identified articles investigating the relationship between EEG changes and acute cerebral hypoxia in healthy adults. Quality of clinical evidence was assessed using the Newcastle-Ottawa scale. Results: Eighty-one studies were included for analysis. Only one study investigated divers. Twelve studies described quantitative EEG spectral power differences. Moderate hypoxia tended to result in increased alpha activity. With severe hypoxia, alpha activity decreased whilst delta and theta activities increased. However, since studies that utilised cognitive testing during the hypoxic exposure more frequently reported opposite results it appears cognitive processing might mask hypoxic EEG changes. Other analysis techniques (evoked potentials and electrical equivalents of dipole signals), demonstrated sustained regulation of autonomic responses despite worsening hypoxia. Other studies utilised quantitative EEG analysis techniques, (Bispectral index [BISTM], approximate entropy and Lempel-Ziv complexity). No change was reported in BISTM value, whilst an increase in approximate entropy and Lempel-Ziv complexity occurred with worsening hypoxia. Conclusions: Electroencephalographic frequency patterns change in response to acute cerebral hypoxia. There is paucity of literature on the relationship between quantitative EEG analysis techniques and cerebral hypoxia. Because of the conflicting results in EEG power frequency analysis, future research needs to quantitatively define a hypoxia-EEG response curve, and how it is altered by concurrent cognitive task loading.
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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 | 0.005 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.015 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".