Development of the hyperspectral near infrared spectroscopy for monitoring tissue oxygen metabolism
Bibliographic record
Abstract
Near Infrared Spectroscopy (NIRS) employs optical window of 650-1100 nm to measure the concentrations of different chromophores such as, oxygenated hemoglobin (HbO2), deoxygenated hemoglobin (Hb) and water. In addition, changes in the redox cytochrome c oxidase (Cyt-ox) can be measured using hyperspectral NIRS. Cyt-ox has a relatively low concentration compared to HbO2 and HHb therefore its measurements is very challenging. The main objective of this thesis was to identify the best signal-processing algorithm for deconvolution of the Cyt-ox changes. In this thesis two different studies have been done; in the first study, functional broadband NIRS during simulated driving (on 16 adult subjects) has been recorded and the best data processing algorithm for deconvolution of the chromophores was identified. In the second series of experiments broadband NIRS data during cardiac arrest and resuscitation was recorded on 19 pigs. A novel signal-processing algorithm was introduced to deconvolve Cyt-ox. The Introduction section covers relevant aspects of the theory and mathematical concepts. The Journal Manuscript part contains the two projects mentioned above. The last part is The Journal Manuscript part contains the two projects mentioned above. The last part is allocated for the overall conclusion of the entire thesis.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".