An integrated system to compensate for temperature drift and ageing in non-invasive blood pressure measurement
Bibliographic record
Abstract
Accurate blood pressure (BP) measurement is critical in the diagnosis and management of hypertension, as an error of 5 mmHg can be responsible for doubling or halving the number of patients diagnosed with this condition. Sensor drift, due to changing environmental factors, such as ambient temperature, can contribute to the inaccuracy. Studies also show that long term sensor drift, or ageing, can lead to a change of almost 9 mmHg in blood pressure measurement during the first three months of usage. In this work, a new stage is added to current cuff-based BP devices. This stage is responsible for adjusting the pressure reading before displaying it to end users, by monitoring changes in the ambient temperature and sensor ageing and adaptively compensating for these inaccuracies. These sources of inaccuracy are suppressed using algorithms based on Empirical Mode Decomposition (EMD), which has the feature of removing unwanted noise components without affecting the phase or the frequency distribution of the measured signal.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".