Elimination of artifacts in oscillometric waveforms using empirical wavelet transform to improve accuracy of blood pressure estimation
Bibliographic record
Abstract
Blood pressure measurement is vital in healthcare and many methods have been developed for this purpose of which the oscillometric technique is the most popular. However, accurate estimation of BP using an oscillometric waveform remains a challenge, particularly when the waveform is distorted by noise and artifacts, such as due to involuntary motion. In response, this paper proposes a band pass filtering (BPF) method to deliver a filtered oscillometric signal that is suitable for blood pressure estimation using envelope detection. To validate the utility of the proposed method, two algorithms are implemented to estimate the systolic and diastolic BPs for various operating conditions. The obtained results indicate that a properly designed BPF exhibits significant improvement in delivering accurate BP measurement. It is envisioned that the proposed method is suitable for implementation in emergency medical devices, such as for blood pressure monitoring in an ambulance under significant motion artifacts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".