Novel Method for More Precise Determination of Oscillometric Pulse Amplitude Envelopes
Bibliographic record
Abstract
Curve fitting for oscillometric waveforms is vital to maximum amplitude algorithm (MAA) in blood pressure measurement. Popular methods in recent years, such as asymmetric Gaussian or Lorentzian functions, perform well when the profile of the oscillometric waveforms (OMW) are close to them. But they will have a relatively large mean square error (MSE) when the oscillometric pulse amplitude envelopes are not so regularly shaped. In this contribution, the artificial neural network (ANN) is implemented instead for the curving fitting. Aided by LabVIEW and MATLAB, its number of neurons in the hidden layer, weight initialization algorithm, training goal and learing algorithm are implemented or carefully considered after some necessary preliminary work. The experiment with 48 subjects ranging in age from 18 to 60 years is included in this research. The results show that the back propagation network with 11 neurons in the hidden layer, 0.0025 as training goal and Levenberg-Marquardt learning algorithm is well enough for the curve fitting. ANN with proposed optimum parameters is then compared with the asymmetric Gaussian/Lorentzian functions. After properly adjust the max epoch, ANN can finish computing the envelope in less than a second (3.3 GHz CPU and 4 GB RAM) in all of our experiments like the other two methods while its MSE is still much lower than the other two methods. Their performance in measuring blood pressure is also compared, and ANN shows greater robustness.
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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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".