Oscillometric blood pressure estimation using principal component analysis and neural networks
Bibliographic record
Abstract
Estimation of systolic and diastolic pressures from the oscillometric waveform is a challenging task in noninvasive electronic blood pressure (BP) monitoring devices. Since the conventional oscillometric algorithms cannot model and extract the complex and nonlinear relationship that may exist between BP and oscillometric waveform, artificial neural networks (NNs) have been proposed as a possible alternative. However, the research on this topic has been limited to some simple architectures that directly estimate the BP from raw oscillation amplitudes (OAs). In this paper, we propose principal component analysis as a preprocessing step to decorrelate the OAs and extract the most effective components. Two architectures of NNs, namely, feed-forward and cascade-forward, are employed to estimate the BP using the preprocessed OAs. The networks are trained using the gradient descent with momentum and adaptive learning rate backpropagation algorithm and tested on a dataset of 85 BP waveforms. The performance is then compared with that of the conventional maximum amplitude algorithm and already published NN-based methods. It is found that the proposed networks achieve lower values of mean absolute error and standard deviation of error in estimation of BP compared with the other studied methods.
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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 | 0.004 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".