Challenges in Using Seismocardiography for Blood Pressure Monitoring
Bibliographic record
Abstract
The capability of continuously and non-invasive blood pressure (BP) monitoring is valuable in the clinical setting.Pulse transit time (PTT) has shown the promise to track changes in the arterial BP.This study examines the efficacy of seismocardiogram (SCG) and photoplethysmogram (PPG) combination for timing the proximal and distal pulse respectively of a pulse wave.The PTT is defined as a difference between the proximal and distal timing of a pulse wave.Methods: A total of 18 subjects participated in the study.Subjects were subjected to a lower-body negative pressure (LBNP) protocol to -60 mmHg.Electrocardiogram (ECG), SCG, PPG and BP was recorded simultaneously during the LNBP protocol.Subjects that endured negative pressure to -40mmHg, had SCG signals with distinct AO points and for whom the average BP was lower in the final stage of LBNP compared to supine baseline were included in data analysis.A simple logarithmic model was used to estimate BP based on PTT.Estimated BP was then correlated with PTT.Results: A total of 7 subjects were included in the final data analysis.On an average the subjects systolic BP dropped during the LBNP protocol and PTT was shortened.None of these changes were significant.For 3 subjects the correlation between PTT and estimated systolic blood pressure was significant.Conclusion: The current study showed a trend toward shorter PTT as blood pressure lowers, but the trend was weak and not consistent.
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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 | 0.050 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.009 |
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".