Analysis of seismocardiogram capability for trending stroke volume changes: A lower body negative pressure study
Bibliographic record
Abstract
Features were extracted from seismocardiogram (SCG) data to correlate with stroke volume changes, estimated from arterial pressure recorded from the finger. Stroke volume was gradually reduced in twenty nine human subjects using lower body negative pressure. Twenty three features were extracted from SCG amplitudes and timings. There was at least one feature in every subject with correlation coefficient of 0.87or greater (P-value <0.01). The ratio of left ventricular pre-ejection interval to left ventricular ejection time (LVPEI/LVET) proved to correlate the most with stroke volume (r=-0.92±0.06) compared to the other features. The second most correlated SCG feature, with stroke volume, was LVPEI with (r=−0.90±0.12). The findings of this paper suggest that with simple processing of SCG it should be possible to detect a sudden drop in stroke volume that could result from a variety of different cardiac abnormalities.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".