The repeatability of estimated systolic time intervals in healthy subjects using seismocardiogram and electrocardiogram
Bibliographic record
Abstract
OBJECTIVE: We investigated the repeatability of systolic time intervals (STIs) in healthy subjects using a combination of seismocardiogram (SCG) and electrocardiogram (ECG). STIs have been extensively used in the past to quantify heart performance, particularly the left ventricle. In this study, STIs included pre-ejection period (PEP), left ventricular ejection time (LVET), and their ratio. APPROACH: We conducted the repeatability test of STI estimation through two experiments. The first involved three consecutive one-minute recordings separated by one-minute intervals, and the second involved two one-minute recordings separated by 24 h. Twenty healthy subjects participated in our study. We considered the coefficient of variation (CV) to quantify the repeatability. As there was no agreed upon values for optimal CV values, we compared our results with an alternative method using a combination of impedance cardiography (ICG) and ECG. Similar to our method, the alternative method was noninvasive and could be employed for personal heart monitoring. We also studied the repeatability after STIs were corrected for heart rate using two approaches. The first approach used a multiplicative factor per subject based on the heart rates in each recordings of that subject. The second approach employed sex-specific regression models for all subjects (Weissler's equations). MAIN RESULTS: We found that the repeatability of our method (SCG and ECG) was in agreement with the alternative method (ICG and ECG) in both experiments. Moreover, the Weissler's equations approach for heart rate increased the repeatability. SIGNIFICANCE: It can be concluded that estimation of PEP, LVET and their ratio through SCG and ECG signals was repeatable in healthy subjects.
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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 | 0.003 | 0.010 |
| 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 | 0.001 | 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".