Cardiac Contractility and Performance can be Reliably Measured Day-to-Day Using Digital Ballistocardiography
Bibliographic record
Abstract
PURPOSE: We measured the mechanical function of the heart to determine whether cardiac contractility can be reliably measured using digital ballistocardiography (dBG), as this would have important implications for non-invasive cardiac assessment following exercise training or tapering. METHODS: A group of healthy subjects (N=8; 3 females; mean ± SD, age= 31.7±10.5yrs) with no known cardiovascular disease were assessed on three consecutive days (D1, D2, D3), at the same time of the day and under similar conditions in a quite laboratory setting. Each subject had the dBG-300 sensor attached to the skin using solid gel electrodes (single lead EKG) while lying supine under resting conditions. The factory calibrated dBG-300 sensor, which records the hearts vibration (milli-gravity, mG) was placed over the sternum approximately 1cm above the xiphoid process. Thereafter, a 30-second ballistocardiogram was recorded and stored for later analysis. A total of 15 dBG waveforms (heart beats) were analysed for each subject per day and then averaged (mean±SD). RESULTS: Results showed that group means were not statistically (p≤0.05) different for any of the following contractile variables, including: atrial systole or A-wave (D1= 9.3±3.5 mG; D2= 8.6±4.9 mG; D3= 9.0±5.4 mG), mitral valve open (D1= 26.3±6.5 mG; D2= 26.2±7.5 mG; D3= 23.7±6.2 mG), aortic valve open (D1= 23.9±8.6 mG; D2= 20.8±10.7 mG; D3= 16.5±8.9 mG), early diastolic filling (E-wave) (D1= 13.4±5.8 mG; D2= 13.3±7.1 mG; D3= 12.4±5.2 mG), and E/A ratio (D1= 1.8±0.9; D2= 2.5±1.9; D3= 2.1±1.8). However, significant differences were found within subject data for some individuals, but these differences were likely related to changes in daily heart rate (p≤0.05). CONCLUSION: These data suggest that: 1) day-to-day cardiac contractility can be measured reliably using non-invasive dBG; 2) individual differences day-to-day should be corrected to an arbitrary heart rate, and 3) both systolic and diastolic cardiac events can be reliably monitored before and after an exercise training program with dBG.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".