Precordial acceleration signals improve the performance of diastolic timed vibrations
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: This paper introduces a seismocardiography based methodology of predicting the start and the end of diastole to be used in diastolic timed vibrations (DTV), which provides non-invasive emergency treatment of acute coronary thrombosis by applying direct mechanical vibrations to the patient chest during diastole of heart cycles. It is proposed that seismocardiogram (SCG), in combination with electrocardiogram (ECG), provides a new means of diastole prediction. METHODS: An accelerometer was placed on the sternum of 120 healthy participants and 22 ischemic heart patients to record precordial accelerations created by the heart. The accelerometer signal was used to extract SCG and phonocardiogram (PCG). Two independent trained experts annotated the extracted signals based on the timings of the start and end of diastole. RESULTS: In the ischemic heart disease population by using 15 consecutive SCG cycles, the start and end of diastole was predicted in the upcoming cycles with 95 percentile error margin of 10.7 ms and 5.8 ms, respectively. These error margins were 7.4 ms and 3.5 ms, respectively, for normal participants. CONCLUSION: The results provide that prediction of the aortic valve closure point in the SCG signal helps start the vibrator in time to cover most of the isovolumic relaxation period. Also, through prediction of the mitral valve closure point in the SCG signal, safety of the technique can be assessed through prediction of the amount of unwanted vibrations applied during the isovolumic contraction period.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".