Moving toward automatic and standalone delineation of seismocardiogram signal
Bibliographic record
Abstract
The purpose of this research is to propose an algorithm that could accomplish automatic delineation of the seismocardiogram (SCG) signal without using a reference electrocardiogram R-wave. As a result, the SCG signal could be used, as a stand-alone solution for many cardiovascular medical applications such as hemorrhage detection, cardiac computed tomographic gating, cardiac resynchronization therapy, hemodynamics estimations and diastolic timed vibration. Multiple envelopes were derived from the seismocardiogram signal by using filtering and triple integration. The first envelope is referred as the heart rate envelope, which has the characteristics of having a period of exactly one cardiac cycle and its purpose is to replace the ECG R-wave as a reference point. Our dataset is based on the lower body negative pressure (LBNP) test that was conducted on 18 individuals, containing 21610 cardiac cycles. For 94% of the LBNP dataset, the aforementioned envelope estimated heart rate within 3 beats per minute. Three different peaks of the SCG signal are of our interest: isovolumic contraction (IM), aortic valve opening (AO) and aortic valve closure (AC). For each of these desired peaks of the SCG signal, a different envelope was designed in a manner that its peak is very close to IM, AO and AC, respectively. For the same lower body negative pressure data set, a mean difference of (9, 9, 6) and standard deviation of (8, 9, 9) millisecond between the peak of envelopes and IM, AO and AC is accomplished. This could be used as a good initial estimation of the annotation points.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.004 |
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".