Moving toward automatic and standalone delineation of seismocardiogram signal
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 it