Toward a better noninvasive assessment of preejection period: A novel automatic algorithm for B‐point detection and correction on thoracic impedance cardiogram
Why this work is in the frame
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Bibliographic record
Abstract
Impedance cardiography is the most common clinically validated, noninvasive method for determining the timing of the opening of the aortic valve, an important event used for measuring preejection period, which reflects sympathetic beta-adrenergic influences on the heart. Automatic detection of the exact time of the opening of the aortic valve (B point on the impedance cardiogram) has proven to be challenging as its appearance varies between and within individuals and may manifest as a reversal, inflection, or rapid slope change of the thoracic impedance derivative's (dZ/dt) rapid rise. Here, a novel automatic algorithm is proposed for the detection of the B point by finding the main rapid rise of the dZ/dt signal, which is due to blood ejection. Several conditions based on zero crossings, minima, and maxima of the dZ/dt signal and its derivatives are considered to reject any unwanted noise and artifacts and select the true B-point location. The detected B-point locations are then corrected by modeling the B-point time data using forward and reverse autoregressive models. The proposed algorithm is validated against expert-detected B points and is compared with different conventional methods; it significantly outperforms them by at least 54% in mean error, 30% in mean absolute error, and 27% in standard deviation of error. This algorithm can be adopted in ambulatory studies requiring beat-to-beat evaluation of cardiac hemodynamic parameters over extended time periods where expert scoring is not feasible.
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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