Method for the Measurement of the Dicrotic Notch in the Photoplethysmography Signal
Bibliographic record
Abstract
A critical feature in photoplethysmography (PPG) signals is the diastolic (reflective) pressure wave following the systolic (forward) pressure wave during each compression cycle. The shape of these can often result in a feature known as the dicrotic notch. The PPG signal’s attributes provide valuable insights into cardiovascular health related to arterial vessel health, yet its measurement and characterization remain challenging due to variability and noise. We introduce an improved Gaussian decomposition algorithm to identify and quantify the forward and reflected pressure waves in individual cycles of the PPG signal. The algorithm iteratively decomposes the signal for each heartbeat into two Gaussian components, isolating the rising edge portion of the signal to model the systolic peak, followed by a model of the remaining diastolic peak. This enables robust characterization of the systolic and diastolic peaks across varying signal classes. Validation was performed on synthetic data, with parameter sweeps to assess robustness under different noise levels, and a wide range of signal morphologies then revalidated on patient data from the MIMIC-III database. Results demonstrate that the algorithm accurately identifies the PPG waveform measurements including dicrotic notch in most PPG signal classes, outperforming traditional peak detection and derivative-based methods, particularly under moderate noise conditions.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".