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.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".