Blood Pressure Level and Heart Rate Detection from Photoplethysmography Signals Using DT–CWT
Bibliographic record
Abstract
In this study, it was aimed to estimate systolic and diastolic blood pressures and heart rate using Photoplethysmography (PPG) signals. The PPG signals data used in the study were obtained from an open database containing signals and information of 219 people. With the help of the Dual Tree Complex Wavelet Transform (DT-CWT) method, The properties such as the average power, absolute value mean, kurtosis, skewness and standard deviation of the coefficients of each frequency subbands were obtained. Regression analysis was performed on the extracted PPG signals using Linear Regression (DR), Random Forest (RF) and Support Vector Machines (SVM) algorithms in the Weka program, and blood pressure levels and heart rates were estimated. As a result of the regression analysis, it was seen that blood pressure and heart rate estimations with a higher correlation coefficient and a lower average margin of error, heart rate and diastolic blood pressure analysis with the RF algorithm using the DT-CWT method, and systolic blood pressure analysis with the SVM algorithm would be more accurate.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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 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".