Performance Analysis of Oscillometric Blood Pressure Estimation Techniques in Cardiac Patients
Bibliographic record
Abstract
Accurate Blood Pressure (BP) estimation plays a crucial role in providing significant information about the patient's cardiovascular health. In this paper, performance analysis of the various oscillometric BP estimation algorithms - Maximum Amplitude Algorithm (MAA), Maximum Minimum Slope Algorithm (MMSA), Arterial Lumen Area Algorithm (ALA) and Pulse Transit Time (PTT) algorithm has been performed. The analysis was carried out for a data set comprising 37 patients suffering from cardiac problems. The BP for each cardiac patient, estimated with the four oscillometric BP estimation algorithms, was compared with reference invasive BP measurements. The reference BP was estimated from the arterial pulse wave obtained by placing the pressure sensor inside the femoral or brachial arteries of the patients. The comparisons between the oscillometric BP estimation algorithms and the estimated reference BP are reported in terms of Mean Absolute Error (MAE), Standard Deviation of Error (SDE) and Bland-Altman plots analysis. It was found that the ALA followed by PTT are relatively accurate in estimation of BP with lowest MAE and SDE for cardiac patients, whereas MAA is the least accurate BP estimation technique. The results also emphasize the need to develop improved algorithms to estimate BP in cardiac patients.
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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 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 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".