Toward Continuous Estimation of Cardiorespiratory Parameters in Oscillometry: A Simulation Study
Bibliographic record
Abstract
Blood pressure's oscillometric waveform (OMW) comprises several cardiovascular components such as cardiac activity, respiration-related changes, and Mayer wave that contribute to its total variability over time. Accurate modeling of the OMW as a function of these components and continuous tracking of their underlying parameters can provide insights into the cardiovascular system dynamics and help determine the role played by each component in blood pressure variability. This paper presents a new state-space model for the OMW consisting of different parameters such as cardiac and respiration frequencies, amplitudes, and phases. Since the dynamic state-space model of the OMW is highly nonlinear and dependent on a large number of parameters, we utilized the extended Kalman filter (EKF). Since the EKF accuracy is highly dependent on the parameter's initial values, to obtain reasonable estimates of model initial values, a system identification procedure based on frequency domain analysis and curve-fitting was employed. The proposed method's performance was analyzed on simulated data with and without the proposed system identification procedure. A mean absolute percentage error of 2.68% was achieved in estimating OMW when using the proposed system identification approach. The proposed approach shows promise toward beat-to-beat tracking of cardiovascular parameters in oscillometric devices.
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 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 | 0.001 |
| 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".