Wavelet-based Adaptive Boosting Method for Cuffless Blood Pressure Estimation on PYNQ-Z2
Bibliographic record
Abstract
Hypertension or high blood pressure is a significant global health issue. Having high blood pressure is a big risk for conditions like coronary heart disease, including ischemic and hemorrhagic stroke. In general, the measurement of blood pressure is performed using a sphygmomanometer. However, this technique has several limitations in continuous and long-term monitoring due to bulky electronic devices with pneumatic systems (pump, valve, battery) to inflate and deflate the cuff. Cuffless blood pressure estimation has recently emerged as a good alternative to overcome these limitations. This paper proposes a machine learning-based approach using wavelet-based time-frequency features and adaptive boosting regression for cuffless blood pressure estimation from photoplethysmogram signals. The efficacy of the proposed approach is evaluated using various parameters concerning different state-of-the-art approaches. The proposed approach is found to perform better than various state-of-the-art methods. Furthermore, the proposed approach is implemented on the Xilinx PYNQ-Z2 board to validate the hardware compatibility.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".