Measurement of Cardiac Parameters by Motion Artifacts Free Photoplethysmography Signals
Bibliographic record
Abstract
In the past, it has been extensively shown that photoplethysmography (PPG) signal has sufficient information to being utilized for monitoring of various cardiorespiratory parameters. However, motion artifacts, which caused by the voluntary or involuntary movements of the subject, degrade the accuracy and reliability of such a monitoring-based technique. In this work, we propose a novel signal processing methodology to efficiently and effectively remove the motion artifacts from the acquired PPG signals. The propounded technique includes the stop-band filters to filter out the motion artifacts frequencies. The central rejection frequencies of the filters are determined by analysis of accelerometer output signals in the frequency domain. Results illustrate that our proposed motion artifacts removal technique can enhance the accuracy estimation of different cardiorespiratory parameters, such as heart-rate, respiratory cycle, and blood oxygen saturation, in comparison to the PPG signals with the motion artifacts. We demonstrate the superiorities of our proposed method in terms of ease of implementation as well as efficiency by direct comparison to the conventional adaptive filtering methods.
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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 | 0.001 |
| 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) | 0.001 | 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".