Empirical mode decomposition for respiratory and heart rate estimation from the photoplethysmogram
Bibliographic record
Abstract
We introduce a method based on empirical mode decomposition (EMD) to estimate both respiratory rate (RR) and heart rate (HR) from the photoplethysmographic (PPG) signal obtained from pulse oximetry. The spectral analysis of the EMD applied to the PPG signal was used to extract two signals, the respiratory and cardiac modulations respectively. On these modulated signals, an additional spectral analysis was applied to calculate their frequency peaks. To improve spectral resolution a parametric power spectral analysis based on autoregressive modelling was selected. The frequency peak found in the respiratory and cardiac signals reflects RR and HR, respectively. The PPG signals were analysed using a 1-min sliding window with 50% overlap. The RR and HR estimation accuracy was assessed using the unnormalized root mean square (RMS) error. Median errors (quartiles) were calculated to account for the non-normal RMS distribution. The test dataset consisted of 8-min PPG and capnometric signals from 29 paediatric and 13 adults cases (42 subjects in total) containing reliable recordings of either spontaneous or controlled breathing. A research assistant manually labelled the signals. The reference RR (from capnogram) and HR (from PPG) were manually extracted. The median RMS error (quartiles) obtained for RR was 3.5 (1.1, 11) breaths/min and for HR was 0.35 (0.2, 0.59) beats/min. Therefore, the spectral analysis of the respiratory and cardiac signals extracted through EMD, introduces a useful method to estimate and monitor RR and HR simultaneously from the PPG signal obtained from pulse oximetry.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".