Adaptive R-Peak Detector in Extreme Noise Using EMD Selective Analyzer
Bibliographic record
Abstract
Accurately detecting R-peaks in electrocardiogram (ECG) signals is important in various health monitoring applications, such as cuffless blood pressure measurements. In some cases, such as with single-arm ECG measurement, the signal may be buried in large amounts of noise. Moreover, many of the current existing ECG monitors pause the reading until the noise conditions are better, which may lead to a large loss in data. In this work, an adaptive approach is introduced, based on Empirical Mode Decomposition (EMD), to accurately detect the R-peaks in an extremely noisy ECG signal obtained using a single-arm measurement. The proposed algorithm starts by examining the Intrinsic Mode Functions (IMFs) extracted from the recorded signal, using the Hausdorff Distance (HD) as a selection tool, before applying the peak detection algorithm. Experimental measurements used to evaluate the algorithm were obtained from 10 healthy subjects for a total of 30 noisy ECG records, containing close to 2000 QRS complexes in total, the majority of which were at an estimated S/N (Signal-to-noise ratio) below −10 dB. The obtained results show a promising technique for detecting R-peaks in extreme noise with a low percentage of detection error ratio and an average percentage error in R-R interval estimation of 6.8%.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".