Single Channel QRS Detection Using Wavelet And Median Denoising With Adaptive Multilevel Thresholding
Bibliographic record
Abstract
The study of heartbeats in electrocardiogram (ECG) signals is very important to sustain good health. Any anomalies in the heart rhythm can be detected by carefully studying the ECG signal. The detection of the QRS is obstructed by external and internal sources of noise. Automatic detection of the QRS is achieved by diminishing these noises to a minimum by different types of filtering such as band-pass filtering, wavelet transform, and applying thresholds. This paper presents a new method of QRS detection using discrete wavelet transform (DWT), median filtering, and adaptive multilevel thresholding (AMT). The proposed method is tested for the MIT-BIH Arrhythmia database and shows a high sensitivity of 99.74%, positive predictivity of 99.88%, and a detection error rate of 0.38%. In addition to this, the proposed technique is quite robust and can adapt to signals with a low signal-to-noise ratio.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.001 |
| 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".