Heartbeat detection from the upper arm using an SWT-based zero-phase filter bank incorporated with a voting Scheme
Bibliographic record
Abstract
Electrocardiogram (ECG) signal provides a graphical representation of cardiac activity and is the most commonly adopted clinical tool for cardiac abnormalities detection. Heartbeat detection, as the first step in analyzing ECG signals, is required for an accurate diagnosis. Stationary wavelet transform (SWT) as a commonly used algorithm for heartbeat detection has a disadvantage of phase shift regarding the original signal. This work addresses this issue by presenting a new method that incorporates an SWT-based zero-phase filter bank with a voting scheme. Our results indicated that a superior performance in heartbeat detection was achieved from the upper arm compared to conventional SWT with a more accurate localization. We achieved sensitivity (SE) and positive predictive value (PPV) of 0.98±0.04 and 0.95±0.09 with the most distance of 50 ms from the actual heartbeats. The SE and PPV changed to 0.75±0.15 and 0.73±0.16, respectively for the distance of 20 ms. Clinical Relevance- The proposed method can be later implemented in wearable devices for convenient cardiac activity monitoring from upper arm or other none-conventional sites.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".