Signal enhancement of wearable ECG monitoring sensors based on Ensemble Empirical Mode Decomposition
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Bibliographic record
Abstract
The use of electrocardiogram (ECG) signals is an important standard for the diagnosis of heart diseases and other pathological phenomena. The ECG signal, however, is always contaminated by different types of noise, especially when the sensor is worn by patients during their normal activities, where the muscle and motion artefact are the dominant noise. This paper proposes a novel ECG enhancement method, which is based on Ensemble Empirical Mode Decomposition, to eliminate the contact noise in the signals. The performance of the proposed method is validated by using real data from the MIT-BIH database. Simulation results show that ECG signals from wearable monitoring sensors can be significantly enhanced by filtering out the contact noise while keeping all of the ECG features. The EEMD-based method exhibits obvious advantages over other similar ones in terms of de-noising.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 it