Ensemble Empirical Mode Decomposition and adaptive filtering for ECG signal enhancement
Why this work is in the frame
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Bibliographic record
Abstract
The morphologic analysis of electrocardiogram (ECG) signals, which are always contaminated by certain types of noise, is a very important standard for medical diagnosis of heart diseases and other pathological phenomena. In this paper a novel ECG enhancement method based on Ensemble Empirical Mode Decomposition (EEMD) and adaptive filtering is proposed to filter out Gaussian noise and contact noise contained in raw ECG signals. The reference signal of the adaptive filter is produced by the selective reconstruction of the decomposition results of EEMD. Real ECG signals from the MIT-BIH database are used to validate the performance of the proposed method. Conventional Empirical Mode Decomposition (EMD), EEMD, and EEMD-Adaptive (EEMDA) are tested to compare the filtering performance. The results of simulations show that ECG signals can be significantly enhanced by using the proposed method where the contact noise is eliminated while useful ECG features are kept. It is shown that the EEMDA method is better than other filtering methods in terms of filtering ECG noise.
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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