An Unbiased Linear Adaptive Filter with Normalized Coefficients for the Removal of Noise in Electrocardiographic Signals
Bibliographic record
Abstract
The authors propose an unbiased linear adaptive filter (ULAF) to eliminate high-frequency random noise in electrocardiographic (ECG) signals. The ULAF does not contain a bias in its summation unit, and the filter coefficients are normalized. During the adaptation process, the normalized coefficients are updated with the steepest-descent algorithm in order to achieve efficient filtering of noisy ECG signals. The authors tested the ULAF with ECG signals recorded from 16 subjects, and compared the performance of the ULAF with that of the least-mean-square (LMS) and recursive-least-squares (RLS) adaptive filters. The filtering performance was quantified in terms of the root-mean-squared error (RMSE), normalized correlation coefficient (NCC), and filtered noise entropy (FNE). A template derived from each ECG signal was used as the reference to compute the measures of filtering performance. The results indicated that the ULAF was able to provided noise-free ECG signals with an average RMSE of 0.0287, which was lower than the second best RMSE (0.0365) obtained with the LMS filter. With respect to waveform fidelity, the proposed ULAF provided the highest average NCC (0.9964) among the three filters studied. In addition, the ULAF effectively removed more noise measured by FNE versus the LMS and RLS filters in most of the ECG signals tested.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".