Efficient Wavelet Thresholding and Wiener Filtering Association Incorporating a Median Filter Smoother Followed by R-Peaks Recovery for ECG Denoising
Bibliographic record
Abstract
The accurate denoising of acquired electrocardiogram (ECG) signals is a critical preprocessing step in data acquisition for both medical professionals and expert systems to make reliable assessments of cardiac health.In this study, we present an advanced denoising algorithm designed to mitigate the effects of additive white Gaussian noise (AWGN), which is known for its capacity to disrupt the entire frequency band within a signal.Our approach offers a novel integration of wavelet transform and Wiener filtering techniques.The proposed algorithm comprises a single-level discrete wavelet transform (DWT) decomposition followed by hard thresholding of the detail wavelet coefficients and the application of wavelet-domain Wiener filtering to the approximation coefficients.Subsequently, the inverse DWT is employed to generate an initial stage denoised signal.To further improve signal restoration quality, a median filter is utilized.Lastly, to recover Rpeaks affected during the previous stage, each R-peak and its adjacent samples are replaced with those from the denoised signal before median filtering.We compared the performance of our technique with three state-of-the-art methods and found that it is highly competitive with the recently published DWT-SBWT method.Our approach also significantly outperforms both the reference wavelet-thresholding technique and the GS-WT strategy, with gains of more than 1.5 dB in most cases of utilized input SNR levels.These findings demonstrate the efficacy of our proposed algorithm in reducing AWGN interference, enabling more accurate evaluations of human cardiac health.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".