Smoothing and Denoising ECG Signals Based on Modified Smoothing Spline and Discrete Wavelet
Bibliographic record
Abstract
Smoothing and denoising ECG signals are challenging in biomedical signal processing applications.This paper proposes a new smoothing and denoising ECG signal method based on a proposed modified smoothing spline (MSS) method and mean discrete wavelet (MDW).The traditional smoothing spline (TSS) method uses a certain smoothing parameter value for overall samples of the noisy signal with moderate performance.On the other hand, the proposed modified version of the smoothing spline method is based on utilizing a range of smoothing parameter values for each sample instead of a single value.The smoothing parameter values are selected according to the MDW value to improve the method's smoothing and denoising performance.The method is evaluated using the MIT-BIH-arrhythmia (MBA) database, and the obtained results illustrate a high smoothing and denoising performance without losing signal information.The signal-to-noise ratio (SNR), the Mean Square Error (MSE), and the Percent Root Mean Square Difference (PRD) are the parameters used to evaluate the method's performance.Based on white Gaussian noise at 10 dB input SNR, the results are 8.14 dB improved SNR, 0.0015 MSE, and 12.68 PRD.The evaluation results for the proposed method show a high performance compared with the TSS and the existing methods.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".