Noise Removal from ECG Signals by Adaptive Filter Based on Variable Step Size LMS Using Evolutionary Algorithms
Bibliographic record
Abstract
Nowadays, the electrocardiogram (ECG) signal is widely used to detect cardiovascular diseases. Several studies are conducted on noise removal of ECG signal based on the adaptive filter with least-mean Square (LMS) algorithm. In this paper, for improving the traditional LMS method, the evolutionary algorithms are used to select the variable optimal step size of LMS, causing the least error between the main and filtered ECG signals. The proposed Adaptive Noise Cancellation System (ANC) includes Wavelet Transform and IIR-Notch filter to reduce the baseline Wander and Power Line Interference noises. Afterward, an additive white noise generator unit is employed to evaluate the performance of the three adaptive models involving GA-LMS, PSO-LMS, and GA-PSO-LMS algorithms in terms of Signal to Noise Ratio (SNR) and Mean Square Error (MSE). Eventually, to evaluate the performance of the proposed models in terms of the MSE and SNR criteria, we conduct comprehensive experiments on the ECG records of the MIT -BIH database. The obtained results of variable step size, GA-LMS, PSO-LMS, and hybrid GA-PSO-LMS, demonstrate more efficiency in filtered signal compared to constant step size LMS. Besides, in most cases, the Hybrid GA-PSO-LMS method has superiority over two other proposed methods concerning the SNR and MSE criteria.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".