Eliminate Artifact on ECG Recording Using the Soft Threshold Setting on Wavelet Coefficients at Independent Components of ICA
Bibliographic record
Abstract
A common problem in ECG signal acquisition is the removal of artifacts and undesirable components to obtain a clean ECG signal, which helps to increase the accuracy of the clinical diagnosis process.However, with expecting received ECG in the high accuracy, the basic filters are not enough, because the ECG signal recording's often affected from differential sources with varying amplitudes and frequencies; furthermore, the recording process needs to be implemented via electrodes on the skin, which not only record the electrical activity of the heart, but also many other participating components such as Respiratory, Electroencephalogram (EEG), electrooculography (EOG), electromyography (EMG) with many artifact from outside.Therefore, conventional filters didn't meet the requirements of removing most of the impacting artifact components.In this study, the author has proposed a new method, that is to apply the independent component analysis (ICA) -combining wavelet transforms on each independent components to remove the abnormal noise, especially EMG to improve the accuracy of ECG signal recording with a correlation value of up to 0.971 compared to the desired.
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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 | 0.001 | 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".