Respiratory and Motion Artefacts Removal from ICG Signal Using Denoising Techniques for Hemodynamic Parameters Monitoring
Bibliographic record
Abstract
The impedance cardiography (ICG) is a reliable, non-invasive method widely used in clinical practice for the measurement of a multitude of hemodynamic parameters for the diagnosis of cardiovascular disease and continuous monitoring.Signal processing field is necessary to eliminate noises as an artefact of respiration and movement, to extract features characteristics from ICG signals.This paper discusses the concept of wavelet denoising based on scale-dependent thresholding, which is used in two types of the orthogonal wavelet family: Daubechies wavelets (db) and Symlet (sym) applied to the ICG.The study is based on wavelet coefficients that are thresholded using Sureshrink, NeighBlock, and classical thresholds such as Rigrsure and Sqtwolog; they are all compared with linear filters as well as with the LMS-based adaptive filtering algorithm already implemented in biosignal denoising.The results of the evaluation of the performance parameters show that the best denoising technique that gives good results in noise reduction is that of sym8 wavelets at level 5, and the most optimal thresholding technique is the Rigrsure technique with a mean error rate (MER) equal to 0.0001%.The proposed method has shown the reliability of results that can help us later to extract precisely significant information to diagnose earlier and monitor cardiovascular disorders.
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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) | 0.000 | 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 | 0.001 | 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".