Assessing the Effectiveness of Various Filtering Techniques on Seismocardiography Signals in Individuals with Valvular Heart Disease
Bibliographic record
Abstract
Many factors, such as excessive noise and artifacts, contribute to the low-quality standards commonly encountered while interpreting Seismocardiography (SCG) signals.In this work, different types of digital filters are used to process SCG signals, and their performance is assessed in the study.Among the filters investigated were the multistage filters: the Butterworth filter (BF), Chebyshev filter (Cheby), wavelet transform (WT), Principal Component Analysis (PCA), Independent Component Analysis (ICA), Empirical Mode Decomposition (EMD), Variable Mode Decomposition (VMD), and Continuous Wavelet Transform (CWT) methods were also analysed.Performance evaluation was based on performance metrics such as Signal-to-noise Ratio (SNR), Peak Signal-to-Noise Ratio (PSNR), Peak Relative Difference (PRD), Structural Similarity Index (SSIM), and mean square error (MSE).The experimental results highlight the advantages and limitations of each filter technique.A thorough assessment of these techniques in SCG signal processing is provided.The study highlights which filters can be used effectively to obtain significant information from the SCG signals which will contribute and assist the future studies and applications.
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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 | 0.001 | 0.002 |
| 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".