Advanced Low-Pass Filters for Signal Processing: A Comparative Study on Gaussian, Mittag-Leffler, and Savitzky-Golay Filters
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Bibliographic record
Abstract
Signal processing plays a crucial role in biomedical applications, facilitating accurate health monitoring and clinical diagnoses.This study presents a comparative analysis of Gaussian, Mittag-Leffler, and Savitzky-Golay filters, evaluating their effectiveness in noise reduction and signal enhancement for electrocardiogram (ECG) signals.These filters offer adjustable parameters, making them adaptable to various applications.Our findings demonstrate that the Savitzky-Golay smoothing filter outperforms the others in smoothing data and computing derivatives of noisy data, despite its limitations in suppressing noise at higher frequencies.On the other hand, the adaptive Gaussian and Mittag-Leffler filters excel in noise reduction but may compromise fine signal details.Through MATLAB simulations and mean squared error (MSE) comparisons as well as Signal to Nosie Ratio (SNR), we evaluate the filters' performance in denoising realworld ECG signals.The results indicate that both the Savitzky-Golay smoothing and Mittag-Leffler filters hold promise for noise reducing in other biomedical signals, such as medical EEG and medical EMG signals.This research serves as a foundational exploration of the application and enhancement of these filters in biomedical signal processing.
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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 | 0.000 | 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 | 0.001 | 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 it