A Low-Power Non-Uniform Third-Derivative-Based Sampling Technique for ECG Applications
Bibliographic record
Abstract
In this work, we present an approach called third-derivative-based sampling (TDS), which is a non-uniform sampling technique that can be used in a variety of applications. Here, we will focus on applying the technique to electrocardiogram (ECG) signals. We also present a possible hardware implementation of the approach. The TDS method uses cubic spline interpolation for sampling the signal as well as signal reconstruction. As compared to other non-uniform sampling techniques, TDS offers an improved accuracy of the reconstructed signal and/or a reduction in the number of retained samples which enhances the compression factor (CF). Such improvements will also lead to an enhanced power efficiency in ECG monitoring systems. Users can control the associated reconstruction error by adjusting the maximum number of samples that can be disregarded (N) and/or the magnitude of the fourth derivative of the signal. The presented results demonstrate that the proposed method can offer up to 20% improvement in the CF as compared to the state-of-the-art techniques reported in the literature without compromising the reconstruction error. Alternatively, it can offer up to 53% enhancement in the accuracy of the reconstructed signal for a given CF. By integrating the proposed TDS block into an ECG data acquisition and processing system, the overall power consumption of the system can be reduced since only a small fraction of samples (proportional to CF) are kept and processed.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".