A Successive Approximation Algorithm with Machine Learning for ECG Signals
Bibliographic record
Abstract
This paper proposes a new approximation algorithm that digitizes the estimation error of second-order difference of signal samples rather than digitizing the individual samples or their first and second order differences. This new method allows the number of comparisons needed to convert a signal sample into digital numbers, which is usually fixed at N in a conventional successive-approximation-register (SAR) analog-to-digital converter (ADC), to fall between 2 and N for almost all the signal samples in an N-bit ADC. With the implementation of machine learning to estimate the second-order difference of samples, this method is tested on electrocardiogram (ECG) signals in MATLAB. The results indicate a reduction of up to 60.41% in comparison to the regular SAR ADC, and an 8.8% or more decrease in comparison to other state-of-the-art techniques. On top of that, it also reduces the digital to analog converter (DAC) switching energy as well as the energy consumption of the SAR ADC digital portion.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".