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 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 | 0.000 | 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".