A Low-Power Successive Approximation Algorithm for ECG Signals
Bibliographic record
Abstract
This paper presents a novel successive approximation (SA) algorithm designed for electrocardiogram (ECG) signals. Instead of digitizing each sample independently, the proposed method encodes the difference between consecutive samples, effectively leveraging the low activity nature of ECG signals. Unlike the conventional successive approximation register (SAR) ADCs, which require a fixed N comparisons for an N-bit conversion, the proposed approach dynamically adjusts the number of comparisons per sample, typically ranging from 2 to N. The algorithm was implemented in MATLAB and tested on ECG signals, demonstrating significant efficiency gains. Results show that the proposed method reduces the number of comparisons by $73.29 \%$ compared to the conventional SAR ADC method and by 13.81 % compared to the LSB-first algorithm, a popular scheme from the literature. Additionally, DAC update operations are minimized by the same percentages, leading to substantial power savings in both DAC and digital logic components of the SAR ADC design. These improvements make the method particularly wellsuited for low-power biomedical applications, especially batteryoperated ECG monitoring devices.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".