MAXimum Feasible Subsystem Recovery of Compressed ECG Signals
Bibliographic record
Abstract
Electrocardiograph (ECG) signals are recorded continuously to monitor the health of potential cardiovascular disease (CVD) patients, leading to large amounts of data. An efficient way to acquire and compress signals would reduce bandwidth requirements for transmission and reduce memory and power requirements at the monitoring device. Compressive Sensing (CS) is an efficient method for ECG compression. However, the existing CS sparse recovery algorithms have small critical sparsity, which means that acceptable signal recovery requires many measurements. In this paper, two MAXimum Feasible Subsystem (MAX-FS)-based recovery algorithms that have shown good performance in speech compression are investigated for recovery of compressed ECG signals from the MIT-BIH Arrhythmia database. The two MAX-FS-based methods provide better recovery of compressed ECG signals than conventional recovery algorithms such as Smoothed ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</inf> Norm (SL0) and Basis Pursuit (BP) with almost 47.5% and 30% reduction in the required number of measurements, respectively.
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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".