Wireless ECG systems with New Sampling-rate Approach Based on Compressed Sensing Theory
Bibliographic record
Abstract
The main drawback of current ECG systems is the location-specific nature of the system due to the use of fixed/wired systems. The currently ECG systems are also restricted by size, patient’s mobility, power, and transmission capacity. Therefore, the currently ECG systems need to be further developed in order to achieve extended mobility and wireless monitoring of several patients at the same time. The wireless ECG systems provide vital information about the heart to physicians and doctors at anytime and anywhere by removing constraints of time and location of patients while increasing both the mobility and the quality of healthcare systems. With this in mind, Compressed Sensing (CS) procedure and the collaboration of Block Sparse Bayesian Learning (BSBL) framework is used to provide a robust low sampling-rate approach for wireless ECG systems. Advanced wireless ECG systems based on our approach will be able to deliver healthcare not only to patients in hospital and medical centers; but also in their homes and workplaces thus offering cost saving, and improving the quality of life. Our simulation results illustrate 15% reduction of Percentage Root- mean-square Difference (PRD) for a selected recode of ECG signals. The simulation results also show that sampling-rate can be minimized to 35% of nyquist-rate. Index Terms- Sampling-rate, Signal-to-noise ratio, Wireless ECG Systems, Compressed Sensing, Block Sparse Bayesian learning
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".