New sampling approach for wireless ECG systems with compressed sensing theory
Bibliographic record
Abstract
Wireless Body Area Networks (WBANs) consist of small intelligent biomedical wireless sensors attached on or implanted to the body to collect vital biomedical data such as electrocardiogram (ECG) signals to provide continuous health monitoring systems for diagnostic and therapeutic purposes. ECG signals are widely used in health care systems because they are noninvasive mechanisms to establish medical diagnosis of heart diseases. In order to fully exploit the benefits of WBANs to Electronic Health (EH), Mobile Health (MH), and Ambulatory Health Monitoring Systems (AHMS) the power consumption and sampling rate should be restricted to a minimum. With this in mind, Compressed Sensing (CS) procedure and the collaboration of Block Sparse Bayesian Learning (BSBL) framework is used to provide new sampling approach for wireless ECG systems with CS theory. 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 25% reduction of Percentage Root-mean-square Difference (PRD) and a good level of quality for Signal to Noise Ratio (SNR), sampling-rate, and power consumption.
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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".