Deterministic Compressed Domain Analysis of Multi-channel ECG Measurements
Bibliographic record
Abstract
Continuous and long term acquisition of multichannel ECG measurements are significant for diagnostic purposes. Compressive sensing has been proposed in the literature for obtaining continuous ECG measurements as it provides advantages including a reduced number of measurements, reduced power consumption and bandwidth for transmission. Reconstruction of the compressed ECG measurements is then done to analyze the measurements for diagnostic purposes. However, reconstruction of ECG measurements is computationally expensive. Therefore, in this paper, ECG analysis is carried out in the compressed domain without resorting to reconstruction. Multi-channel ECG measurements from MIT-BIH Arrhythmia database is used to validate the compressed domain ECG analysis. ECG signals are compressed at various compression ratios using morphology preserving deterministic sensing matrix. Structural similarity measures are used to quantitatively demonstrate the fidelity of the compressed measurements. R-peaks are detected in the compressed domain from compressed ECG measurements. Detection performance metrics such as sensitivity, positive predictivity and detection rate decrease as compression ratio increases.
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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 | 0.000 | 0.002 |
| 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) | 0.001 | 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".