Efficient ECG Compression and QRS Detection for E-Health Applications
Why this work is in the frame
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Bibliographic record
Abstract
Current medical screening and diagnostic procedures have shifted toward recording longer electrocardiogram (ECG) signals, which have traditionally been processed on personal computers (PCs) with high-speed multi-core processors and efficient memory processing. Battery-driven devices are now more commonly used for the same purpose and thus exploring highly efficient, low-power alternatives for local ECG signal collection and processing is essential for efficient and convenient clinical use. Several ECG compression methods have been reported in the current literature with limited discussion on the performance of the compressed and the reconstructed ECG signals in terms of the QRS complex detection accuracy. This paper proposes and evaluates different compression methods based not only on the compression ratio (CR) and percentage root-mean-square difference (PRD), but also based on the accuracy of QRS detection. In this paper, we have developed a lossy method (Methods III) and compared them to the most current lossless and lossy ECG compression methods (Method I and Method II, respectively). The proposed lossy compression method (Method III) achieves CR of 4.5×, PRD of 0.53, as well as an overall sensitivity of 99.78% and positive predictivity of 99.92% are achieved (when coupled with an existing QRS detection algorithm) on the MIT-BIH Arrhythmia database and an overall sensitivity of 99.90% and positive predictivity of 99.84% on the QT database.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 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 it