Single-lead ECG Compression for Connected Healthcare Applications
Bibliographic record
Abstract
Preventive healthcare is achievable through physiological long-term remote monitoring. In connected healthcare, wearables that can collect physiological signals such as electrocardiograms (ECG) and electroencephalogram (EEG) can help improve health outcomes in society. For single-lead ECG devices, there are still limitations for this role that includes short time continuous operability and uncomfortable sensors worn by the user making it non-appealing for uninterrupted remote monitoring. However, with the current advances in microelectronics, embedded systems, sensors, and Internet of Medical Things (IoMT), long-term monitoring is realizable. A decrease to the overall power consumption of the wearable leads to an increase in device longevity while dry ECG electrodes can be used to increase user comfort. This work proposes a lossless LempelZiv Welch (LZW) compression algorithm used to compress and optimize the raw ECG data obtained from a 3D printed dry electrode based single-lead ECG device. This approach utilizes the ECG's inherent waveform characteristics. The single-lead ECG's R-peak and RR-intervals are used as one-bit information that are further compressed for shorter wireless transmission, leading to an increase in battery life and device operation. The algorithm showed a high compression ratio (CR) for 10 seconds, 30 seconds, 1-minute and 5-minute ECG signals where CR was 0.99, 0.91, 0.91, 0.92, respectively. For the 5-minute ECG signal, the size of data decreased from 225 Kbytes to 18.75 Kbytes while retaining R-peak and RR interval information for heart rate (HR) and heart rate variability (HRV) calculations. This work adds to the current progress in single-lead ECG in long-term continuous remote monitoring for connected healthcare applications.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".