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 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".