Energy Saving on Constrained 12-Leads Real-Time ECG Monitoring
Bibliographic record
Abstract
Continuous real-time electrocardiogram (ECG) monitoring can detect arrhythmia and provide early warning for heart attacks. Effective power management signals and controlling the mode of operation to reduce the need for full fidelity ECG signal. This work studies the impact of the base-delta compression technique for different cardiac conditions on power consummation. It also aims to evaluate operational strategies and their effect on the battery life when the ECG patch can switch between different operating modes (e.g., varying the number of leads according to the cardiac conditions). We use a binary classifier to inform the decision of switching between different operational strategies. Both scenarios are evaluated in terms of execution time, Bluetooth Low Energy (BLE) communication airtime, power consumption, and energy-saving ratios on a Texas Instruments CC2650 Micro-controller Unit (MCU). We compare the performance of the base-delta compression and changing the mode of operation scenarios on various cardiac abnormalities. Performance evaluation shows that operational strategies outperforms data compression in power saving for normal ECG readings by a double fold. In contrast, operational strategies incurs an additional overhead of 1011 ms during an abnormal status. However, base-delta satisfies the embedded platform constraints on execution time and airtime with 25 ms and 20 ms, respectively in the MCU environment.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".