XBeats: A Real-Time Electrocardiogram Monitoring and Analysis System
Why this work is in the frame
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Bibliographic record
Abstract
This work presents XBeats, a novel platform for real-time electrocardiogram monitoring and analysis that uses edge computing and machine learning for early anomaly detection. The platform encompasses a data acquisition ECG patch with 12 leads to collect heart signals, perform on-chip processing, and transmit the data to healthcare providers in real-time for further analysis. The ECG patch provides a dynamically configurable selection of the active ECG leads that could be transmitted to the backend monitoring system. The selection ranges from a single ECG lead to a complete 12-lead ECG testing configuration. XBeats implements a lightweight binary classifier for early anomaly detection to reduce the time to action should abnormal heart conditions occur. This initial detection phase is performed on the edge (i.e., the device paired with the patch) and alerts can be configured to notify designated healthcare providers. Further deep analysis can be performed on the full fidelity 12-lead data sent to the backend. A fully functional prototype of the XBeats has been implemented to demonstrate the feasibly and usability of the proposed system. Performance evaluation shows that XBeats can achieve up to 95.30% detection accuracy for abnormal conditions, while maintaining a high data acquisition rate of up to 441 samples per second. Moreover, the analytical results of the energy consumption profile show that the ECG patch provides up to 37 h of continuous 12-lead ECG streaming.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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 it