A Novel Sensor-Array System for Contactless Electrocardiogram Acquisition
Bibliographic record
Abstract
The cardiac ECG is one of the most important human biometrics. An electrocardiogram (ECG) or EKG, captures the electrical activity of the heart and allows a healthcare professional to evaluate, diagnose, and monitor patient cardiac condition. The standard method to capture electrocardiogram signals (ECG) involves skin preparation and attachment of wet electrodes to the skin, which is not comfortable for the patient and requires a trained technician. In this work, a novel contactless-based ECG system is proposed, where 128 sensors are deployed on a mattress to capture the ECG information from the back of the patient. The proposed system can capture the ECG through clothing and is more comfortable to the patients. The measurements captured by the proposed system provides a 100% accuracy of QRS complex detection and heartbeat rate estimation and a maximum of 4% error in other major ECG features compared to a hospital-grade standard system. This paper shows that ECG features can be accurately extracted from contactless electrodes, through clothing and from the back of the patient.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".