Gating of false identifications in electrocardiogram based biometric system
Bibliographic record
Abstract
A signal-to-noise ratio based false identification reduction system was proposed for an ECG based biometric system. The system generated a signal quality index (SQI) based on the 25 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> percentile of the signal-to-noise ratios of the individual beats within a 20 s segment of ECG data. Identifications generated from ECG segments with SQIs below a set threshold were gated. The system was tested using 642 ECG segments collected from 32 subjects while standing still and while jogging. With no gating the biometric system attained a precision of 0.49. Following the application of the gating system at a threshold of 1 dB, the precision increased to 0.68. The system eliminated 98.7% (155/157) of the false identification during the noise corrupted (jogging) interval while maintaining the count of the true identifications (2/2). During the clean (standing still) intervals, the system gated 57.8% (193/334) of the false identifications and 8.14% (25/307) of the true identifications.
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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 | 0.001 | 0.001 |
| 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".