Enhanced binary patterns for electrocardiogram (ECG) biometrics
Why this work is in the frame
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Bibliographic record
Abstract
Most, if not all, binary patterns variants consider signals observations separately; hence, binary patterns variants ignore any relationship among observations. In this paper we proposed an algorithm that enhances binary patterns extraction to accommodate for temporal progression changes. The enhanced binary patterns feature extraction extracts a single feature vector that captures changes occurred to observations over time. This enhancement is crucial in cases where the examined signal is repetitive in nature, such as ECG signal. Enhanced binary patterns were examined for ECG biometric application on ECG database with 1,012 subjects. The enhanced binary patterns achieved an EER of 7.89% in comparison to 12.4% and 12.3% for non-enhanced binary patterns and a state of the art work. We also showed that enhanced binary patterns features are capable to extract discriminative ECG features that reduced EER by 15% when compared to time-domain raw samples.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 it