Person Identification using Electrocardiograms
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this paper, we demonstrate that the electrocardiogram (ECG) can be used as a biometric. While previous studies have shown the potential of an ECG biometric, this research demonstrates it under conditions that include intra-individual variations and a simple user interface, consisting of electrodes held on the pads of the subject's thumbs. ECG person identification was accomplished through quantitative comparisons of an unknown signal to enrolled signals. The quantitative comparisons were: the correlation coefficient and a wavelet distance measure. It was found that the combination of these two methods provided improved performance, relative to either individual method. ECG person identification accuracy was 90.8%. While this accuracy is relatively low compared to conventional biometrics, such as fingerprints, the ECG can be used as supplementary information for a multi-modal biometric system. A multi-modal system that includes the ECG would have increased accuracy and robustness, without necessarily requiring any change to the perceived user interface. At minimum, the ECG would be useful in providing liveness detection.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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