Integrating Analytic and Appearance Attributes for Human Identification from ECG Signals
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, we investigate identification of human subjects from electrocardiogram (ECG) signals. We segment the ECG records into individual heartbeat based on the localization of R wave peaks. Two types of features, namely analytic and appearance features, are extracted to represent the characteristics of heartbeat signal of different subjects. Feature selection is performed to find out significant attributes. We compared the performance of different classification algorithms. To better utilize the advantages of different types of features, we proposed two schemes for data fusion and classification. Our system achieves promising results with 100% correct human identification rate and 98.90% accuracy for heartbeat identification. The proposed framework reveals the potential of employing appearance based analysis in ECG signal, yet demonstrates the advantage of a hierarchical architecture in pattern recognition problems.
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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 | 0.000 |
| 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