A new ECG feature extractor for biometric recognition
Bibliographic record
Abstract
In this paper, a new wavelet based framework is developed and evaluated for automatic analysis of single lead electrocardiogram (ECG) for application in human recognition. The proposed system utilizes a robust preprocessing stage that enables it to handle noise and outliers so that it is directly applied on the raw ECG signal. Moreover, it is capable of handling ECGs regardless of the heart rate (HR) which renders making presumptions on the individual's stress level unnecessary. One of the novelties of this paper is the design of personalized heartbeat template so that the gallery set consists of only one heartbeart per subject. This substantial reduction of the gallery size, decreases the storage requirements of the system significantly. Furthermore, the classification process is speeded up by eliminating the need for dimensionality reduction techniques such as PCA or LDA. Experimental results for identification over PTB and MIT healthy ECG databases indicate a robust subject identification rate of 99.61% using only 2 heartbeats in average for each individual.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".