A new ECG feature extractor for biometric recognition
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, 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.
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 | 0.001 |
| 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