Multi-Grained Deep Cascade Learning for ECG Biometric Recognition
Bibliographic record
Abstract
Recently, electrocardiogram (ECG) biometric recognition has become an emerging area of interest in the identity identification technology.However, robust and accurate of ECG biometric recognition are challenging.Sparse representation-based classification (SRC) and deep neural networks (DNNs) have achieved significant success in biometric recognition, but there are some problems, and SRC-based learning methods are one-step models where the latent discriminative information cannot be fully exploited.DNNs are complex models and require large amounts of training data.The proposed method describes the design of a multi-grained deep cascade learning for ECG biometric recognition that addresses above problems.First, the global and local features are generated by principal components analysis (PCA) and the use of one-dimensional multi-resolution local binary pattern (1DMRLBP) method.Second, we obtain new features of class coding based on the sparse representation by multi-granularity scanning.Third, we propose an end-to-end deep cascade learning model without back-propagation to seek more discriminative information.This approach not only effectively solves the one-step model problem of sparse representation, but also effectively reduces the ECG signal noise.Extensive experiments are conducted on four ECG datasets.The results demonstrate that the proposed method can outperform other state-ofthe-art methods in terms of both accuracy and efficiency.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".