Entropy‐based feature extraction and classification of vibroarthographic signal using complete ensemble empirical mode decomposition with adaptive noise
Why this work is in the frame
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Bibliographic record
Abstract
Non‐invasive methods accomplished by a computer aided diagnosis of knee‐joint disorders provide an effective tool. The objective of this study is to analyse vibroarthographic (VAG) signals using non‐linear signal processing technique. This study includes different entropy‐based feature extraction techniques to attain highly distinguishable features. The authors proposed to use a non‐linear method known as complete ensemble empirical mode decomposition with adaptive white noise to decompose the VAG signals into intrinsic mode functions (IMFs). Entropy‐based features involving approximate entropy, sample entropy, Shannon entropy, Rényi entropy, Tsallis entropy and permutation entropy (PeEn) are computed from dominant IMFs and reconstructed VAG signals. These extracted features are given as input to the least squares support vector machine as a classifier. The results illustrated that PeEn performed better with respect to other entropies. PeEn gives a classification accuracy of 86.61% and Matthews correlation coefficient of 0.7082. The computational complexity of entropies was also analysed. Results inferred that PeEn has a computational complexity of O ( N ) provided a simple, robust and low computational feature extraction technique. Analysis of VAG signals using non‐linear preprocessing and entropy‐based features can provide highly distinguishable features for accurate detection of knee‐joint disorders.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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