Comparative analysis of the performance of the time-frequency distributions with knee joint vibroarthrographic signals
Bibliographic record
Abstract
Vibroarthrographic (VAG) signals emitted by human knee joints can be used to develop a non-invasive diagnostic tool to detect articular cartilage degeneration. VAG signals are nonstationary and multicomponent in nature; time-frequency distributions (TFDs) provide powerful means to analyze such signals. The objective of this paper is to determine the TFD suitable for identification and extraction of VAG signal features of clinical significance. The TFDs considered are: autoregressive (AR) model-based TFD; the reassigned, smoothed, pseudo-Wigner-Ville (RSPWV) distribution; and a TFD based on signal decomposition using the matching pursuit (MP) algorithm. As the true TFD of a VAG signal is not known, the results of the TFDs were compared based on the expected characteristics using synthetic signals. The MP TFD shows good potential in analyzing multicomponent signals with low signal-to-noise ratio when compared to the AR model-based TFD and the RSPWV method. The TFD techniques were also tested on VAG signals with additional information provided by auscultation and arthroscopy. The results indicate that the MP TFD is the best available TFD to analyze VAG signals.
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How this classification was reachedexpand
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 | 0.002 |
| 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) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".