Time-Frequency Analysis and Hermite Projection Method Applied to Swallowing Accelerometry Signals
Why this work is in the frame
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Bibliographic record
Abstract
Fast Hermite projections have been often used in image-processing procedures such as image database retrieval, projection filtering, and texture analysis. In this paper, we propose an innovative approach for the analysis of one-dimensional biomedical signals that combines the Hermite projection method with time-frequency analysis. In particular, we propose a two-step approach to characterize vibrations of various origins in swallowing accelerometry signals. First, by using time-frequency analysis we obtain the energy distribution of signal frequency content in time. Second, by using fast Hermite projections we characterize whether the analyzed time-frequency regions are associated with swallowing or other phenomena (vocalization, noise, bursts, etc.). The numerical analysis of the proposed scheme clearly shows that by using a few Hermite functions, vibrations of various origins are distinguishable. These results will be the basis for further analysis of swallowing accelerometry to detect swallowing difficulties.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 it