Time-Frequency Analysis of Clinical Percussion Signals Using Matrix Pencil Method
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Bibliographic record
Abstract
This paper discusses time-frequency analysis of clinical percussion signals produced by tapping over human chest or abdomen with a neurological hammer and recorded with an air microphone. The analysis of short, highly damped percussion signals using conventional time-frequency distributions (TFDs) meets certain difficulties, such as poor time-frequency localization, cross terms, and masking of the lower energy features by the higher energy ones. The above shortcomings lead to inaccurate and ambiguous representation of the signal behavior in the time-frequency plane. This work describes an attempt to construct a TF representation specifically tailored to clinical percussion signals to achieve better resolution of individual components corresponding to physical oscillation modes. Matrix Pencil Method (MPM) is used to decompose the signal into a set of exponentially damped sinusoids, which are then plotted in the time-frequency plane. Such representation provides better visualization of the signal structure than the commonly used frequency-amplitude plots and facilitates tracking subtle changes in the signal for diagnostic purposes. The performance of our approach has been verified on both ideal and real percussion signals. The MPM-based time-frequency analysis appears to be a better choice for clinical percussion signals than conventional TFDs, while its ability to visualize damping has immediate practical applications.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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