Wavelet Decomposition for the Analysis of Esophageal Manometric Data in the Study of Gastroesophageal Reflux Disease
Bibliographic record
Abstract
Wavelet decomposition is gaining attention as a novel signal processing tool for analyzing nonlinear time-series. Compared to traditional Fourier transform, wavelet transform better represents functions exhibiting discontinuities and sudden changes. As such, wavelet-based techniques are strong candidates for the analysis of bio-signals (e.g. gastric and esophageal signals), in which, sudden changes and sharp peaks are likely. For the first time, this paper applies wavelet decomposition to the analysis of esophageal manometric data, which is critical in the diagnosis of gastroesophageal reflux disease. Simulation results of wavelet decomposition are compared with those of a recent approach based on empirical mode decomposition. Such comparison shows that wavelet decomposition leads to better results in terms of number of decomposition coefficients (15 versus 17), CPU-time (0.5 s versus 75 s), and signal-to-background ratio (0.97 versus 0.85).
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".