Feature selection for swallowing sounds classification
Bibliographic record
Abstract
In recent years swallowing sounds analysis have received great attention for observing the abnormalities in swallowing mechanisms. In this paper a comprehensive set of features were extracted from time and frequency domains characteristics of the signals. 111 features were obtained from different parts of swallowing sounds including initial discrete sounds (IDS), bolus transmission sounds (BTS) and the entire swallowing sounds signal (WHL). Reducing the number of features and selecting a set of most important ones is a crucial step in sketching the signal characteristics, observing the signal variations in classification problems. Therefore, in this study features were examined thoroughly and arranged by maximizing the Mahalanobis distances between normal and dysphagic classes. The results indicate low- and high-frequency components represent the main characteristics of the signals for IDS segment of the swallowing sound, while the medium frequency components play the principal role for BTS segment. Different feature subsets with variable number of features were investigated for classifying normal and dysphagic swallowing sound signals. It was found that the overall performances of the feature subset extracted from WHL was superior to the results of the feature subsets extracted from IDS or BTS individually.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".