Bibliographic record
Abstract
This study examines the automated detection of swallowing sounds for normal subjects. A normal swallowing sound is characterized by three phases including oral, pharyngeal and esophageal where complications lead to swallowing disorder or dysphagia. Current gold standard testing for this abnormality is videofluorography, an x-ray based procedure with detrimental radiation side effects. New non-invasive techniques are necessarily explored to help assess the performance of the swallowing mechanism. Recent developed studies in acoustical airflow estimation indicate the need to detect and extract swallowing segments from sound signals. Extraction is currently a manual process, both subjective and time-consuming. Thus, an automated, objective and quick method is developed in the form of a “smart” algorithm with the ability to make decisions like trained technicians and physicians. Three sound signal features were explored to assist in the classification process (AR- coefficients, RMS values and average power). Utilizing the features, classification sequences were produced for six healthy subjects swallowing sounds. The results were compared with known values (acquired through visual and auditory means). RMS features in combination with the “smart” code yield the lowest error, on average 20.7 ± 4.6%. Future studies include testing variations of smart algorithm code in order to create a robust algorithm. Also, future work includes varying test subject ages, test media (bolus textures) and creating a program-user interface for decision-making assistance. Keywords: swallowing, dysphagia, spectrogram, RMS, automated detection.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".