Silent aspiration detection by breath and swallowing sound analysis
Bibliographic record
Abstract
Detecting aspiration after swallows (the entry of bolus into trachea) is often a difficult task particularly when the patient does not cough; those are called silent aspiration. In this study, the application of acoustical analysis in detecting silent aspiration is investigated. We recorded the swallowing and the breath sounds of 10 individuals with swallowing disorders, who demonstrated silent aspiration during the fiberoptic endoscopic evaluation of swallowing (FEES) assessment. We analyzed the power spectral density (PSD) of the breath sound signals following each swallow; the PSD showed higher magnitude at low frequencies for the breath sounds following an aspiration. Therefore, we divided the frequency range below 300 Hz into 3 sub-bands, over which we calculated the average power as the characteristic features for the classification purpose. Then, the fuzzy k-means unsupervised classification method was deployed to find the two clusters in the data set: the aspirated and non-aspirated groups. The results were evaluated using the FEES assessments provided by the speech language pathologists. The results show 82.3% accuracy in detecting swallows with silent aspiration. Although the proposed method should be verified on a larger dataset, the results are promising for the use of acoustical analysis as a clinical tool to detect silent aspiration.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".