Analysis and Classification of Swallowing Sounds Using Reconstructed Phase Space Features
Bibliographic record
Abstract
The paper presents a quantitative analysis of swallowing sounds in normal and dysphagic subjects based on nonlinear dynamics metric tools. In addition, an automated method is proposed to identify patients at risk of dysphagia. Multidimensional phase space representation of the swallowing sound was reconstructed using Takens method of delays. Rosenstein and false nearest neighbor (FNN) methods were employed to evaluate the optimum time delay and proper embedding dimension, respectively. A Grassberger-Procaccia algorithm was utilized to calculate the correlation dimension as a measure of the complexity of the reconstructed attractor. The analysis demonstrated the low-dimensional dynamic characteristics of normal and dysphagic swallowing sounds. The optimum time delay and correlation dimension of the opening and transmission phases of swallowing sounds were used as features for a 3-nearest neighbor classifier to identify individuals at risk of dysphagia. The method was applied to tracheal sound recordings of 15 healthy subjects and 11 patients with some degree of dysphagia. The algorithm was able to classify 83% of swallows correctly. Finally, a screening algorithm was used which correctly classified 24 out of 26 subjects. This study suggests that nonlinear analysis is a promising tool for quantitative analysis of swallowing sounds and swallowing disorders.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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 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".