Analysis of cough sound measurements including COVID-19 positive cases: A machine learning characterization
Bibliographic record
Abstract
Remote monitoring and measurement are valuable tools for medical applications and they are particularly important in the context of pandemic outbreaks, like the current COVID-19. This paper presents an analysis of sound measurements of cough events from the point of view of their predictive content with respect to identification of different types of cough, including positive COVID-19 cases. The data consisted of a collection of audio samples collected from different sources including dry, wet, whooping and COVID-19 coughs. Unsupervised and supervised machine learning techniques were used to reveal the underlying structure of the data, described by dissimilarity spaces constructed from pair-wise dynamic time warping measures derived from the original sound measurements. Intrinsic dimensionality, nonlinear mappings to low-dimensional spaces and visual cluster assessment techniques allowed a representation of the cough types distribution. Supervised classification techniques were used to obtain models identifying cough classes and high performance classifiers were obtained for most of them, including COVID-19. These results are preliminary and there is potential to improve, as they were obtained directly from a small dataset, without signal preprocessing (trimming, filtering, etc.), hyperparameter tuning, ensemble models, and class imbalance handling approaches.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".