Variance fractal dimension trajectory as a tool for hear sound localization in lung sounds recordings
Bibliographic record
Abstract
Methods by which to deduce information regarding respiratory mechanics via respiratory acoustics would present favorable additions to traditional pulmonological testing for determination of respiratory conditions. The combination of sounds in sound signals acquired on the chest wall, mainly those originating from pulmonary airflow and the heart, complicate the establishment of a firm definition of flow-specific lung sounds as a function of airway narrowing. In this study, the variance fractal dimension trajectory (VFDT) algorithm has been applied to lung sounds data to examine its use as a heart sounds locator regardless of pulmonary airflow. Lung sounds data was recorded from anterior-right chest locations of six healthy male and female subjects, aged 10-26 years, under three body-mass-standardized flow conditions (low flow: 7.5 mL/s/kg, medium flow: 15 mL/s/kg, high flow: 22.5 mL/s/kg). Suitable window sizes and increment values (chosen based on signal characteristics) were tested, and results are presented for optimal parameters in terms of number of successful heart sound detections. The results show that the VFDT is most successful for heart sound localization at low and medium flow, which are also of most interest. Overall, the method shows promise as a viable technique for this purpose.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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".