Analysis of click-evoked otoacoustic emissions by concentration of frequency and time: Preliminary results from normal hearing and Ménière’s disease ears
Bibliographic record
Abstract
The presence of click-evoked (CE) otoacoustic emissions (OAEs) has been clinically accepted as an indicator of normal cochlear processing of sounds. For treatment and diagnostic purposes, however, clinicians do not typically pay attention to the detailed spectrum and waveform of CEOAEs. A possible reason is due to the lack of noise-robust signal processing tools to estimate physiologically meaningful time-frequency properties of CEOAEs, such as the latency of spectral components. In this on-going study, we applied a modern tool called concentration of frequency and time (ConceFT, [1]) to analyze CEOAE waveforms. Randomly combined orthogonal functions are used as windowing functions for time-frequency analysis. The resulting spectrograms are subject to nonlinear time-frequency reassignment so as to enhance the concentration of time-varying sinusoidal components. The results after reassignment could be further averaged across the random choice of windows. CEOAE waveforms are acquired by a linear averaging paradigm, and longitudinal data are currently being collected from patients with Ménière’s disease (MD) and a control group of normal hearing subjects. When CEOAE is present, the ConceFT plots show traces of decreasing but fluctuating instantaneous frequency against time. For comparison purposes, same processing methods are also applied to analyze CEOAE data from cochlear mechanics simulation.
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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 | 0.002 |
| 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 | 0.001 |
| 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".