Digital Earplug Featuring Combined Noise Dosimetry and Electrocochleography: a Proof of Concept.
Bibliographic record
Abstract
Electrocochleography (ECochG) is a promising electrophysiology measure that evaluates cochlear function and identifies different auditory disorders. ECochG measures inner ear potentials generated in response to acoustic simulation. Recent studies on cochlear synaptopathy have shown that ECochG can detect insults to auditory nerve fibers following noise exposure. These damages go undetected by routine audiology testing such as audiometry. Therefore, ECochG could be used to identify auditory damage during excessive noise exposure in a noisy workplace, and thus, ultimately help to prevent noise-induced hearing loss. To this aim, a dedicated electronic earpiece has been designed. It features passive hearing protection and can continuously monitor ECochG during a wearer’ shift. A pair of such earpiece is wired to a dedicated hardware device, dubbed eCoGeers, with a microcontroller and several analog-to-digital converters for ECochG potentials and audio signals processing. The synchronization aspect between both data acquisitions is paramount for accurate noise-exposure and inner ear integrity monitoring. To validate the prototype’s capabilities, tests have been conducted by sending a known ECochG stimulus as an electrical input to the earpiece device and by reading its low voltage on the prototype earpiece. Thus, to successfully extracting the original ECochG stimulus from the electrical noise floor. This research paves the way for a future device that could monitor noise exposure and auditory damage during a work shift. This device could ultimately warn the wearer when a significant damage has been detected and contribute to better noise-induced hearing loss prevention programs in the workplace.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".