Evaluating the Audiological Testing Process Through Galvanic Skin Response Using a One-Dimensional Convolutional Neural Network
Bibliographic record
Abstract
The evaluation of audiometric tests, which assess an individual's ability to perceive various sounds and frequencies, is crucial for diagnosing and monitoring hearing loss.This study aims to evaluate the effects of the audiological testing process on individuals by classifying their galvanic skin response (GSR) with a one-dimensional convolutional neural network (1D-CNN).GSR, which reflects physiological changes due to psychological states such as stress and relaxation, was measured during audiological tests to distinguish between resting and active states.Various transformations of the GSR data were applied to the 1D-CNN input to determine the most effective method in classification.The results demonstrate that GSR data, when processed through 1D-CNN, can reliably reflect the physiological and emotional impacts of audiological testing on individuals.This approach provides a novel method for enhancing the understanding of the audiological test experience through objective physiological measures.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".