Auditory Evoked Potential-Based Hearing Loss Level Recognition Using Fully Convolutional Neural Networks
Bibliographic record
Abstract
Hearing perception loss is the main common disabilities existing in adults confirmed by the auditory evoked potential exam (AEP). This technique is characterized by limited medical information from feedback response in full routine examination of patients. Body movements, measuring equipment, low-frequency noise are outside factors that cause a misinterpretation. In clinical workflow, AEP signals are manually classified by the experts in order to precise the hearing loss level. In order to enhancethe diagnosis rung rightness, the fully convolutional neural networks methodology is proposed to highlight reliably automated hearing loss analysis. The validation of the proposed approach was focused on 494 factual incorporated auditory loss cases and 177 seen normal undergoing different auditory stimuli (20 dB, 50, 60... and 80 dB) from AEP recordings. The used classification method can represent a highly reduced labor-intensive study loads of ear nose throat (ENT) doctor by applying the pertinent analysis strategy for each hearing loss level and significantly increase the auditory diagnosis performance which provides ability for a computerized ENT assessment. Compared to state-of-the-art methods, the used technique presents a higher accuracy rate by requiring hearing loss level classes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".