The use of spontaneous vestibular response for diagnosis of meniere’s disease
Bibliographic record
Abstract
Meniere's disease is a common inner ear disorder that affects balance and hearing. Electrovestibulography (EVestG) is a relatively new vestibular driven test that measures spontaneous and driven field potential activity recorded in the external ear canal in response to various vestibular stimuli. The main objectives of this thesis were to record and analyze EVestG signals in order to 1) testify whether the EVestG technology is capable of classifying individuals with Meniere’s from healthy ones, and if it is, then 2) identify the EVestG tilt stimulus providing the most informative response in relation to identifying Meniere’s symptoms; thus, optimizing the EVestG experimental protocol as a Meniere’s disease diagnostic aid. EVestG signals of two groups of Meniere’s and control individuals during seven different EVestG tilt stimuli were recorded and analyzed by linear and nonlinear signal processing techniques. Data of 14 with Meniere’s disease and 16 healthy individuals were used as the training set, while additional data of 21 individuals with vertiginous disorders (and suspected of Meniere’s disease) and 10 controls were used as the test set. An ad-hoc voting classifier built upon single-feature linear classifiers was designed, and used for classification of the two groups of both training and test datasets. The results showed an overall accuracy of 87% and 84% for training and test datasets, respectively. Among the seven different tilts that each evokes a specific part of the inner ear organ, the side tilt which stimulates most of the labyrinth and particularly the utricle, was found to generate the best characteristic features for identifying Meniere’s disease from controls. Thus, one may simplify the EVestG protocol to only the side tilt stimulus for a quick screening of Meniere’s disease. The proposed method encourages the use of EVestG technology as a non-invasive and potentially reliable diagnostic/screening tool to aid clinical diagnosis of Meniere’s diseases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.000 | 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 teacher head, 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".