Application of fractal dimension on vestibular response signals for diagnosis of Parkinson's disease
Bibliographic record
Abstract
In this paper, a novel method based on analysis of dynamic response of vestibular system for diagnosis of Parkinson's Disease (PD) is introduced. Electrovestibulography (EVestG) signals are recorded from the ear canal in response to a vestibular stimulus. EVestG signals are in fact the vestibular response modulated by more cortical brain signals. We used EVestG data of 20 patients with PD and 26 age-matched healthy controls recorded in a previous study. We calculated the Katz Fractal Dimension (FD) of the extracted timing signal of firings during contralateral and ipsilateral stimuli of both left and right ear. We used multivariate analysis of variance (MANOVA) to select pairs of features showing the most significant differences between the groups. Then, Linear and Quadratic Discriminant (LDA, QDA) classification algorithms were applied on the selected features. The results have shown above 77.27% accuracy. Given the small population of the subjects and the patients were at different stage of disease, the results encourage continuing exploration of the application of EVestG for PD diagnosis and perhaps as a quick and non-invasive screening tool.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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".