Exploring quantile graphs: A novel approach for classifying Parkinson’s disease-related events during the TUG test
Bibliographic record
Abstract
Parkinson’s disease (PD) is a neurological disorder that impacts the central nervous system, leading to a progressive decline in motor functions, including symptoms like tremors and episodes of freezing. An important area of research has been the challenges posed by these freezing episodes, during which individuals suddenly halt and struggle to resume coordinated movements. Recent studies have utilized electroencephalography (EEG) to analyze PD; however, this method faces certain limitations, prompting the investigation of alternative and interdisciplinary approaches to obtain a more comprehensive understanding of EEG signals. A promising methodology involves the use of Quantile Graphs (QGs), which have demonstrated potential in differentiating patients with various medical conditions. However, they have yet to be applied to those with PD. This article aims to explore the application of QGs for quantifying differences in the brains of individuals affected by PD, thereby paving the way for the integration of this method into PD research. To achieve this goal, EEG data were collected from 18 channels across six PD patients, capturing three distinct events during the Timed Up and Go (TUG) test: normal walking, freezing of gait (FoG), and voluntary stop. The combination of the QG method with machine learning techniques was effective in distinguishing between FoG and non-FoG EEG events. The findings validated the utility of QG in analyzing complex and nonlinear signals, such as those produced by EEG recordings in individuals with PD.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".