Exploring quantile graphs: A novel approach for classifying Parkinson’s disease-related events during the TUG test
Bibliographic record
Abstract
Abstract Parkinson’s disease (PD) is a neurological disorder that affects the central nervous system, resulting in the progressive impairment of movements, such as tremors, slow movements, muscle stiffness, imbalance, speech and writing disturbances, and deterioration of gait. Understanding the challenges associated with PD, including episodes of freezing, where individuals suddenly stop and struggle to resume rhythmic movements, has been a subject of study in the literature. The reliable detection of freezing before it occurs still lacks an effective mechanism, emphasizing the importance of understanding the dynamic changes in the brain related to both frozen moments and healthy functioning. Recent studies have focused on analyzing PD through techniques such as electroencephalography (EEG), which records the brain’s electrical activity and generates temporal series. However, this approach faces challenges, prompting the exploration of alternative techniques and interdisciplinary approaches to gather more comprehensive information about these signals. A promising approach is the use of Quantile Graphs (QG), which have shown potential in distinguishing patients with various medical conditions but have not yet been tested in PD patients. The QG method involves mapping temporal series into a network based on transition probabilities. In this context, this article aims to investigate the application of QG to quantify differences in brains affected by PD and pave the way for the application of this method to the disease. To achieve this initial goal, EEG data from 18 channels were collected from four PD patients, classifying three events of the patient during the Timed Up and Go (TUG) test (normal walking, Freezing of Gait, and voluntary stop). The findings confirmed the utility of QG in analyzing complex and nonlinear signals, such as those generated by EEG recordings in individuals with PD. Using QG, researchers can obtain valuable information about the intricate dynamics of brain activity related to PD. This approach holds promise for improving the understanding of PD and potentially aiding in the early detection and treatment of freezing episodes, ultimately enhancing the quality of life for individuals living with this challenging condition.
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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".