Screening of Mild Cognitive Impairment in Patients with Parkinson's Disease Using a Variational Mode Decomposition Based Deep-Learning
Bibliographic record
Abstract
Parkinson's disease (PD) which is the second most common neurodegenerative disease in the United States is challenging for specialists to diagnose and grade. Prior to the onset of motor symptoms of PD, patients exhibit alteration in sleep architecture which plays a critical role in consolidating memory, a key cognitive process of the brain. Standard spectral and signal analysis techniques have been recently introduced to exploit the changes in the electroencephalography of sleep related to PD or its cognitive complications including dementia. However, the use of artificial intelligence for the automated detection of the progression of PD to mild cognitive impairment (MCI) or dementia in sleep EEG have not yet been investigated. In this paper, we introduce a novel highly accurate variational mode decomposition based deep-learning framework applied on sleep electroencephalography signals in order to classify PD subjects into patients exhibiting normal cognition (NC) or MCI. The proposed framework is capable of detecting MCI at a significantly high 4-fold cross validation accuracy, sensitivity, specificity and quadratic weighted Kappa score of almost 99% offering a rapid and supportive tool for specialists to monitor the progression of PD and ensure the early initiation of efficient therapeutic treatments that will accordingly improve the quality of life for patients and their caregivers.
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 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 | 0.000 | 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 | 0.001 |
| 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 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".