A Hybrid Deep Spatiotemporal Attention‐Based Model for Parkinson's Disease Diagnosis Using Resting State <scp>EEG</scp> Signals
Bibliographic record
Abstract
ABSTRACT Parkinson's disease (PD), a severe and progressive neurological illness, affects millions of individuals worldwide. For effective treatment and management of PD, an accurate and early diagnosis is crucial. This study presents a deep learning‐based model for the diagnosis of PD using a resting state electroencephalogram (EEG) signal. The objective of the study is to develop an automated model that can extract complex hidden nonlinear features from EEG and demonstrate its generalizability on unseen data. The model is designed using a hybrid model, consisting of a convolutional neural network (CNN), bidirectional gated recurrent unit (Bi‐GRU), and attention mechanism. The proposed method is evaluated on three public datasets (UC San Diego, PRED‐CT, and University of Iowa [UI] dataset), with one dataset used for training and the other two for evaluation. The proposed model demonstrated remarkable performance, attaining high accuracy scores of 99.4%, 84%, and 73.2% using UC San Diego, PRED‐CT, and UI datasets, respectively. These results justify the effectiveness and robustness of the proposed model across diverse datasets, highlighting its potential for versatile applications in data analysis and prediction tasks. Our proposed hybrid spatiotemporal attention‐based model has been developed with 10‐fold cross‐validation (CV) for UC San Diego dataset and 10‐fold CV and leave‐one‐out cross‐validation (LOOCV) strategies for PRED‐CT and UI datasets. Our results indicate that the proposed PD detection system is accurate and robust. The developed prototype can be used for other neurodegenerative diseases such as Alzheimer's disease, Huntington's disease, and so forth.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".