Generalizable electroencephalographic classification of Parkinson's disease using deep learning
Bibliographic record
Abstract
Abstract There is growing interest in using electroencephalography (EEG) and deep learning (DL) to aid in the diagnosis of neurological conditions like Parkinson’s Disease (PD). Many existing DL approaches to classify PD from EEG data cite performance metrics in the high 90% accuracies, but may be grossly overestimating their real-word capabilities due to information-leakage between training and testing data. Our aim was to characterize the potential of deep learning for classifying PD using a conservative training approach with unseen external testing data. We used publicly available resting-state EEG data from patients with PD from two seperate centers (University of New Mexico (n = 54) and University of Iowa (n = 28)) for our training and testing sets, respectively. We implemented a channelwise convolutional neural network and tuned it using a subjectwise cross validation approach. We found that an approach commonly cited in the literature overestimated performance in excess of 20%, while our pipeline more conservatively estimated performance by epoch (accuracy: 69.2%; sensitivity: 66.5%; specificity: 72.2%) and by subject (accuracy: 77.4%, sensitivity: 76.9%, specificity: 77.8%). Moreover, we show that our model generalized well to an unseen and external testing dataset without degradation in performance by epoch (accuracy: 77.2; sensitivity: 83.5%; specificity: 71.0%) and by subject (accuracy: 83.8%, sensitivity: 88.6%, specificity: 79.0%). These results highlight the effect of information leakage and serve as a new benchmark for future generalization of DL approaches to classify PD using EEG data.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 0.001 |
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".