Parkinson’s disease diagnosis through electroencephalographic signal processing and neural network classification
Bibliographic record
Abstract
Parkinson's disease (PD) is the second most prevalent neurological disorder, following Alzheimer's. Despite this, there is currently no successful treatment for PD. Therefore, early detection of Parkinson's disease is crucial for preventing its progression. To address this, a computer-aided diagnosis system has been implemented to identify any abnormalities. Significant research has been conducted using speech and gait analysis. However, there is growing interest in using electroencephalographic (EEG) signals to diagnose Parkinson's disease at an early stage. This paper aims to use EEG to capture neural correlates of dysfunction in PD patients and compare with the normal ones to determine whether a person has PD. The method is to preprocess the EEG dataset using MATLAB and EEGLAB and to analyze and classify the preprocessed data using MLP neural networks, which has good expressiveness and adaptability. Our dataset contains 25 sets of data with 11 healthy people and 14 Parkinson's Disease patients. Experiments show that the model has an average test accuracy of 96.8% and average test loss of 12.8%.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".