Developing an Interpretable EEG-Based Model for ADHD Diagnosis in Children Using Temporal, Spectral, and Wavelet Features
Bibliographic record
Abstract
Attention-deficit and hyperactivity disorder (ADHD) is a clinical challenge because its signs and symptoms overlap with those of other conditions, making accurate and objective diagnosis difficult using behavioral tests alone.The study aims at a multi-view EEG signal classification model for ADHD, as well as interpretation of clinical implications.Three kinds of features were extracted: temporal statistical features, power waveform retorn time, and frequency-domain analyzed data across four levels and power spectral density (PSD).The Random Forest method was employed to analyze them.Additionally, to support anatomical interpretation, the most important channel-related features were projected onto a topographic map.The suggested method surpassed the other machine learning models tested in this study, including the K-nearest Neighbor algorithm (95.1% test accuracy) and the Decision Tree model (82% test accuracy).The Logistic Regression algorithm attained an accuracy of 69%, while the Support Vector Machine algorithm recorded the lowest accuracy at 55.9% The Random Forest model achieved 95.7% test accuracy.These results were further confirmed through cross-validation, which showed consistent performance and low variability for the Random Forest model.This comparison demonstrated the Random Forest model's ability to handle nonlinear, time-varying data and its generalization capability.The results indicate the potential of the proposed approach as a robust and clinically applicable tool for ADHD detection, laying the groundwork for future investigations involving larger datasets and advanced methodologies.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".