Version 1
: Received: 26 July 2024 / Approved: 29 July 2024 / Online: 30 July 2024 (10:19:36 CEST)
How to cite:
Montgomery, R. M. Deep Learning for ADHD Diagnosis: Integrating Diverse EEG Biomarkers for Enhanced Predictive Accuracy. Preprints2024, 2024072416. https://doi.org/10.20944/preprints202407.2416.v1
Montgomery, R. M. Deep Learning for ADHD Diagnosis: Integrating Diverse EEG Biomarkers for Enhanced Predictive Accuracy. Preprints 2024, 2024072416. https://doi.org/10.20944/preprints202407.2416.v1
Montgomery, R. M. Deep Learning for ADHD Diagnosis: Integrating Diverse EEG Biomarkers for Enhanced Predictive Accuracy. Preprints2024, 2024072416. https://doi.org/10.20944/preprints202407.2416.v1
APA Style
Montgomery, R. M. (2024). Deep Learning for ADHD Diagnosis: Integrating Diverse EEG Biomarkers for Enhanced Predictive Accuracy. Preprints. https://doi.org/10.20944/preprints202407.2416.v1
Chicago/Turabian Style
Montgomery, R. M. 2024 "Deep Learning for ADHD Diagnosis: Integrating Diverse EEG Biomarkers for Enhanced Predictive Accuracy" Preprints. https://doi.org/10.20944/preprints202407.2416.v1
Abstract
Attention Deficit Hyperactivity Disorder (ADHD) diagnosis poses significant challenges due to its heterogeneous nature and reliance on subjective assessments. This study leverages deep learning to develop a robust diagnostic model by integrating a comprehensive set of EEG biomarkers. We incorporate parameters such as theta/beta ratio, coherency measures, delta power, event-related potentials (ERPs), power spectral density (PSD), microstates, entropy measures, fractal dimension, and source localization. Our convolutional neural network (CNN) model, designed to process these diverse features, demonstrates high accuracy and stability in distinguishing ADHD patients from controls, from all ages. The model's architecture includes convolutional layers for spatial feature extraction, followed by dense layers that integrate additional EEG parameters. The results indicate that deep learning, coupled with a rich feature set, can significantly enhance the predictive accuracy of ADHD diagnosis, offering a promising tool for clinical application.
Keywords
ADHD; EEG; enhanced biomarker; deep learning
Subject
Biology and Life Sciences, Neuroscience and Neurology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
