High Dimensional Convolutional Neural Network for EEG Connectivity-Based Diagnosis of ADHD

Area of Science:

• Neuroscience
• Medical Imaging
• Artificial Intelligence

Background:

• Attention-deficit/hyperactivity disorder (ADHD) is a prevalent neurodevelopmental disorder requiring early detection for effective treatment.
• Electroencephalography (EEG) is a key tool for classifying ADHD in children.
• Deep learning approaches are enhancing the accuracy of ADHD classification using EEG data.

Purpose of the Study:

• To adapt convolutional neural networks (CNNs) for ADHD classification.
• To analyze EEG signal connectivity for diagnosing ADHD in children.

Main Methods:

• Utilized EEG data from 61 ADHD and 60 normal children.
• Employed synchronization likelihood (SL) and wavelet coherence (WC) as connectivity measures.
• Developed 4D and 6D CNN architectures using connectivity tensors derived from EEG channel neighborhoods.

Main Results:

• Achieved high accuracy in epoch-based classification: 98.56% (4D CNN) and 98.85% (6D CNN).
• Attained 99.17% accuracy in subject-based classification for both models.
• Demonstrated the effectiveness of CNNs in differentiating ADHD from normal children.

Conclusions:

• The proposed CNN models accurately diagnose ADHD in children.
• EEG connectivity analysis with deep learning provides a reliable method for distinguishing ADHD from normal children.