Optimized Complex-Valued Spatio-Temporal Graph Convolutional Networks for attention deficit hyperactivity disorder...

Area of Science:

• Neuroscience
• Machine Learning
• Biomedical Signal Processing

Background:

• Attention Deficit Hyperactivity Disorder (ADHD) is a neurodevelopmental condition impacting various cognitive and emotional functions.
• Electroencephalogram (EEG) signals are crucial for ADHD diagnosis but present challenges like complexity and non-stationarity.
• Existing diagnostic methods for ADHD and Learning Disabilities (LD) using EEG signals have limitations in feature interpretability and accuracy.

Purpose of the Study:

• To propose an optimized complex-valued spatio-temporal graph convolutional network (CSTGCN) for accurate detection of ADHD in pediatric EEG signals.
• To enhance the classification performance for both ADHD and Learning Disabilities (LD) by addressing signal complexity and feature extraction challenges.
• To introduce a novel framework integrating advanced signal processing and deep learning for improved neurodevelopmental disorder detection.

Main Methods:

• Preprocessing of EEG signals using Multi-Window Savitzky-Golay Filter (MWSGF) for noise reduction and Synchro Transient Extracting Transform (STET) for feature extraction.
• Utilizing a Complex-Valued Spatio-Temporal Graph Convolutional Network (CSTGCN) for classifying EEG signals into ADHD/No-ADHD and LD/No-LD categories.
• Employing the Red-Billed Blue Magpie Optimization Algorithm (RBBMO) to optimize the weights of the CSTGCN model for enhanced performance.

Main Results:

• The proposed CSTGCN-ADHD-EEG framework achieved a high accuracy of 99.46% and a precision of 98.32% in classifying pediatric EEG signals.
• The model demonstrated superior performance compared to existing methods in detecting both ADHD and Learning Disabilities.
• Effective noise and artifact removal were achieved through MWSGF and STET, leading to robust feature extraction.

Conclusions:

• The optimized CSTGCN framework offers a highly accurate and effective method for detecting ADHD and LD in pediatric EEG data.
• This advanced deep learning approach overcomes limitations of traditional EEG analysis for neurodevelopmental disorders.
• The findings suggest significant potential for the CSTGCN-ADHD-EEG model in clinical diagnostic applications for children.