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

Attention Deficit Hyperactivity Disorder (ADHD) is a neurodevelopmental condition affecting mood, anxiety, learning, and sleep. Electroencephalogram (EEG) signals assist diagnosis, but challenges include complexity, nonlinearity, non-stationarity, overlapping patterns, and limited feature interpretability. To address these issues, Optimized Complex-Valued Spatio-Temporal Graph Convolutional Networks for ADHD Detection in Pediatric EEG Signals (c) is proposed. Input signals are collected from an EEG dataset for ADHD and an EEG dataset of children with Learning Disabilities (LD). Preprocessing is performed using a Multi-Window Savitzky-Golay Filter (MWSGF) to remove noise and artifacts, followed by the Synchro Transient Extracting Transform (STET) for extracting EEG channel features. These features are input into a Complex-Valued Spatio-Temporal Graph Convolutional Network (CSTGCN), classifying signals into ADHD or No-ADHD, and LD or No-LD. Red-Billed Blue Magpie Optimization Algorithm (RBBMO) is employed to optimize the network weights. Implemented in Python, the proposed CSTGCN-ADHD-EEG framework achieves 99.46% accuracy and 98.32% precision, outperforming existing models.