Classification of Autism Spectrum Disorder Using Edge‐Weight Enhanced Graph Attention Network With Multiple Features of Resting‐State fNIRS Signals

ABSTRACT Functional near‐infrared spectroscopy (fNIRS), as a noninvasive brain imaging modality, has shown great potential for autism spectrum disorder (ASD) identification combined with machine learning. In this work, we proposed an ASD identification method using edge‐weight enhanced graph attention network (EWE‐GAT) with multiple features in resting‐state fNIRS signals measured from the bilateral temporal lobes on 22 typically developing (TD) children and 25 children with ASD. Seven features were selected for the EWE‐GAT model, including five node features: the coupling between oxygenated hemoglobin (HbO) and deoxygenated hemoglobin (Hb) fluctuations, sample entropy for HbO and Hb, and average resting‐state functional connectivity (RSFC) for HbO and Hb of each channel, and two edge features: RSFC between each channel pair for both HbO and Hb. With the proposed method, high accurate classification was achieved with 97.92% accuracy, 100% sensitivity, 96.43% precision, and 98.08% F1 score, outperforming usually used traditional machine learning and convolutional neural network models.