Dominant cross-frequency analysis-based early diagnosis of Autism Spectrum Disorder in pediatrics using FRHIS and DSTCNN.

Autism Spectrum Disorder (ASD) is a prevalent neurodevelopmental condition that affects children's cognitive, social, and behavioral skills. Thus, early diagnosis of ASD for children below 2 years old is very significant. Nevertheless, the prevailing works overlooked the depth of the dynamic frequency band variation during ASD detection, leading to misclassification. Therefore, a dominant cross-frequency analysis-based early ASD detection in pediatrics using a Fuzzy Root Hesitant Inference System (FRHIS) and Deep Squared Tau Convolutional Neural Network (DSTCNN) is proposed in this paper. Primarily, the Electroencephalogram (EEG) signals of children below 2 years old are gathered. Then, the artifacts are removed using the EWI-BPF. Afterward, by using Variation Frechet Distribution Mode Decomposition (VFDMD), the signals are decomposed. Then, by utilizing the FRHIS, the frequency bands are determined, followed by non-linear variables and feature extraction. In the meantime, the cross-frequency analysis is carried out by estimating the modulation depth using the Phase Amplitude Method (PAM) and determining the cross-frequency strength. Then, to select optimal features, the CRBM is applied. Lastly, the ASD classification is carried out using DSTCNN. When analogized with other traditional works, the proposed work attained superior performance in detecting ASD for pediatrics with a higher accuracy of 99.03%.