PFT: A Novel Time-Frequency Decomposition of BOLD fMRI Signals for Autism Spectrum Disorder Detection

Abstract

Diagnosing Autism spectrum disorder (ASD) is a challenging task for clinicians due to the inconsistencies in existing medical tests. The Internet of things (IoT) has been used in several medical applications to realize advancements in the healthcare industry. Using machine learning in tandem IoT can enhance the monitoring and detection of ASD. To date, most ASD studies have relied primarily on the operational connectivity and structural metrics of fMRI data processing while neglecting the temporal dynamics components. Our research proposes Progressive Fourier Transform (PFT), a novel time-frequency decomposition, together with a Convolutional Neural Network (CNN), as a preferred alternative to available ASD detection systems. We use the Autism Brain Imaging Data Exchange dataset for model validation, demonstrating better results of the proposed PFT model compared to the existing models, including an increase in accuracy to 96.7%. These results show that the proposed technique is capable of analyzing rs-fMRI data from different brain diseases of the same type.