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

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.

Automated summary

Diagnosing Autism spectrum disorder (ASD) has been challenging due to inconsistencies in existing medical tests. The use of Internet of things (IoT) combined with machine learning can improve the monitoring and detection of ASD. Our research proposes a new technique, Progressive Fourier Transform (PFT), together with a Convolutional Neural Network (CNN), which shows better results compared to existing ASD detection systems, achieving an accuracy of 96.7% using the Autism Brain Imaging Data Exchange dataset.