An 8.62 μ W Processor for Autism Spectrum Disorder Classification using Shallow Neural Network

Autism Spectrum Disorder (ASD) is the prevalent child neurological and developmental disorder causing cognitive and behavioral impairments. The early diagnosis is an urgent need for treatment and rehabilitation of ASD patients. This work presents an electroencephalogram (EEG) based ASD classification processor that targets a patch-form factor sensor that can be used for long time monitoring in a wearable environment. The selection of frontal and parietal lobe electrodes causes minimum uneasiness to the children. The proposed and implemented algorithm utilizes only four EEG electrodes. The processor is implemented and validated on Artix-7 FPGA which requires only 26K lookup tables and 15K flip flops. The hardware efficient implementation of the complex kurtosis value and Katz fractal dimension (KFD) features using kurtosis value indicator and KFD indicator with 54% and 38% efficient implementations, respectively, is provided. A hardware feasible shallow neural network architecture is used for the ASD classification. The system classifies the ASD with a high classification accuracy of 85.5% using the power and latency of $8.62 \mu \mathrm{W}$ and 2.25ms, respectively.