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

Aslam, A. R., Hafeez, N., Heidari, H. and Altaf, M. A. B. (2021) An 8.62 µW Processor for Autism Spectrum Disorder Classification using Shallow Neural Network. In: 3rd IEEE International Conference on Artificial Intelligence Circuits & Systems, 6-9 Jun 2021, ISBN 9781665430258 (doi: 10.1109/AICAS51828.2021.9458412)

Abstract

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 8.62μW and 2.25ms, respectively.

• Item Type: Conference Proceedings
• Additional Information: This work was funded by the Higher Education Commission (HEC), Pakistan, under grant no.7989/Punjab/NRPU/R&D/HEC/2017 and Syed Babar Ali Research Award (SBARA), LUMS, Pakistan.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Heidari, Professor Hadi
• Authors: Aslam, A. R., Hafeez, N., Heidari, H., and Altaf, M. A. B.
• College/School: College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering
• ISBN: 9781665430258
• Copyright Holders: Copyright © 2021 IEEE
• First Published: First published in Proceedings of 2021 IEEE 3rd International Conference on Artificial Intelligence Circuits and Systems (AICAS)
• Publisher Policy: Reproduced in accordance with the copyright policy of the publisher

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