Journal article
Integer convolutional neural network for seizure detection
ND Truong, AD Nguyen, L Kuhlmann, MR Bonyadi, J Yang, S Ippolito, O Kavehei
IEEE Journal on Emerging and Selected Topics in Circuits and Systems | Published : 2018
Abstract
Outstanding seizure detection algorithms have been developed over past two decades. Despite this success, their implementations as part of implantable or wearable devices are still limited. These works are mainly based on heavily handcrafted feature extraction, which is computationally expensive and is shown to be data set specific. These issues greatly limit the applicability of such methods to hardware implementation, including in-silicon implementations such as application specific integrated circuits. In this paper, we propose an integer convolutional neural network (CNN) implementation, Integer-Net, as a memory-efficient unified hardware-friendly CNN framework. The performance of Intege..
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Awarded by Commonwealth Scientific and Industrial Research Organisation
Funding Acknowledgements
This work was supported by the Sydney Informatics Hub, University of Sydney. The work of N. D. Truong was supported in part by the Faculty of Engineering and Information Technology, The University of Sydney, through the Early Career Research Grant, and in part by the Commonwealth Scientific and Industrial Research Organisation (CSIRO) through a Ph.D. Scholarship under Grant PN 50041400. The work of J. Yang was supported by the National Natural Science Foundation of China under Grant 61501332. The work of O. Kavehei was supported by the Faculty of Engineering and Information Technology, The University of Sydney, through the Early Career Research Grant.