Integer convolutional neural network for seizure detection

Truong, N, Nguyen, A, Kuhlmann, L, Bonyadi, M, Yang, J, Ippolito, S and Kavehei, O 2018, 'Integer convolutional neural network for seizure detection', IEEE Journal on Emerging and Selected Topics in Circuits and Systems, vol. 8, no. 4, pp. 849-857.


Document type: Journal Article
Collection: Journal Articles

Title Integer convolutional neural network for seizure detection
Author(s) Truong, N
Nguyen, A
Kuhlmann, L
Bonyadi, M
Yang, J
Ippolito, S
Kavehei, O
Year 2018
Journal name IEEE Journal on Emerging and Selected Topics in Circuits and Systems
Volume number 8
Issue number 4
Start page 849
End page 857
Total pages 9
Publisher Institute of Electrical and Electronics Engineers
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 Integer-Net is evaluated with multiple time-series data sets consisting of intracranial and scalp electroencephalogram (EEG) signals. Integer-Net shows a consistent seizure detection performance across three data sets: Freiburg Hospital intracranial EEG data set, Children's Hospital of Boston-MIT scalp EEG data set, and UPenn and Mayo Clinic's seizure detection data set. Our experimental results show that a 4-bit Integer-Net leads to only 2% drop of accuracy compared with a 32-bit real-value resolution CNN model, while offering more than 7 times improvement in memory efficiency. We discuss the structure of the integer convolution to improve the computational gain and reduce the inference time that are crucial for real-time application.
Subject Biomedical Engineering not elsewhere classified
Pattern Recognition and Data Mining
Keyword(s) Integer-Net
seizure detection
machine learning
convolutional neural network
integer convolution
DOI - identifier 10.1109/jetcas.2018.2842761
Copyright notice © 2018 IEEE
ISSN 2156-3357
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