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Sensors 2018, 18(4), 1229; https://doi.org/10.3390/s18041229

LiteNet: Lightweight Neural Network for Detecting Arrhythmias at Resource-Constrained Mobile Devices

1
Collaborative Innovation Center for Internet Healthcare, Zhengzhou University, 75 University North Road, Erqi District, Zhengzhou 450000, China
2
School of Software Engineering, Zhengzhou University, 97 Culture Road, Jinshui District, Zhengzhou 450000, China
3
Department of Mathematical and Systems Engineering, Shizuoka University, 5-627, 3-5-1 Johoku Hamamatsu 432-8561, Japan
4
College of Engineering, Ibaraki University, 4-12-1 Nakanarusawa, Hitachi, Ibaraki 316-8511, Japan
*
Author to whom correspondence should be addressed.
Received: 1 April 2018 / Revised: 12 April 2018 / Accepted: 12 April 2018 / Published: 17 April 2018
(This article belongs to the Special Issue New Paradigms in Data Sensing and Processing for Edge Computing)
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Abstract

By running applications and services closer to the user, edge processing provides many advantages, such as short response time and reduced network traffic. Deep-learning based algorithms provide significantly better performances than traditional algorithms in many fields but demand more resources, such as higher computational power and more memory. Hence, designing deep learning algorithms that are more suitable for resource-constrained mobile devices is vital. In this paper, we build a lightweight neural network, termed LiteNet which uses a deep learning algorithm design to diagnose arrhythmias, as an example to show how we design deep learning schemes for resource-constrained mobile devices. Compare to other deep learning models with an equivalent accuracy, LiteNet has several advantages. It requires less memory, incurs lower computational cost, and is more feasible for deployment on resource-constrained mobile devices. It can be trained faster than other neural network algorithms and requires less communication across different processing units during distributed training. It uses filters of heterogeneous size in a convolutional layer, which contributes to the generation of various feature maps. The algorithm was tested using the MIT-BIH electrocardiogram (ECG) arrhythmia database; the results showed that LiteNet outperforms comparable schemes in diagnosing arrhythmias, and in its feasibility for use at the mobile devices. View Full-Text
Keywords: deep learning algorithms; lightweight neural network; resource-constrained mobile devices; electrocardiogram deep learning algorithms; lightweight neural network; resource-constrained mobile devices; electrocardiogram
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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He, Z.; Zhang, X.; Cao, Y.; Liu, Z.; Zhang, B.; Wang, X. LiteNet: Lightweight Neural Network for Detecting Arrhythmias at Resource-Constrained Mobile Devices. Sensors 2018, 18, 1229.

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