De Melo Ribeiro, Henrique, Arnold, Ahran, Howard, James P., Shun-Shin, Matthew J., Zhang, Ying, Francis, Darrel P., Lim, Phang B., Whinnett, Zachary and Zolgharni, Massoud ORCID: https://orcid.org/0000-0003-0904-2904 (2022) ECG-based real-time arrhythmia monitoring using quantized deep neural networks: a feasibility study. Computers in Biology and Medicine, 143. p. 105249. ISSN 0010-4825

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Abstract

Continuous ambulatory cardiac monitoring plays a critical role in early detection of abnormality in at-risk patients, thereby increasing the chance of early intervention. In this study, we present an automated ECG classification approach for distinguishing between healthy heartbeats and pathological rhythms. The proposed lightweight solution uses quantized one-dimensional deep convolutional neural networks and is ideal for real-time continuous monitoring of cardiac rhythm, capable of providing one output prediction per second. Raw ECG data is used as the input to the classifier, eliminating the need for complex data preprocessing on low-powered wearable devices. In contrast to many compute-intensive approaches, the data analysis can be carried out locally on edge devices, providing privacy and portability. The proposed lightweight solution is accurate (sensitivity of 98.5% and specificity of 99.8%), and implemented on a smartphone, it is energy-efficient and fast, requiring 5.85mJ and 7.65ms per prediction, respectively.

Item Type:	Article
Identifier:	10.1016/j.compbiomed.2022.105249
Keywords:	Arrhythmia, ECG, Deep learning, Continuous monitoring, Heart disease
Subjects:	Computing > Intelligent systems
Related URLs:	Publisher
Depositing User:	Massoud Zolgharni
Date Deposited:	27 Jan 2022 16:21
Last Modified:	04 Nov 2024 11:19
URI:	https://repository.uwl.ac.uk/id/eprint/8748

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