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Sensors 2017, 17(11), 2445; doi:10.3390/s17112445

Arrhythmia Evaluation in Wearable ECG Devices

1
Department of Mechanical Engineering and Innovation Center for Big Data and Digital Convergence, Yuan Ze University, Taoyuan, Chung-Li 32003, Taiwan
2
Healthcare and Beauty RD Center, Kinpo Electronics, Inc., New Taipei City 222, Taiwan
3
Department of Electronic and Computer Engineering, Brunel University London, Uxbridge UB8 3PH, UK
*
Author to whom correspondence should be addressed.
Received: 19 September 2017 / Revised: 20 October 2017 / Accepted: 21 October 2017 / Published: 25 October 2017
(This article belongs to the Special Issue Biomedical Sensors and Systems 2017)
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Abstract

This study evaluates four databases from PhysioNet: The American Heart Association database (AHADB), Creighton University Ventricular Tachyarrhythmia database (CUDB), MIT-BIH Arrhythmia database (MITDB), and MIT-BIH Noise Stress Test database (NSTDB). The ANSI/AAMI EC57:2012 is used for the evaluation of the algorithms for the supraventricular ectopic beat (SVEB), ventricular ectopic beat (VEB), atrial fibrillation (AF), and ventricular fibrillation (VF) via the evaluation of the sensitivity, positive predictivity and false positive rate. Sample entropy, fast Fourier transform (FFT), and multilayer perceptron neural network with backpropagation training algorithm are selected for the integrated detection algorithms. For this study, the result for SVEB has some improvements compared to a previous study that also utilized ANSI/AAMI EC57. In further, VEB sensitivity and positive predictivity gross evaluations have greater than 80%, except for the positive predictivity of the NSTDB database. For AF gross evaluation of MITDB database, the results show very good classification, excluding the episode sensitivity. In advanced, for VF gross evaluation, the episode sensitivity and positive predictivity for the AHADB, MITDB, and CUDB, have greater than 80%, except for MITDB episode positive predictivity, which is 75%. The achieved results show that the proposed integrated SVEB, VEB, AF, and VF detection algorithm has an accurate classification according to ANSI/AAMI EC57:2012. In conclusion, the proposed integrated detection algorithm can achieve good accuracy in comparison with other previous studies. Furthermore, more advanced algorithms and hardware devices should be performed in future for arrhythmia detection and evaluation. View Full-Text
Keywords: wearable sensor; arrhythmia; sample entropy; fast Fourier transform; artificial neural networks wearable sensor; arrhythmia; sample entropy; fast Fourier transform; artificial neural networks
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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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MDPI and ACS Style

Sadrawi, M.; Lin, C.-H.; Lin, Y.-T.; Hsieh, Y.; Kuo, C.-C.; Chien, J.C.; Haraikawa, K.; Abbod, M.F.; Shieh, J.-S. Arrhythmia Evaluation in Wearable ECG Devices. Sensors 2017, 17, 2445.

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