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Arrhythmia Evaluation in Wearable ECG Devices

Department of Mechanical Engineering and Innovation Center for Big Data and Digital Convergence, Yuan Ze University, Taoyuan, Chung-Li 32003, Taiwan
Healthcare and Beauty RD Center, Kinpo Electronics, Inc., New Taipei City 222, Taiwan
Department of Electronic and Computer Engineering, Brunel University London, Uxbridge UB8 3PH, UK
Author to whom correspondence should be addressed.
Sensors 2017, 17(11), 2445;
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)
PDF [2975 KB, uploaded 25 October 2017]


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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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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