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Journal of Healthcare Engineering
Volume 2018, Article ID 5694595, 8 pages
https://doi.org/10.1155/2018/5694595
Research Article

QRS Detection Based on Improved Adaptive Threshold

School of Data and Computer Science, University of Sun Yat-sen, Guangzhou Higher Education Mega Center, No. 132 Waihuan East Road, Guangzhou 510006, China

Correspondence should be addressed to Yang Yu; nc.ude.usys.liam@yuy

Received 1 August 2017; Revised 30 November 2017; Accepted 18 January 2018; Published 15 March 2018

Academic Editor: Zong-Min Wang

Copyright © 2018 Xuanyu Lu et al. 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.

Abstract

Cardiovascular disease is the first cause of death around the world. In accomplishing quick and accurate diagnosis, automatic electrocardiogram (ECG) analysis algorithm plays an important role, whose first step is QRS detection. The threshold algorithm of QRS complex detection is known for its high-speed computation and minimized memory storage. In this mobile era, threshold algorithm can be easily transported into portable, wearable, and wireless ECG systems. However, the detection rate of the threshold algorithm still calls for improvement. An improved adaptive threshold algorithm for QRS detection is reported in this paper. The main steps of this algorithm are preprocessing, peak finding, and adaptive threshold QRS detecting. The detection rate is 99.41%, the sensitivity (Se) is 99.72%, and the specificity (Sp) is 99.69% on the MIT-BIH Arrhythmia database. A comparison is also made with two other algorithms, to prove our superiority. The suspicious abnormal area is shown at the end of the algorithm and RR-Lorenz plot drawn for doctors and cardiologists to use as aid for diagnosis.