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Journal of Healthcare Engineering
Volume 2018, Article ID 9050812, 8 pages
Research Article

Performance Analysis of Ten Common QRS Detectors on Different ECG Application Cases

1The State Key Laboratory of Bioelectronics, Jiangsu Key Lab of Remote Measurement and Control, School of Instrument Science and Engineering, Southeast University, Nanjing, China
2Shandong Zhong Yang Software Limited Company, Jinan, China
3School of Control Science and Engineering, Shandong University, Jinan, China

Correspondence should be addressed to Chengyu Liu; nc.ude.uds@ycltseb and Shoushui Wei; nc.ude.uds@iewss

Received 16 January 2018; Revised 22 March 2018; Accepted 10 April 2018; Published 8 May 2018

Academic Editor: Norio Iriguchi

Copyright © 2018 Feifei Liu 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.


A systematical evaluation work was performed on ten widely used and high-efficient QRS detection algorithms in this study, aiming at verifying their performances and usefulness in different application situations. Four experiments were carried on six internationally recognized databases. Firstly, in the test of high-quality ECG database versus low-quality ECG database, for high signal quality database, all ten QRS detection algorithms had very high detection accuracy ( >99%), whereas the results decrease significantly for the poor signal-quality ECG signals (all <80%). Secondly, in the test of normal ECG database versus arrhythmic ECG database, all ten QRS detection algorithms had good results for these two databases (all >95% except RS slope algorithm with 94.24% on normal ECG database and 94.44% on arrhythmia database). Thirdly, for the paced rhythm ECG database, all ten algorithms were immune to the paced beats (>94%) except the RS slope method, which only output a low result of 78.99%. At last, the detection accuracies had obvious decreases when dealing with the dynamic telehealth ECG signals (all <80%) except OKB algorithm with 80.43%. Furthermore, the time costs from analyzing a 10 s ECG segment were given as the quantitative index of the computational complexity. All ten algorithms had high numerical efficiency (all <4 ms) except RS slope (94.07 ms) and sixth power algorithms (8.25 ms). And OKB algorithm had the highest numerical efficiency (1.54 ms).