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Journal of Healthcare Engineering
Volume 2017, Article ID 5980541, 14 pages
Research Article

An Adaptive and Time-Efficient ECG R-Peak Detection Algorithm

School of Instrument Science and Engineering, Southeast University, Nanjing 210018, China

Correspondence should be addressed to Jianqing Li; nc.ude.ues@qjl

Received 16 March 2017; Revised 19 June 2017; Accepted 12 July 2017; Published 6 September 2017

Academic Editor: Ioannis G. Tollis

Copyright © 2017 Qin Qin 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.


R-peak detection is crucial in electrocardiogram (ECG) signal analysis. This study proposed an adaptive and time-efficient R-peak detection algorithm for ECG processing. First, wavelet multiresolution analysis was applied to enhance the ECG signal representation. Then, ECG was mirrored to convert large negative R-peaks to positive ones. After that, local maximums were calculated by the first-order forward differential approach and were truncated by the amplitude and time interval thresholds to locate the R-peaks. The algorithm performances, including detection accuracy and time consumption, were tested on the MIT-BIH arrhythmia database and the QT database. Experimental results showed that the proposed algorithm achieved mean sensitivity of 99.39%, positive predictivity of 99.49%, and accuracy of 98.89% on the MIT-BIH arrhythmia database and 99.83%, 99.90%, and 99.73%, respectively, on the QT database. By processing one ECG record, the mean time consumptions were 0.872 s and 0.763 s for the MIT-BIH arrhythmia database and QT database, respectively, yielding 30.6% and 32.9% of time reduction compared to the traditional Pan-Tompkins method.