Comparison of Low-Complexity Algorithms for Real-Time QRS Detection using Standard ECG Database

Silvestri Francesca, Cardarilli Gian Carlo, Luca Di Nunzio, Fazzolari Rocco, Re Marco


Today, thanks to the development of advanced wearable devices, it is possible to track patient conditions outside hospital setting for several days. One of the most important bio-signals used for health analysis is the electrocardiographic (ECG) signal. It provides information about the heart rate, rhythm, and morphology of heart. Many algorithms are proposed over years for automated ECG analysis. Due to their computational complexity, not all these techniques can be implemented on wearable devices for real-time ECG detection. In this frame, a particular interest is toward the algorithms for automatic QRS detection. Different algorithms have been presented in the literature. Among all, more suitable class for the implementation on embedded systems is based on the use of signal derivatives and thresholds. These algorithms are composed by pre-processing stage, for the noise removal, and decision stage for the QRS detection. In literature, the different algorithms were discriminated only with respect to their pre-processing stages. Furthermore, not all algorithms were tested with standard database: this makes the results difficult to compare and evaluate. Moreover, the algorithms performance in case of pathological behaviours was not compared. This paper is motivated by the need to perform a comparison of the whole algorithms, both pre-processing and decision stages, under a standard database (MIT-BIH ECG database of Physionet), either for non-pathological and pathological signals. The results confirm that the Pan & Tompkins algorithm has the best performance in terms of QRS complex detection. However, in some cases, its performance is comparable with the other algorithms ones. For this reason, in the applications in which the reduced of computational complexity is an important constraint, it is possible to implemented algorithms with comparable performance but with lesser complexity with respect to P&T algorithm.


QRS detection; ECG processing; wearable computing; real-time analysis; low power.

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Braunwald E.(ed), Heart Disease: A Textbook of Cardiovascular Medicine, Fifth Edition, Philadelphia, W.B. Saunders Co, 1997.

World Health Organization, Cardiovascular Diseases (CVDs). 2014.

Bert-Uwe Kohler, Carsten Henning, Reinhold Orglmeister, The principles of software QRS detection, IEEE Engineering in Medicine and Biology, January, February 2002.

Ferroni, Patrizia, Fabio Massimo Zanzotto, Noemi Scarpato, Silvia Riondino, Umberto Nanni, Mario Roselli, and Fiorella Guadagni, Risk Assessment for Venous Thromboembolism in Chemotherapy-Treated Ambulatory Cancer Patients, in Medical Decision Making, 37 (2): 234-42, 2017. doi:10.1177/0272989X16662654.

John G. Webster, Medical Instrumentation: Application and Design, 4th Edition.

M. Chan, D. Est_eve, J.Y. Fourniols, C. Escriba and E. Campo, Smart wearable systems: current status and future challenges, in Artificial Intelligence in Medicine, 56, pp. 137-156, 2012.

Gary M. Friesen et all., A Comparison of the Noise Sensitivity of Nine QRS Detection Algorithms, IEEE Transaction on Biomedical Engineering, vol. 37, No. I, January 1990.

George B. Moody and Roger G. Mark, “The Impact of the MIT-BIH Arrhythmia Databaseâ€, IEEE Engineering in Medicine and Biology, Vol: 20, Issue: 3, May-June 2001.

Massachusetts Institute of Technology, MIT-BIH ECG database. Available:

P.M. Mahoudeaux, Simple Microprocessor based system for on line ECG Analysis, Med. Biol.Eng. Computing, vol. 19, pp497-500, 1981.

J.F. M.R. Neumann, QRS wave detection, Med. Biol.Eng. Comput., vol 18, pp 125-132, 1980.

D.E. Gustafson et all, ECG/VCG Rhythm Diagnosis Using Statistical Signal Analysis Identification of Persistent Rhythms, Biomedical Engineering IEEE Transactions on, vol.BME-25 pp.344-353, 1978.

A. Menrad, Dual Microprocessor System for Cardiovascular Data Acquisition, Processing and Recording. IEEE pp. 64-69, 1981.

W.P. Holsinger, K.M. Kemper, and M.H. Miller, A QRS Preprocessor Based on Digital Differentiation, Biomedical Engineering IEEE Transaction on, vol. BME-18, pp 212-217, 1971.

R.A. Balda, J.H.Van Bemmel, J.L. Willems, Trends in Computer-Processes Electrocardiograms. 1977.

M.L. Ahlstrom, WJ. Tompkins, Automated High Speed Analysis of Holter Types with Microcomputers, Biomedical Engineering, IEEE Transaction on, vol. BME-30, pp 651-657, 1983.

W.A.H. Engelse, C. Zeelemberg, A simple scan algorithm for QRS detection and feature extraction, IEEE Computer Society, pp 37-42, 1979.

M. Okada, A Digital Filter for the QRS Complex Detection, Biomedical Engineering, IEEE Transaction on, vol. BME-26, pp 700-703, 1984.

J. Pan and W.J. Tompkins, A real-time QRS detection algorithm, IEEE Trans. Biomed. Eng., vol. BME-32, pp. 230-236, 1985.

Massachusetts Institute of Technology, The Apnea-ECG Database. Available:

ANSI/AAMI EC57: Testing and reporting performance results of cardiac rhythm and ST segment measurement algorithms (AAMI Recommended Practice/American National Standard, 1988. Available:

F. Silvestri, S. Acciarito, G.C. Cardarilli, G.M. Khanal, L. Di Nunzio, R. Fazzolari, M. Re, FPGA Implementation of a Low-power QRS extractor, in Lecture Notes in Electrical Engineering, 2018 (Article In Press).

Cardarilli, G.C., Di Nunzio, L., Re, M. Arithmetic/logic blocks for fine-grained reconfigurable units (2009) Proceedings - IEEE International Symposium on Circuits and Systems, art. no. 5118184, pp. 2001-2004

Cardarilli, G.C., Di Nunzio, L., Fazzolari, R., Re, M., Lee, R.B., Butterfly and inverse butterfly nets integration on Altera NIOS-II embedded processor, Conference Record - Asilomar Conference on Signals, Systems and Computers, art. no. 5757737, pp. 1279-1283.

Cardarilli, G.C., Di Nunzio, L., Fazzolari, R., Pontarelli, S., Re, M., Salsano, A., Implementation of the AES algorithm using a Reconfigurable Functional Unit, ISSCS 2011 - International Symposium on Signals, Circuits and Systems, Proceedings, art. no. 5978668, pp. 97-100.

Cardarilli, G.C., Di Nunzio, L., Fazzolari, R., Re, M., Fine-grain reconfigurable functional unit for embedded processors, Conference Record - Asilomar Conference on Signals, Systems and Computers, art. no. 6190048, pp. 488-492.

Cardarilli, G.C., Di Nunzio, L., Fazzolari, R., Pontarelli, S., Re, M., A reconfigurable functional unit for modular operations, Lecture Notes in Electrical Engineering, 289, pp. 141-152.

Cardarilli, G.C., Di Nunzio, L., Fazzolari, R., Re, M., TDES cryptography algorithm acceleration using a reconfigurable functional unit, 21st IEEE International Conference on Electronics, Circuits and Systems, ICECS 2014, art. no. 7050011, pp. 419-422.



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