Abstract
In this paper, a novel algorithm for the accurate localization of QRS complex with low average time error is proposed. The idea is thought that the various features of ECG signal like P, Q, R, S and T peaks can be independently detected from raw ECG recording and fused together to obtain a better estimate of QRS position. To explore, in this paper, an algorithm is suggested to first estimate R peak and S peak from raw ECG signal and then fused together to detect and localize QRS complex. The algorithm is validated on all the signals of MIT-BIH arrhythmia database, QT database and noise stress database taken from physionet.org. The algorithm performs reasonably well even for the signals highly corrupted with noise, and these noises are generated by adding the power line interference, electrode motion artifact, baseline wandering interference and muscle artifact to all the signals of MIT-BIH arrhythmia database and QT database. The algorithm performance is confirmed not only with a very high sensitivity and positive predictivity, but also with a very low average time error of 0.63 ms against the 3.03 ms the best results reported so far for the signals of MIT-BIH arrhythmia database and 0.85 ms against the 3.6 ms the best results reported in the literature for the signals of QT database.
Similar content being viewed by others
References
J. Arteaga-Falconi, H. Al Osman, A. El Saddik, R-peak detection algorithm based on differentiation, in 2015 IEEE 9th International Symposium on Intelligent Signal Processing (WISP) (IEEE, 2015), pp. 1–4
N.M. Arzeno, Z.D. Deng, C.S. Poon, Analysis of first-derivative based QRS detection algorithms. IEEE Trans. Biomed. Eng. 55(2), 478–484 (2008)
S. Banerjee, M. Mitra, ECG feature extraction and classification of anteroseptal myocardial infarction and normal subjects using discrete wavelet transform, in 2010 International Conference on Systems in Medicine and Biology (ICSMB) (IEEE, 2010), pp. 55–60
D. Benitez, P. Gaydecki, A. Zaidi, A. Fitzpatrick, The use of the Hilbert transform in ECG signal analysis. Comput. Biol. Med. 31(5), 399–406 (2001)
D. Castells-Rufas, J. Carrabina, Simple real-time QRS detector with the MaMeMi filter. Biomed. Signal Process. Control 21, 137–145 (2015)
M. Elgendi, B. Eskofier, D. Abbott, Fast T wave detection calibrated by clinical knowledge with annotation of P and T waves. Sensors 15(7), 17693–17714 (2015)
A. Gacek, W. Pedrycz, ECG Signal Processing, Classification and Interpretation: A Comprehensive Framework of Computational Intelligence (Springer, Berlin, 2011)
S. Guzzetti, M.T.L. Rovere, G.D. Pinna, R. Maestri, E. Borroni, A. Porta, A. Mortara, A. Malliani, Different spectral components of 24 h heart rate variability are related to different modes of death in chronic heart failure. Eur. Heart J. 26(4), 357–362 (2004)
S. Hamdi, A.B. Abdallah, M.H. Bedoui, Real time QRS complex detection using DFA and regular grammar. Biomed. Eng. Online 16(1), 31 (2017)
W. Jiang, W.R. Hathaway, S. McNulty, R.L. Larsen, K.L. Hansley, Y. Zhang, C.M. O’Connor, Ability of heart rate variability to predict prognosis in patients with advanced congestive heart failure. Am. J. Cardiol. 80(6), 808 (1997)
M. Kearney, K. Fox, A. Lee, W. Brooksby, A. Shah, A. Flapan, R. Prescott, R. Andrews, P. Batin, D. Eckberg et al., Predicting sudden death in patients with mild to moderate chronic heart failure. Heart 90(10), 1137–1143 (2004)
B.U. Kohler, C. Hennig, R. Orglmeister, The principles of software QRS detection. IEEE Eng. Med. Biol. Mag. 21(1), 42–57 (2002)
P. Laguna, R. Jané, P. Caminal, Automatic detection of wave boundaries in multilead ECG signals: validation with the CSE database. Comput. Biomed. Res. 27(1), 45–60 (1994)
P. Laguna, R.G. Mark, A. Goldberg, G.B. Moody, A database for evaluation of algorithms for measurement of QT and other waveform intervals in the ECG, in Computers in Cardiology 1997 (IEEE, 1997), pp. 673–676
C. Li, C. Zheng, C. Tai, Detection of ECG characteristic points using wavelet transforms. IEEE Trans. Biomed. Eng. 42(1), 21–28 (1995)
H. Li, X. Wang, L. Chen, E. Li, Denoising and R-peak detection of electrocardiogram signal based on EMD and improved approximate envelope. Circuits Syst. Signal Process. 33(4), 1261–1276 (2014)
Y. Ma, T. Li, Y. Ma, K. Zhan, Novel real-time FPGA-based R-wave detection using lifting wavelet. Circuits Syst. Signal Process. 35(1), 281–299 (2016)
M.S. Manikandan, B. Ramkumar, Straightforward and robust QRS detection algorithm for wearable cardiac monitor. Healthc. Technol. Lett. 1(1), 40–44 (2014)
R. Mark, G. Moody, MIT-BIH Arrhythmia Database Directory (Massachusetts Institute of Technology, Cambridge, 1988)
J.P. Martínez, R. Almeida, S. Olmos, A.P. Rocha, P. Laguna, A wavelet-based ECG delineator: evaluation on standard databases. IEEE Trans. Biomed. Eng. 51(4), 570–581 (2004)
J. Pan, W.J. Tompkins, A real-time QRS detection algorithm. IEEE Trans. Biomed. Eng. 3, 230–236 (1985)
D. Pandit, L. Zhang, C. Liu, S. Chattopadhyay, N. Aslam, C.P. Lim, A lightweight QRS detector for single lead ECG signals using a max–min difference algorithm. Comput. Methods Programs Biomed. 144, 61–75 (2017)
T. Penzel, J. McNames, P. De Chazal, B. Raymond, A. Murray, G. Moody, Systematic comparison of different algorithms for apnoea detection based on electrocardiogram recordings. Med. Biol. Eng. Comput. 40(4), 402–407 (2002)
R. Polikar, The Wavelet Tutorial (Rowan Univeristy, 1996)
C.S. Poon, C.K. Merrill, Decrease of cardiac chaos in congestive heart failure. Nature 389(6650), 492 (1997)
M. Rakshit, S. Das, An efficient wavelet-based automated R-peaks detection method using Hilbert transform. Biocybern. Biomed. Eng. 37(3), 566–577 (2017)
S. Rezk, C. Join, S. El Asmi, An algebraic derivative-based method for R wave detection, In 2011 19th European Signal Processing Conference (IEEE, 2011), pp. 1578–1582
P. Sabherwal, M. Agrawal, L. Singh, Automatic detection of the R peaks in single-lead ECG signal. Circuits Syst. Signal Process. 36(11), 4637–4652 (2017)
P. Sabherwal, L. Singh, M. Agrawal, Aiding the detection of QRS complex in ECG signals by detecting S peaks independently. Cardiovas. Eng. Technol. 9, 469. https://doi.org/10.1007/s13239-018-0355-0
J. Sahambi, S. Tandon, R. Bhatt, Using wavelet transforms for ECG characterization. An on-line digital signal processing system. IEEE Eng. Med. Biol. Mag. 16(1), 77–83 (1997)
U.J. Scholz, A.M. Bianchi, S. Cerutti, S. Kubicki, Vegetative background of sleep: spectral analysis of the heart rate variability. Physiol. Behav. 62(5), 1037–1043 (1997)
T. Sharma, K.K. Sharma, A new method for QRS detection in ECG signals using QRS-preserving filtering techniques. Biomed. Eng./Biomedizinische Technik 63(2), 207–217 (2018)
R.B. Shouldice, L.M. O’brien, C. O’brien, P. de Chazal, D. Gozal, C. Heneghan, Detection of obstructive sleep apnea in pediatric subjects using surface lead electrocardiogram features. Sleep 27(4), 784–792 (2004)
J. Trinder, J. Kleiman, M. Carrington, S. Smith, S. Breen, N. Tan, Y. Kim, Autonomic activity during human sleep as a function of time and sleep stage. J. Sleep Res. 10(4), 253–264 (2001)
L. Zapanta, C.S. Poon, D. White, C. Marcus, E. Katz, Heart rate chaos in obstructive sleep apnea in children, in 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2004 (IEMBS’04), vol. 2 (IEEE, 2004), pp. 3889–3892
H. Zhang, J. Wang, Adaptive sliding-mode observer design for a selective catalytic reduction system of ground-vehicle diesel engines. IEEE/ASME Trans. Mechatron. 21(4), 2027–2038 (2016)
H. Zhang, J. Wang, Active steering actuator fault detection for an automatically-steered electric ground vehicle. IEEE Trans. Veh. Technol. 66(5), 3685–3702 (2017)
Z. Zidelmal, A. Amirou, D. Ould-Abdeslam, A. Moukadem, A. Dieterlen, QRS detection using S-transform and shannon energy. Comput. Methods Programs Biomed. 116(1), 1–9 (2014)
Acknowledgements
The authors would like to thank the anonymous reviewers and editor for their valuable comments which helped in improving this manuscript. The authors would also like to thank the Project Supported by the Government of India, Department of Science and Technology under No. SR/WOS-A/ET-1049/2015(G).
Author information
Authors and Affiliations
Corresponding author
Appendix
Appendix
The complete fusion algorithm is given as,
Rights and permissions
About this article
Cite this article
Sabherwal, P., Agrawal, M. & Singh, L. Fusion Algorithm for Accurate Delineation of QRS Complex in ECG Signal. Circuits Syst Signal Process 38, 1811–1832 (2019). https://doi.org/10.1007/s00034-018-0939-9
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00034-018-0939-9