Aiding the Detection of QRS Complex in ECG Signals by Detecting S Peaks Independently
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In this paper, a novel algorithm for the accurate detection of QRS complex by combining the independent detection of R and S peaks, using fusion algorithm is proposed. R peak detection has been extensively studied and is being used to detect the QRS complex. Whereas, S peaks, which is also part of QRS complex can be independently detected to aid the detection of QRS complex. In this paper, we suggest a method to first estimate S peak from raw ECG signal and then use them to aid the detection of QRS complex. The amplitude of S peak in ECG signal is relatively weak than corresponding R peak, which is traditionally used for the detection of QRS complex, therefore, an appropriate digital filter is designed to enhance the S peaks. These enhanced S peaks are then detected by adaptive thresholding. The algorithm is validated on all the signals of MIT-BIH arrhythmia database and noise stress database taken from physionet.org. The algorithm performs reasonably well even for the signals highly corrupted by noise. The algorithm performance is confirmed by sensitivity and positive predictivity of 99.99% and the detection accuracy of 99.98% for QRS complex detection. The number of false positives and false negatives resulted while analysis has been drastically reduced to 80 and 42 against the 98 and 84 the best results reported so far.
KeywordsQRS complex S peak Wavelet transform Fusion algorithm
This study was funded by Government of India, Ministry of Science and Technology, Department of Science and Technology, (Grant Number : SR/WOS-A/ET-1049/2015(G)).
Conflict of interest
Pooja Sabherwal has received research grants from Department of Science and Technology, India. Dr Latika Singh declares that she has no conflict of interest. Dr Monika Agrawal declares that she has no conflict of interest.
This article does not contain any studies with animals and humans performed by any of the authors. All the analysis of the algorithm has been done on the freely available data from physionet.org.26
- 2.Banerjee, S. and M. Mitra. ECG feature extraction and classification of anteroseptal myocardial infarction and normal subjects using discrete wavelet transform. In: Proceedings of IEEE International Conference on Systems in Medicine and Biology, IIT Kharagpur, 2010, pp. 55–59.Google Scholar
- 4.Beraza, I. and I. Romeroand. Comparative study of algorithms for ECG segmentation. Biomed. Signal Process. Control 34:166–173, 2017. https://doi.org/10.1016/j.bspc.2017.01.013.
- 6.Chang, R. C.H., H. L. Chen, and C. H. Lin. Design of a low-complexity real-time arrhythmia detection system. J. Signal Process. Syst. (2017). https://doi.org/10.1007/s11265-017-1221-2
- 7.Chiarugi, F., V. Sakkalis, D. Emmanouilidou, T. Krontiris, M. Varaniniand, and I. Tollis. Adaptive threshold QRS detector with best channel selection based on a noise rating system. Comput. Cardiol. 34:157–164, 2007.Google Scholar
- 9.Elgendi, M., M. Jonkman, and F. De.Boer. R wave detection using Coiflets wavelets. In: IEEE 35th Annual Northeast Bioengineering Conference, Boston, MA, 2009, pp. 1–2.Google Scholar
- 11.Gacek, A. and W. Pedrycz. ECG Signal Processing, Classification and Interpretation: A Comprehensive Framework of Computational Intelligence. London: Springer, p. 108, 2011. ISBN 978-0-85729-867-6.Google Scholar
- 12.Hamdi, S., A. B. Abdallah, and M. H. Bedoui. Real time QRS complex detection using DFA and regular grammar. BioMed Eng. OnLine 16:31, 2017. https://doi.org/10.1186/s12938-017-0322-2.
- 13.Hamilton, P. S. and W. J. Tompkins. A real time QRS detection algorithm. IEEE Trans. Biomed. Eng. 32(3):230–236 1985.Google Scholar
- 14.Hongyan, X. and H. Minsong. A new QRS detection algorithm based on empirical mode decomposition. In: Proceedings of the 2nd International Conference on Bioinformatics and Biomedical Engineering, 2008, pp. 693-696.Google Scholar
- 15.Hossein Rabbani, M., E. Parsa Mahjoob, A. Farahabadi, R. Farahabadi. Peak detection in electrocardiogram signal based on an optimal combination of wavelet transform, Hilbert transform and adaptive thresholding. J. Med. Signals Sens. 1(2):91–98, 2011.Google Scholar
- 16.Hu, Y. H., W. J. Tompkins, J. L. Urrusti, and V. X. Afonso. Applications of artificial neural networks for ECG signal detection and classification. J. Electrocardiol. 26:66–73, 1993.Google Scholar
- 17.James, J., J. H. Park, V. C. M. Leung, C.-L. Wang, and T. Shon. Future information technology, application and service. In: Future Tech 2012 Proceedings, Vol. 1.Google Scholar
- 18.Kaplan, D. Simultaneous QRS detection and feature extraction using simple matched filter basis functions. In: Proceedings of Computers in Cardiology, 1990, pp. 503–506.Google Scholar
- 19.Kohler, B. U., C. Hennig, and R. Orglmeister. QRS detection using zero crossing counts. Prog. Biomed. Res. 8(3):138–145, 2003.Google Scholar
- 20.Kumar, M., R. B. Pachori, and U. R. Acharya. Characterization of coronary artery disease using flexible analytic wavelet transform applied on ECG signals. Biomed. Signal Process. Control 31:301–308, 2017. https://doi.org/10.1016/j.bspc.2016.08.018.
- 23.Ma, Y., T. Li, Y. Ma, and K. Zhan. Novel real time FPGA based R wave detection using lifting wavelet. Circ. Syst. Signal Process. (2015). ISSN 0278-081X, https://doi.org/10.1007/s00034-015-0063-z
- 24.Manikandan, M. S., and K. P. Soman. A novel method for detecting R-peaks in electrocardiogram (ECG) signal. Biomed. Signal Process. Control 7(2):118–128, 2012.Google Scholar
- 26.Massachusetts Institute of Technology. MIT-BIH Arrythmia Database. Cambridge, MA: Massachusetts Institute of Technology, Biomedical Engineering Center, 1992. www.physionet.org/physiobank/databse/html/mitdbdir/mitdbdir.htm.Google Scholar
- 30.Polikar, R. The Wavelet Tutorial. http://users.rowan.edu/ polikar/Wavelets/WTpart1.html.Google Scholar
- 31.Pooyan, M. and F. Akhoondi. Providing an efficient algorithm for finding R peaks in ECG signals and detecting ventricular abnormalities with morphological features. J. Med. Signals Sens. 6(4):218–223, 2016.Google Scholar
- 33.Sabarimalai Manikandan, M. and B. Ramkumar. Straightforward and robust QRS detection algorithm for wearable cardiac monitor. Healthcare Technol. Lett. 1(1):40-44, 2014.Google Scholar
- 34.Sabherwal, P., M. Agrawal, and L. Singh. Automatic detection of the R peaks in single lead ECG signal. J. Circ. Syst. Signal Process. (2017). https://doi.org/10.1007/s00034-017-0537-2.
- 35.Sachin Kumar, S., N. Mohan, P. Prabaharan, and K. P. Soman. Total variation denoising based approach for R-peak detection in ECG signals. In: 6th International Conference on Advances in Computing and Communications, ICACC 2016, 6–8 September 2016, Cochin, India.Google Scholar
- 39.Thiamchoo, N. and P. Phukpattaranont. Application of wavelet transform and Shannon energy on R peak detection algorithm. In: 13th International IEEE Conference on conference Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), 2016.Google Scholar