An Efficient QRS Complex Detection Using Optimally Designed Digital Differentiator
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Heart rate variability (HRV) analysis is considered as a preliminary diagnosis method to check the cardiac health of the human heart. The reliability of the HRV analysis system solely depends on the accuracy of the QRS complex detector. Hence, in this paper, an optimally designed digital differentiator (DD) for precise detection of QRS complex is proposed. The proposed DD is designed by using an efficient evolutionary optimization technique called gases Brownian motion optimization (GBMO) algorithm and is used in the preprocessing stage of the QRS detector. In GBMO algorithm, a balanced trade-off is maintained between both the exploration and the exploitation phases to find the global optimum solution. The electrocardiogram signal is preprocessed by using the proposed DD to generate the feature signals corresponding to the R-peaks only. The detection technique utilizes the principle of Hilbert transform and zeroes crossing detection. The proposed approach is verified against all the first channel records of MIT/BIH arrhythmia database by considering the standard QRS detection performance metrics and produces a sensitivity (Se) of 99.92%, positive predictivity (+P) of 99.92%, detection error rate (DER) of 0.1562%, QRS detection rate of 99.92%, accuracy (Acc) of 99.84%, and F score of 0.9992%. With respect to the standard performance metrics, the proposed QRS detector outperforms all the recently reported QRS detection techniques.
KeywordsDigital differentiator Gases Brownian motion optimization Hilbert transform Electrocardiogram (ECG) QRS detection
This project is financially supported by Science and Engineering Research Board (SERB), Department of Science and Technology, Government of India (Grant No: EEQ/2016/000215).
- 5.D.S. Benitez, P.A. Gaydecki, A. Zaidi, A.P. Fitzpatrick, A new QRS detection algorithm based on the Hilbert transforms. Comput. Cardiol. 27, 379–382 (2000)Google Scholar
- 13.Y. Ferdi, J.P. Herbeuval, A. Charef, B. Boucheham, R wave detection using fractional digital differentiation. ITBM-RBM. 24(5–6), 273–280 (2000)Google Scholar
- 14.A.L. Goldberger, L.A.N. Amaral, L. Glass, J.M. Hausdorff, P.C. Ivanov, R.G. Mark, J.E. Mietus, G.B. Moody, C.K. Peng, H.E. Stanley, PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 101(23), e215–e220 (2000)CrossRefGoogle Scholar
- 42.S.K. Saha, R. Kar, D. Mandal, S.P. Ghoshal, Gravitation search algorithm: application to the optimal IIR filter design. J. King Saud Univ. Eng. Sci. 26(1), 69–81 (2014)Google Scholar