This is an open access article published by the IET under the Creative Commons Attribution-NoDerivs License (http://creativecommons.org/licenses/by-nd/3.0/)
Accurate detection of QRS complexes is essential for the investigation of heart rate variability. Several transform techniques have been proposed and extensively used for the detection and analysis of QRS complexes. In this proposed work, the de-noised ECG signal is subjected to a modified S-transform for QRS complex detection.The performance analysis of the proposed work is evaluated using parameters such as sensitivity, positive predictivity and accuracy. The algorithm delivers sensitivity, positive predictivity and overall accuracy of 99.91, 99.91 and 99.77%, respectively. Furthermore, a search back mechanism is employed, which specifies the filtered electrocardiogram (ECG) segment, which was traced for the true R-peak locations. The modified S-transform based QRS complex detection algorithm provides an excellent search back range of only ±2 samples in comparison with other earlier proposed algorithms.
References
-
-
1)
-
17. Ghaffari, A., Golbayani, H., Ghasemi, M.: ‘A new mathematical based QRS detector using continuous wavelet transform’, J. Comput Electr. Eng., 2008, 34, (2), pp. 81–91 (doi: 10.1016/j.compeleceng.2007.10.005).
-
2)
-
3. Zidelmala, Z., Amiroua, A., Ould-Abdeslam, D., et al: ‘QRS detection using S-transform and Shannon energy’, Comput. Methods Programs Biomed., 2014, 116, (1), pp. 1–9 (doi: 10.1016/j.cmpb.2014.04.008).
-
3)
-
14. Ruchita, G., Sharma, A.K.: ‘Detection of QRS complexes of ECG recording based on wavelet transform using Matlab’, Int. J. Eng. Sci., 2010, 2, pp. 3038–3044.
-
4)
-
20. Stockwell, R.G., Mansinha, L., Lowe, R.P.: ‘Localization of the complex spectrum: the S transform’, IEEE Trans. Signal Process., 1996, 44, (4), pp. 998–1001 (doi: 10.1109/78.492555).
-
5)
-
10. Tabakov, S., Lliev, M., Krasteva, V.: ‘Online digital filter and QRS detector applicable in low resource ECG monitoring systems’, Ann. Biomed. Eng., 2008, 36, (11), pp. 1805–1815 (doi: 10.1007/s10439-008-9553-5).
-
6)
-
13. Pan, J., Tompkins, W.J.: ‘A real-time QRS detection algorithm’, IEEE Trans. Biomed. Eng., 1985, 32, (3), pp. 230–236 (doi: 10.1109/TBME.1985.325532).
-
7)
-
22. Moraes, J.C.T.B., Freitas, M.M., Vilani, F.N., et al: ‘A QRS complex detection algorithm using electrocardiogram leads’. Proc. Int. Conf. of Computers in Cardiology, September 2002, pp. 205–208.
-
8)
-
15. Poli, R., Cagnoni, S., Valli, G.: ‘Genetic design of optimum linear and nonlinear QRS detectors’, IEEE Trans. Biomed. Eng., 1995, 42, (11), pp. 1137–1141 (doi: 10.1109/10.469381).
-
9)
-
23. Lewandowski, J., Arochena, H.E., Naguib, R.N.G., et al: ‘A simple real-time QRS detection algorithm utilizing curve-length concept with combined adaptive threshold for electrocardiogram signal classification’. Proc. TENCON IEEE Region 10 Conf., 2012, pp. 1–6.
-
10)
-
16. Biswal, B., Dash, P.K., Panigrahi, B.K.: ‘Power quality disturbance classification using fuzzy C-means algorithm and adaptive particle swarm optimization’, IEEE Trans. Ind. Electron., 2009, 56, (1), pp. 212–220 (doi: 10.1109/TIE.2008.928111).
-
11)
-
8. Afonso, V.X., Tompkins, W.J., Nguyen, T.Q., et al: ‘ECG beat detection using filter banks’, IEEE Trans. Biomed. Eng., 1999, 46, (2), pp. 192–202 (doi: 10.1109/10.740882).
-
12)
-
13. Abibullaev, B., Don Seo, H.: ‘A new QRS detection method using wavelets and artificial neural networks’, J.Med. Syst., 2011, 35, (4), pp. 683–691 (doi: 10.1007/s10916-009-9405-3).
-
13)
-
1. Natalia, M.A., Deng, Z.D., Poon, C.S.: ‘Analysis of first-derivative based QRS detection algorithms’, IEEE Trans. Biomed. Eng., 2008, 55, (2), pp. 478–484 (doi: 10.1109/TBME.2007.912658).
-
14)
-
9. Okada, M.: ‘A digital filter for the QRS complex detection’, IEEE Trans. Biomed. Eng., 1979, 26, (12), pp. 700–703 (doi: 10.1109/TBME.1979.326461).
-
15)
-
19. Mehta, S.S., Ligayat, N.S.: ‘Comparative study of QRS detection in single lead and 12-lead ECG based on entropy and combined entropy criteria using support vector machine’, J. Theory Appl. Inf. Technol., 2007, 3, (2), pp. 8–18.
-
16)
-
16. Chen, S.W., Chen, C.H., Chan, H.L.: ‘A real-time QRS method based on moving-averaging incorporating with wavelet de-noising’, Comput. Method Program Biomed., 2006, 82, (3), pp. 187–195 (doi: 10.1016/j.cmpb.2005.11.012).
-
17)
-
25. Biswal, B., Dash, P.K., Biswal, M.: ‘Time frequency analysis and FPGA implementation of modified S-transform for de-noising’, Int. J. Signal Process. Image Process. Pattern Recognit., 2011, 4, (2), pp. 119–135.
-
18)
-
11. Benitez, D., Gaydecki, P.A., Zaidi, A., et al: ‘The use of the Hilbert transform in ECG signal analysis’, Comput. Biol. Med., 2001, 31, (5), pp. 399–406 (doi: 10.1016/S0010-4825(01)00009-9).
-
19)
-
15. Zidelmal, Z., Amirou, A., Adnane, M., et al: ‘QRS detection using wavelet coefficients’, Comput. Method Program Biomed., 2012, 107, (3), pp. 490–496 (doi: 10.1016/j.cmpb.2011.12.004).
-
20)
-
12. Benitez, D.S., Gaydecki, P.A., Zaidi, A., et al: ‘A new QRS detection algorithm based on the Hilbert transform’. Proc. Int. Conf. of Computers in Cardiology, September 2000, pp. 379–382.
-
21)
-
23. Köhler, B.U., Hennig, C., Orglmeister, R.: ‘The principles of software QRS detection’, IEEE Eng. Med. Biol. Mag., 2002, 21, (1), pp. 42–57 (doi: 10.1109/51.993193).
-
22)
-
2. Stockwell, R.G.: ‘Why use the S-transform?, AMS pseudo-differential operators: partial differential equations and time–frequency analysis’, Fields Institute Commun., 2007, 52, pp. 279–309.
-
23)
-
26. Mark, R., Moody, G.: ‘MIT-BIH arrhythmia database’. .
-
24)
-
21. Meyer, C., Gavela, J.F., Harris, M.: ‘Combining algorithms in automatic detection of QRS complexes in ECG signals’, IEEE Trans. Inf. Technol., 2006, 10, (3), pp. 468–475 (doi: 10.1109/TITB.2006.875662).
-
25)
-
9. Hamilton, P.S., Tompkins, W.J.: ‘Quantitative investigation of QRS detection rules using the MIT/BIH arrhythmia database’, IEEE Trans. Biomed. Eng., 1986, BME 33, pp. 1157–1165 (doi: 10.1109/TBME.1986.325695).
-
26)
-
20. Coast, D.A., Stern, R.M., Cano, G.G., et al: ‘An approach to cardiac arrhythmia analysis using hidden Markov models’, IEEE Trans. Biomed. Eng., 1990, 37, (9), pp. 826–836 (doi: 10.1109/10.58593).
http://iet.metastore.ingenta.com/content/journals/10.1049/htl.2016.0078
Related content
content/journals/10.1049/htl.2016.0078
pub_keyword,iet_inspecKeyword,pub_concept
6
6