FCM algorithm is used to divide the ECG signal into QRS and non-QRS region. This paper presents a simple technique for automatic detection of cardiac beat (QRS-complex) in Electrocardiogram (ECG) using Fuzzy C-Means (FCM) clustering algorithm. The power line interference and baseline wander present in the ECG signal is removed using digital filtering techniques. Absolute derivative of the filtered ECG signal is calculated to enhance the QRS-complexes in the ECG signal. Algorithm performance is validated using original single lead ECG recordings from the CSE ECG database. Detection rate of 98.32% with 1.68% of false negative (FN) and 0.08% of false positive (FP) has been achieved.

1. B. U. Kohler, C. Henning, and R. Orglmeister, “The principles of software QRS detection,” IEEE Eng. in Med. and Bio., vol. 21, pp. 42-47, 2002.
2. F. Gritzali, “Towards a generalized scheme for QRS Detection in ECG waveforms,” in Signal Processing, vol. 15, 1988, pp. 183-192.
3. O. Pahlm and L. Sornmo, “Software QRS detection in ambulatory monitoring- A review,” Med. Biol. Eng. Comp., vol. 22, pp. 289-297, 1984.
4. G. M. Friesen, T. C. Jannett, M. A. Jadallah, S. L. Yates, S. R. Quint, and H. T. Nagle, “ A Comparison of noise sensitivity of nine QRS detection algorithms”, IEEE trans on Biomed. Engg., vol. 37, pp. 85-98, 1990.
5. P. J. M. Fard, M. H. Moradi, and M. R. Tajvidi, “A novel approach in R peak detection using Hybrid Complex Wavelet,” Int. J. Card., 2007, doi:10.1016/j.ijcard.2006. 11.136.
6. M. B. Messaoud, “On the algorithm for QRS complex localization in Electrocardiogram”, I. J. of Computer Science and Network Security, vol. 7, pp. 28-33, 2007.
7. M. P. S. Chawla, H. K. Verma, V. Kumar, “A new statistical PCA-ICA algorithm for location of R-peaks in ECG,” Int. J. Card., 2007.
8. A. Ghaffari, H. Golbayni, and M. Ghasemi, “A new mathematical based QRS detectorsusing continous wavelet transform,” Computers and Electrical Engg. 2007.
9. F. Zhang and Y. Lian, “Electrocardiogram QRS detectionusing multiscale filtering using wavelet transform,” Proc. 29th Annual Int. Conf. of the IEEE EMBS, Lyon, France, pp. 3196-3199, Aug. 2007.
10. S. S. Mehta and N. S. Lingayat, “Development of Entropy based algorithm for cardiac beat detection in 12-lead electrocardiogram,” Sig. Proc., vol. 87, pp. 3190-3201, 2007.
11. S. S. Mehta and N. S. Lingayat, “Combined Entropy based method for detection of QRS complexes in 12-lead electrocardiogram using SVM,” Comp. in Biol. and Med, vol. 38, pp. 138-145, 2008.
12. S. S. Mehta and N. S. Lingayat “SVM-based algorithm for recognition of QRS complexes in Electrocardiogram,” ITBM-RBM, vol. 29, pp 310-317, (2008).
13. V. S. Chouhan and S. S. Mehta, “Detection of QRS-complexes in 12-lead ECG using adaptive quantized threshold,” IJCSNS International Journal of Computer Science and Network Security, vol.8, no.1, pp. 155-163, 2008
14. J. C. Dunn, “A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters,” J. Cybernet., vol. 3, pp. 32-57, 1974.
15. J. C. Bezdek, “Pattern Recognition with fuzzy objective function algorithms,” Plenum Press, New York, 1981.
16. J. L. Willems, P. Arnaud, J. H. Van Bemmel, P. J. Bourdillon, R. Degani, B. Denis, I. Graham, F. M. A. Harms, P. W. Macfarlane, G. Mazzocca, J. Meyer, and C. Zywietz, “A reference database for multilead electrocardiographic computer measurement programs,” J. Amer. Coll. Cardiol., vol. 10, pp. 1313-1321, 1987.
17. J. A. Van Alste, and T.S. Schilder, “Removal of base-line wander and power line interference from the ECG by an efficient FIR filter with a reduced number of taps,” IEEE Trans. Biomed. Eng., vol. 32, pp.1052- 1059, 1985.

ECG QRS-complex ECG detection FCM