International Journal of Biomedical Science and Engineering
Volume 3, Issue 4, August 2015, Pages: 49-61
Received: Apr. 13, 2015;
Accepted: Jun. 25, 2015;
Published: Jul. 7, 2015
Views 5906 Downloads 149
Hla Myo Tun, Department of Electronic Engineering, Mandalay Technological University, Mandalay, Myanmar
Win Khine Moe, Department of Research and Innovation, Ministry of Science and Technology, Yangon, Myanmar
Zaw Min Naing, Department of Research and Innovation, Ministry of Science and Technology, Yangon, Myanmar
Digital signal processing and data analysis are very often used methods in a biomedical engineering research. In this work, the descriptions of two detection algorithms for ECG characteristic points are enclosed. The detection algorithms presented in this work are based on Pan and Tompkins’ algorithm and wavelet transform for signal de-noising and detection of QRS complexes. In the first approach, efficient designed filters are focused on removing supply network 50 Hz frequency and baseline drift due to breathing. A special digital bandpass filter reduces false detection caused by the various types of interference present in ECG signals. The next process after filtering is differentiation followed by squaring, and then integration. The integrated signal is detected by thresholding for QRS complex. P wave and T wave detection are performed by using detected QRS complexes. MATLAB program is developed for the characteristic points’ detection. The algorithm for peak detection case is modified and it is applied to show ECG characteristic points. Wavelet Transform (WT) method is used for peak detection in this work. Wavelet based detection algorithms for one-dimensional signals are presented along with the results of detection ECG data. Firstly, ECG signals are decomposed by the Discrete Wavelet Transform (DWT). The decomposed signals are detected by thresholding for QRS complex. Detection of the QRS complex is the most important task in automatic ECG signal analysis. Finally, P wave and T wave detection are performed by using detected QRS complexes. Different types of algorithms are applied and evaluated their performance with sensitivity (Se), positive predictive (+P).
Hla Myo Tun,
Win Khine Moe,
Zaw Min Naing,
Analysis of Computer Aided Identification System for ECG Characteristic Points, International Journal of Biomedical Science and Engineering.
Vol. 3, No. 4,
2015, pp. 49-61.
B. U. Kohler, C. Henning, and R. Orglmeister, “The principles of software QRS detection,” IEEE Eng. in Med. and Bio., 2002, pp. 42-47.
F. Gritzali, “Detection of the P and T-waves in an ECG,” Comp. and Biomed. Research, vol. 22, 1989, pp. 83-92.
C. Li, C. Zheng, and C. Tai, “Detection of ECG Characteristic points using Wavelet Transform,” IEEE Trans. Biomed. Eng. Vol. 42, 1995, pp.21-28.
Kohler, B.U., Henning, C. and R. Orglmeister, The principles of software QRS detection, IEEE Eng. Med. Biol. Vol. 21, pp. 42–57, (2002).
Sahambi J. S. and Tandon S. N. et al. An Automated Approach to Beat-by-Beat QT-Interval Analysis. IEEE Engineering In Medicine And Biology. (2000).
HosseiniH.G. Computer-aided Diagnosis of Cardiac Events. Flinders vUniversity, Australia.(2001)
Harris N. D. and Ireland R. H. et al. Can changes in QT interval used to predictthe onset of hypoglycemia in typeldiabetes. IEEE Computer in Cardiology,(2001).
Gonzalez R. and Fernadez R. etal.,Real-time QT interval Measuremen,. 22nd Annual EMBS International Conference, Chicago, July 23-28, (2000).
A. Goutas, Y. Ferdi, J. P. Herbeuval, M. Boudraa, and B.Boucheham, “Digital fractional order differentiation-based algorithm for P and T-waves detection and delineation ” ITBM-RBM, vol. 26, 2005, pp. 127-132.
Y. Sun, K. L. Chan, and S. M. Krishnan, “Characteristic wave detection in ECG using morphological transform,” BMC Cardiovascular Disorders, 2005 available on http://www.biomedcentral.com/1471-2261/5/28
J. Dehmeshki, J. Chen, M. V. Casique, and M. Karakoy, “Classification of lung data by sampling and support vector machine,” Proc. of 26th IEEE EMBS annual international conference, San Francisco, CA, USA, 2004, pp. 3194-3197.
F. Chu, G. Jin, and L. Wang, “Cancer diagnosis and protein secondary structure prediction using support vector machine,” StudFuss, vol.177, 2005, pp. 343-363.
J. M. Roig, R. V. Galiano, F. J. Chorro-Gasco, and A. Cebrian, “Support vector machine for arrhythmia discrimination with wavelet transform based feature selection,” Computers in Cardiology, vol. 27, 2000, pp. 407-410.
S. Jankowski, A. Oreziak, A. Skorupski, H.Kowalski, Z. Szymanski, and E. Piatkowska-Janko, “Computer-aided morphological analysis of holter ECG recordings based on support vector learning system,” Computers in Cardiology, vol. 30, 2003, pp. 597-600.
S. Jankowski, and A. Oreziak, “Learning system for Computer-aided ECG analysis based on support vector machines,” I.J. of Bioelectromagnetism, vol. 5, 2003, pp.175-176.
S. Osowski, L. T. Hoai, and T. Markiewicz, “Support vector machines based expert system for reliable heartbeat recognition,” IEEE Trans. Biomed. Eng. Vol. 51, 2004, pp.582-589.
N. Acir, “Classification of ECG beats by using a fast least square support vector machines with a dynamic programming feature selection algorithm,” Neural Comput. and App., vol. 14, 2005, pp. 299-309.
M. H. Song, J. Lee, S. P. Cho, K. J. Lee, and S. K. Yoo, “Support vector machines based arrhythmia classification using reduced features,” I. J. of Control Auto. and Sys., vol.3, 2005, pp. 571-579.
N. Acir, “A support vector machine classifier algorithm based on a perturbation method and its application to ECG beat recognition systems,” Expert Sys. with App., vol. 31, 2006, pp. 150-158.
V. Vapnik, Statistical Learning Theory, Wiley, New York, 1998.
C. J. C. Burges, “A tutorial on support vector machines for pattern recognition, Data mining and knowledge discovery,” vol. 2, 1998, pp.955-971.
G. S. Furno, and W. J. Tompkins, “A learning filter for removing noise interference,” IEEE Trans. Biomed. Eng., vol. 30, 1983, pp. 234-235.
S. S. Mehta, and N. S. Lingayat, “Development of entropy based algorithm for cardiac beat detection in 12-lead electrocardiogram” Signal Processing, in Press.
C. C. Chang, and C. J. Lin, LIBSVM: A library for support vector machines, Technical report, National Taiwan University, Taiwan, 2004.
H. T. Lin, and C. J. Lin, “A study on sigmoid kernels for SVM and the training of non-PSD kernels by SMO-type methods,” Technical report, Department of Computer Science and Information Engineering, National Taiwan University, Taiwan, 2003.
C. W. Hsu, C. C. Chang, and C. J. Lin, “A practical guide to support vector classification,” Technical report, Department of Computer Science and Information Engineering, National Taiwan University, Taiwan, 2003.