ECG Signal Analysis: Different Approaches
|International Journal of Engineering Trends and Technology (IJETT)||
|© 2014 by IJETT Journal|
|Year of Publication : 2014|
|Authors : S. Thulasi Prasad , S. Varadarajan
S. Thulasi Prasad , S. Varadarajan, Article : ECG Signal Analysis: Different Approaches, International Journal of Engineering Trends and Technology(IJETT), 7(5),212-216, published by seventh sense research group
In recent years scientists and engineers are facing several challenges in solving biomedical problems and making Digital Signal Processing as an essential and effective pedagogical approach to solve a problem of detecting selected arrhythmia conditions from a patient’s electrocardiograph (ECG) signals. The detection of QRS complex has many clinical applications as it marks the beginning of the left ventricular contraction. A lot of possible heart malfunctions such as cardiac arrhythmias, transient ischemic episodes and silent myocardial ischemia or failures will be slow while monitoring of ECG signal in real-time during normal activity. Introducing an efficient method for arrhythmia detection can be very useful for better conceptual understanding of signal processing. In this paper, we discussed two methods to clean ECG signal corrupted by noise and to extract required parameters for detecting arrhythmia condition. One method is Hilbert Transform method and another method is Filter Bank method. These methods involve using filter techniques, algorithms of finding peaks & valleys, local maxima & minima etc, for determining R peaks, R-R intervals and QRS complexes.
 N.Goldschlager (June 1989), Principles of Clinical Electrocardiography, Appleton & Lange, 13th edition, ISBN 978-083-8579-510, Connecticut, USA
 T.B. Garcia and N.E. Holtz, 12-lead ECG: The Art of Interpretation, (Jones & Bartlett Publ. Sudbury, MA.236 pp. ISBN 0-7637-1284-1).
 Ruch TC, Patton HD (eds.) (1982): Physiology and Biophysics, 20th ed., 1242 pp. W. B. Saunders, Philadelphia.
 Williams PL, Warwick R (eds.) (1989): Gray`s Anatomy, 37th ed., 1598 pp. Churchill Livingstone, Edinburgh.
 A. Camm, T. L. uscher, and P. Serruys, The ESC Textbook of cardiovascular Medicin,. OUP Oxford, 2009.
 P. S. Hamilton, W. J. Tompkins, (1986). Quantitative Investigation of QRS Detection Rules Using MIT/BIH Arrhythmia Database, IEEE Transactions on Biomedical Engineering, Vol.31, No.3, (March 2007), pp. 1157-1165, ISSN 0018-9294.
 Y. H. Hu, W. J. Tompkins, J. L. Urrusti, and V. X. Afonso, Applications of artificial neural networks for ECG signal detection and classification. Electrocardiology, 1993; 26 (Suppl.): 66-73.
 J. Parák, and J. Havlík, ECG Signal Processing and Heart Rate Frequency Detection Methods, In Proceedings of Technical Computing Prague, 2011; 8.11.2011.
 J. Pucik, J. Cocherova, Elena: Bio-signal analysis, STU Publishing House Bratislava, 2008, 121, pp-ISBN 978-80-227-2833-1.
 H. S. Malvar, Signal Processing with Lapped Transforms ( Norwood, MA: Artech House, 1992).
 P. S. Hamilton and W. J. Tompkins, Quantitative investigation of QRS detection rules using the MIT/BIH arrhythmia database, IEEE Trans. Biomed. Eng., vol. BME-33, pp. 1157–1165, 1986.
 J. R. Glover. Adaptive noise canceling applied to sinusoidal interferences, IEEE Trans. Acoustical., Speech, Signal Processing, and ASSP-25 (12):484–491, Dec. 1977.
 D.R Morgan, An analysis of multiple correlation cancellation loops with a filter in the auxiliary path, IEEE Transactions on Acoustics, Speech and Signal Processing, ASSP-28(4):454–467, August 1980.
 C.C. Boucher, S.J. Elliott, and P.A. Nelson, Effect of errors in the plant model on the performance of algorithms for adaptive feedforward control. Proceedings of Institution of Electrical Engineers, volume 138, pages 313–319, 1991. Nguyen, Truong T., and Soontorn Oraintara, Multidimensional filter banks design by direct optimization, IEEE International Symposium on Circuits and Systems, pp. 1090-1093. May, 2005.
ECG, QRS, Arrhythmia, SA node, AV node, Filter Bank, Adaptive LMS filter, Downsampling, MATLAB.