Call for Paper - August 2019 Edition
IJCA solicits original research papers for the August 2019 Edition. Last date of manuscript submission is July 20, 2019. Read More

Features Extraction of ECG signal for Detection of Cardiac Arrhythmias

IJCA Proceedings on National Conference on Innovative Paradigms in Engineering and Technology (NCIPET 2012)
© 2012 by IJCA Journal
ncipet - Number 8
Year of Publication: 2012
N.G. Bawane

P.D.Khandait, N G Bawane and S.S.Limaye. Article: Features Extraction of ECG signal for Detection of Cardiac Arrhythmias. IJCA Proceedings on National Conference on Innovative Paradigms in Engineering and Technology (NCIPET 2012) ncipet(8):6-10, March 2012. Full text available. BibTeX

	author = {P.D.Khandait and N.G. Bawane and S.S.Limaye},
	title = {Article: Features Extraction of ECG signal for Detection of Cardiac Arrhythmias},
	journal = {IJCA Proceedings on National Conference on Innovative Paradigms in Engineering and Technology (NCIPET 2012)},
	year = {2012},
	volume = {ncipet},
	number = {8},
	pages = {6-10},
	month = {March},
	note = {Full text available}


Electrocardiogram (ECG) is the record of the heart muscle electric impulses. Received and processed ECG signal could be analyzed, and results could be used for detection and diagnostics of cardiovascular diseases (CVD).One of the important cardiovascular diseases is arrhythmia.This paper deals with improved ECG signal features Extraction using Wavelet Transform Techniques which may be employed for Arrhythmia detection. This improvement is based on suitable choice of features in evaluating and predicting life threatening Ventricular Arrhythmia. Analyzing electrocardiographic signals (ECG) includes not only inspection of P, QRS and T waves, but also the causal relations they have and the temporal sequences they build within long observation periods. Wavelet-transform is used for effective feature extraction which may be considered for the classifier model. In a first step, QRS complexes are detected. Then, each QRS is delineated by detecting and identifying the peaks of the individual waves, as well as the complex onset and end. Finally, the determination of P and T wave peaks, onsets and ends is performed. Analysis is carried out using MATLAB Software. We evaluated the algorithm on MIT-BIH Arrhythmia Database which is manually annotated and developed for validation purposes. Features based on the ECG waveform shape and heart beat intervals may be used as inputs to the classifiers. A correct beat classification accuracy of 98.17% is achieved which is a significant improvement


  • Prabhakar D. Khandait, N.G. Bawane and S.S. Limaye, “Wavelet Transformation , Artificial Neural Network and Neuro-Fuzzy Approach for CVD Detection and Voltage(mV) Classification – An Overview”, First IEEE International Conference on Emerging Trends in Engineering and Technology(ICETET-08),pp. 612-617, DOI 10.1109/ICETET.2008.228.
  • R. Hoekema, G. J. H. Uijen, and A. V. Oosterom,“Geometrical aspects of the interindividual variability of multilead ECG recordings,” IEEE Trans.Biomed. Eng., vol. 48, no. 5, pp. 551–559, May 2001.
  • J. Pan and W.J. Tompkins, “A real-time QRS detection algorithm”, IEEE Trans. Biomed. Eng., vol. 32, pp. 230–236, 1985.
  • V.X. Afonso, W.J. Tompkins, T.Q. Nguyen, and S. Luo, “ECG beat detection using filter banks”, IEEE Trans. Biomed. Eng., vol. 46, pp.192–202, 1999.
  • J. Fraden and M.R. Neumann, “QRS wave detection”, Med. Biol. Eng. Comput., vol. 18, pp. 125–132, 1980.
  • W.P. Holsinger, K.M. Kempner, and M.H. Miller, “A QRS preprocessor based on digital differentiation”, IEEE Trans. Biomed. Eng., vol. 18, pp. 121–217, May 1971.
  • M. Bahoura, M. Hassani, and M. Hubin, “DSP implementation of wavelet transform for real time ECG wave forms detection and heart rate analysis”, Comput. Methods Programs Biomed. vol. 52, no. 1, pp. 35– 44, 1997.
  • S. A. Israel et al. “ECG to identify individuals”. Pat. Rec. 38:133-142, 2005.
  • Y.H. Hu, W.J. Tompkins, J.L. Urrusti, and V.X. Afonso, “Applications of artificial neural networks for ECG signal detection and classification”, J.Electrocardiology, vol. 26 (Suppl.), pp. 66-73, 1993.
  • G. Vijaya, V. Kumar, and H.K. Verma, “ANN-based QRS-complex analysis of ECG,” J. Med. Eng. Technol., vol. 22, no. 4, pp. 160-167
  • Q. Xue, Y. H. Hu, and W. J. Tompkins, “Neural-network-based adaptive matched filtering for QRS detection”, IEEE Trans. Biomed. Eng., vol. 39, pp. 317-329, 1992.
  • R. Poli, S. Cagnoni, and G. Valli, “Genetic design of optimum linear and nonlinear QRS detectors”, IEEE Trans. Biomed. Eng., vol. 42, pp. 1137- 1141, 1995.
  • E. Skordalakis, “Syntactic ECG processing: a review”, Pattern Recognition., vol. 19, no. 4, pp. 305–313, 1986.
  • S. K. Zhou, J.-T. Wang and J.-R. Xu, “The real-time detection of QRScomplex using the envelop of ECG”, in Proc. 10th Annu. Int. Conf.,IEEE Engineering in Medicine and Biology Society, New Orleans, LA,1988, p. 38.
  • R. V. Andreao, B. Dorizzi, and J. Boudy, “ECG signal analysis through hidden Markov models”, IEEE Transactions on Biomedical Engineering, vol. 53, no. 8, pp. 1541–1549, 2006.
  • B.U. Kohler, C. Hennig, and R. Orglmeister, “The principles of software QRS detection”, Engineering in Medicine and Biology Magazine, IEEE, vol. 21, pp. 42 – 57, Jan.-Feb. 2002
  • S. S. Mehta et al. “Computer-aided interpretation of ECG for diagnostics”, Int. Journal of System Science, 43-58, 1996.
  • S. W. Chen et al. “A real time QRS detection method based on moving averaging incorporating with wavelet denoising”, Comp. Methods and Progs. in Biomed. 82:187-195, 2006.
  • L. Y. Shyu, Y. H. Wu, and W. C. Hu, “Using wavelet transform and fuzzy neural network for VPC detection from the holter ECG,” IEEE Trans.Biomed. Eng., vol. 51, no. 7, pp. 1269–1273, Jul. 2004.
  • O. T. Inan, L. Giovangrandi, and G. T. A.Kovacs, “Robust neural-networkbased classification of premature ventricularcontractions using wavelet transform and timing interval features,” IEEE Trans. Biomed. Eng., vol. 53, no. 12, pp. 2507–2515, Dec. 2006.
  • P. de Chazal, C. Heneghan, E. Sheridan, R.Reilly, P. Nolan, M. O'Malley, “Automated Processing of the Single-Lead Electrocardiogram for the Detection of Obstructive Sleep Apnoea”, IEEE Trans. Biomed.Eng., 50( 6): 686-689, 2003.
  • S. Z. Mahmoodabadi, A. Ahmadian, and M. D. Abolhasani, “ECG Feature Extraction using Daubechies Wavelets”, Proceedings of the fifth IASTED International conference on Visualization, Imaging and Image Processing, pp. 343-348, 2005.
  • N.V.Thakor, J.G.Webster and W.J.Tompkins, “Estimation of QRS complex power spectra for design of a QRS filter”, IEEE Transactions on Biomedical Engineering, vol. BME-31, no. 11,pp. 702-706, 1986, pp. 702-706, Nov,1986.