Feature Extraction of ECG Signal Using HHT Algorithm

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
© 2014 by IJETT Journal
Volume-8 Number-8                          
Year of Publication : 2014
Authors : Neha Soorma , Jaikaran Singh , Mukesh Tiwari


Neha Soorma , Jaikaran Singh , Mukesh Tiwari . "Feature Extraction of ECG Signal Using HHT Algorithm", International Journal of Engineering Trends and Technology(IJETT), V8(8),454-460 February 2014. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group


This paper describe the features extraction algorithm for electrocardiogram (ECG) signal using Huang Hilbert Transform and Wavelet Transform. ECG signal for an individual human being is different due to unique heart structure. The purpose of feature extraction of ECG signal would allow successful abnormality detection and efficient prognosis due to heart disorder. Some major important features will be extracted from ECG signals such as amplitude, duration, pre-gradient, post-gradient and so on. Therefore, we need a strong mathematical model to extract such useful parameter. Here an adaptive mathematical analysis model is Hilbert-Huang transform (HHT). This new approach, the Hilbert-Huang transform, is implemented to analyze the non-linear and non-stationary data. It is unique and different from the existing methods of data analysis and does not require an a priori functional basis. The effectiveness of the proposed scheme is verified through the simulation.


[1] Guodong Tang and Aina Qin, “ECG Denoising based on Empirical Mode Decomposition,” 9th International Conference for Young Computer Scientists, pp. 903-906, 2008.
[2] P. Zarychta , F.E. Smith, S.T. King, A.J.Haigh ,A. Klinge, S. Stevens , J. Allen, “Body surface potential mapping for detection of myocardial infarct sites,” in Proc. IEEE comput. Cardiol, pp.181-184, 2007.
[3] Osowski S, Linh TH, “ECG beat recognition using fuzzy hybrid neural network”, IEEE Trans Biom Eng, Vol; 48, pp: 1265-1271, 2001.
[4] P. Chazal, M. O’Dwyer, R.B. Reilly, “Automatic classification of heartbeats using ECG morphology and heartbeat interval feature”, IEEE Trans Biom Eng, Vol; 51, pp: 1196-1206, 2004.
[5] M. Kania, M Fereniee, R. Maniewski, “Wavelet Denoising for multi-lead high resolution ECG signal”, Measurement Science Review, Vol: 7, No: 2, No.4, 2007.
[6] S. Karpagachelvi, M. Arthanari, M. Sivakumar, “Classification of ECG signals using extreme Learning Machine”, Computer and Information Science, Canadian Centre of Science and Education, Vol.4, No. 1; 2011.
[7] N. E. Huang, Z. Shen, S. R. Long, M. C. Wu, H. H. Shih, Q. Zheng, N.-C. Yen, C. C. Tung, and H. H. Liu, “The empirical mode decomposition and the Hilbert spectrum for nonlinear and nonstationary time series analysis”, Proc. Roy. Soc. Lond, vol. A,454,. 903–995, 1998.
[8] Jerritta, S. ; Murugappan, M. ; Wan, K. ; Yaacob, S., "Emotion recognition from electrocardiogram signals using Hilbert Huang Transform" IEEE Conference on Sustainable Utilization and Development in Engineering and Technology, pp. 82 – 86, 2012.
[9] S. C. Saxena, A. Sharma, and S. C. Chaudhary, ?Data compression and feature extraction of ECG signals,? International Journal of Systems Science, vol. 28, no. 5, pp. 483-498, 1997.
[10] B. Castro, D. Kogan, and A. B. Geva, ?ECG feature extraction using optimal mother wavelet, 21st IEEE Convention of the Electrical and Electronic Engineers in Israel, pp. 346-350, 2000.
[11] C. Alexakis, H. O. Nyongesa, R. Saatchi, N. D. Harris, C. Davies, C. Emery, R. H. Ireland, and S. R. Heller, ?Feature Extraction and Classification of Electrocardiogram (ECG) Signals Related to Hypoglycaemia,? Conference on computers in Cardiology, pp. 537-540, IEEE, 2003.
[12] A. B. Ramli, and P. A. Ahmad, ?Correlation analysis for abnormal ECG signal features extraction,? 4th National Conference on Telecommunication Technology, 2003. NCTT 2003 Proceedings, pp. 232-237, 2003.
[13] Mazhar B. Tayel, and Mohamed E. El Bouridy, ?ECG Images Classification using Artificial Neural Network Based on Several Feature Extraction Methods,? IEEE, pp113-115, 2008.
[14] P. Tadejko, and W. Rakowski, ?Mathematical Morphology Based ECG Feature Extraction for the Purpose of Heartbeat Classification,? 6th International Conference on Computer Information Systems and Industrial Management Applications, CISIM `07, pp. 322-327, 2007.
[15] Alan Jovic, and Nikola Bogunovic, ?Feature Extraction for ECG Time- Series Mining based on Chaos Theory,? Proceedings of 29th International Conference on Information Technology Interfaces, 2007.
[16] Ubeyli, and Elif Derya, ?Feature extraction for analysis of ECG signals,? Engineering in Medicine and Biology Society, EMBS 2008. 30th Annual International Conference of the IEEE, pp. 1080-1083, 2008.
[17] S. Z. Fatemian, and D. Hatzinakos, ?A new ECG feature extractor for biometric recognition,?16th International Conference on Digital Signal Processing, pp. 1-6, 2009.
[18] Pedro R. Gomes, Filomena O. Soares, J. H. Correia, C. S. Lima ?ECG Data-Acquisition and Classification System by Using Wavelet-Domain Hidden Markov Models? 32nd Annual International Conference of the IEEE EMBS Buenos Aires, Argentina, August 31- September 4, 2010.
[19] S. M. Jadhav, Dr. S. L. Nalbalwar, Dr. Ashok A. Ghatol, ?modular neural network based arrhythmia classification system using ecg signal data?, in International Journal of Information Technology and Knowledge Management January-June 2011.

ECG; wavelet Transform; HHTs; EMD; HAS; IMF.