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Rule Based Identification of Cardiac Arrhythmias from Enhanced ECG Signals Using Multi-Scale PCA
Sharmila Vallem, Ette Hari Krishna, Komalla Ashoka Reddy
Pages - 117 - 130     |    Revised - 15-08-2013     |    Published - 15-09-2013
Volume - 7   Issue - 2    |    Publication Date - September 2013  Table of Contents
ECG, Wavelet Transform, Principle Component Analysis, Arrhythmia Detection.
The detection of abnormal cardiac rhythms, automatic discrimination from rhythmic heart activity, became a thrust area in clinical research. Arrhythmia detection is possible by analyzing the electrocardiogram (ECG) signal features. The presence of interference signals, like power line interference (PLI), Electromyogram (EMG) and baseline drift interferences, could cause serious problems during the recording of ECG signals. Many a time, they pose problem in modern control and signal processing applications by being narrow in-band interference near the frequencies carrying crucial information. This paper presents an approach for ECG signal enhancement by combining the attractive properties of principal component analysis (PCA) and wavelets, resulting in multi-scale PCA. In Multi-Scale Principal Component Analysis (MSPCA), the PCA’s ability to decorrelate the variables by extracting a linear relationship and wavelet analysis are utilized. MSPCA method effectively processed the noisy ECG signal and enhanced signal features are used for clear identification of arrhythmias. In MSPCA, the principal components of the wavelet coefficients of the ECG data at each scale are computed first and are then combined at relevant scales. Statistical measures computed in terms of root mean square deviation (RMSD), root mean square error (RMSE), root mean square variation (RMSV) and improvement in signal to noise ratio (SNRI) revealed that the Daubechies based MSPCA outperformed the basic wavelet based processing for ECG signal enhancement. With enhanced signal features obtained after MSPCA processing, the detectable measures, QRS duration and R-R interval are evaluated. By using the rule base technique, projecting the detectable measures on a two dimensional area, various arrhythmias are detected depending upon the beat falling into particular place of the two dimensional area.
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1 Rodríguez, R., Mexicano, A., Bila, J., Cervantes, S., & Ponce, R. (2015). Feature extraction of electrocardiogram signals by applying adaptive threshold and principal component analysis. Journal of Applied Research and Technology, 13(2), 261-269.
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1 J. C. Huhta and J. G. Webster, “60-Hz interference in electrocardiograph,” IEEE Trans.Biomed. Eng,, vol. 20, pp. 91-101, 1973.
2 L. Sornmo and P. Laguna, Bioelectrical Signal Processing in Cardiac and Neurological Applications, Elsevier Academic Press, MA, USA, 2005.
3 S. C. Pie and C. C. Tseng, “Elimination of AC interference in electrocardiogram using IIR notch filter with transient suppression,” IEEE Trans. Biomed. Eng., vol. 42, pp. 1128-2232, 1995.
4 Ch. Levkov, G. Mihov, R. Ivanov and I. Daskalov, “Subtraction of 50 Hz interference from the electrocardiogram,”
5 Med. & Biol. Eng. & Comp., vol. 22, pp. 371-373, 1984.
6 B. Widrow, “Adaptive noise cancelling: principles and applications,” Proc. IEEE, vol. 63,(12), pp. 1692-1716, 1975.
7 A. K. Ziaranj and A. Konrad, “A nonlinear adaptive method of elimination of PLI in ECG signals,” IEEE Trans. Biomed. Eng., vol. 49, (6), pp. 540-547, 2002.
8 P. S. Hamilton, “A comparison of adaptive and non-adaptive filters for the reduction of PLI in the ECG,” IEEE Trans. Biomed. Eng., vol. 43(1), pp. 105-109, 1996.
9 L. Park, K. J. Lee and H. R. Yoon, “Application of a wavelet adaptive filter to minimize distortion of the ST-segment,” Med. Biol. Eng. & Comput., vol. 36, no. 5, pp. 581- 586,September 1998.
10 Cuiwei Li, Chongxun Zheng, and Changfeng Tai, “ Detection of ECG Characteristic Points using Wavelet Transforms,” IEEE Trans. Biomed. Eng., Vol. 42, No. 1, 1995.
11 J.S Sahambi, S.N. Tandon and R.K.P. Bhatt, “Using Wavelet Transform for ECG Characterization,” IEEE Eng. in Med. and Bio., 1997.
12 S.Z. Mohmoodabadi, A. Ahmadian, M.D. Abolhasani (2005) ECG feature extraction using daubechies wavelets, Proc. of the fifth IASTED International Conference, Benidorm, Spain.
13 M. Alfaouri and K. Daqrouq, “ECG signal denoising by wavelet transform thresholding,”American Journal of Applied Sciences, vol. 5, no. 3, pp. 276-281, 2008.
14 D. L. Donoho, De-noising by softthresholding, IEEE Transaction on Information Theory,Vol. 41, pp. 613–627, May 1995.
15 SW Chen. “Two-stage discrimination of cardiac arrhythmias using a total least squaresbased prony modeling algorithm” IEEE Transaction on Biomedical Engineering, 47: pp.1317-1326, 2000.
16 Owis, M., Abou-Zied, A., Youssef, A.B., Kadah, Y., “Robust feature extraction from ECG signals based on nonlinear dynamical modeling,” 23rd Annual International Conference IEEE Engineering in Medicine and Biology Society. (EMBC’01). Volume 2. pp. 1585–1588,2001.
17 Dingfei Ge, Narayanan Srinivasan, Shankar Krishnan. “Cardiac arrhythmia classification using autoregressive modeling” BioMedical Engineering OnLine, 1(1):5, pp. 1585–1588,2002.
18 GE Ding-Fei, HOU Bei-Ping, and XIANG Xin-Jian, “Study of Feature Extraction Based on Autoregressive Modeling in ECG Automatic Diagnosis”, ACTA Automation Sinica. Vol. 33 No. 5. pp. 462-466, 2007.
19 P. de Chazal, M. O’Dwyer, and R. B. Reilly, “Automatic Classification of Heartbeats Using ECG Morphology and Heartbeat Interval Features,” IEEE Transaction on Biomedical Engineering, Vol. 51, No. 7, pp.1196- 1206, July 2004.
20 Inan, O.T., Giovangrandi, L. and Kovacs, G.T.A., “Robust neuralnetwork-based classification of premature ventricular contractions using wavelet transform and timing interval features”, IEEE Transaction on Biomedical Engineering, Vol. 53, No.12. pp. 2507-2515, 2006.
21 Ahmad R. Naghsh-Nilchi and A. Rahim Kadkhoda mohammadi, “Cardiac Arrhythmias Classification Method Based on MUSIC, Morphological Descriptors, and Neural Network”,EURASIP Journal on Advances in Signal Processing, Article No. 202. Volume 2008.
22 K. Sharmila, E. H. Krishna, K. N. Reddy and K. A. Reddy, “ Appliction of Multi-scale principal component analysis (MSPCA) for enhancement of ECG signals”, in Proc. of 28th IEEE International Instrumentation and Measurement Technology Conf., I2MTC-2011, pp.1540-1544, Hangzhou, China, 10-12 May, 2011.
23 Turker Ince, S. Kiranyaz, and M. Gabbouj, "A Generic and Robust System for Automated Patient-specific Classification of Electrocardiogram Signals", IEEE Transactions on Biomedical Engineering, Vol. 56, No. 5, May 2009.
24 F. Castells, P. Laguna, L. Sörnmo, A. Bollmann and J. Millet Roig. “Principal component analysis in ECG signal processing,” EURASIP J. Adv. Si. Pr., vol. 2007.
25 B. R. Bakshi, “Multiscale PCA with application to multivariate statistical process monitoring,AIChE Journal, 44, 7, pp.1596-1610, 1998.
26 “The MIT-BIH Arrhythmia Database,”http://physionet.ph.biu.ac.il/physiobank/database/mitdb/
27 V. X. Afonso, W. J. Tompkins, T. Q. Nguyen, and S. Luo, “ECG Beat detection using filter banks”, IEEE Trans. on Biomed. Eng. , vol. 46, no.2. pp. 192-202,1999.
Associate Professor Sharmila Vallem
KITS Huzurabad - India
Mr. Ette Hari Krishna
Kakatiya University - India
Professor Komalla Ashoka Reddy
Kakatiya University - India