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Classification of non stationary signals using multiscale decomposition

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DOI: 10.4236/jbise.2010.32025    3,827 Downloads   7,538 Views   Citations

ABSTRACT

The aim of this article is to develop an automatic algorithm for the classification of non stationary signals. The application context is to classify uterine electromyogram (EMG) events to prevent the onset of preterm birth. The idea is to discriminate between the events by allocating them to the physiological classes: contractions, foetus motions, Alvarez or Long Duration Low Frequency waves. Our method is based on the Wavelet Packet (WP) decomposition and the choice of a best basis for classification purpose. Before classification, there is a need to detect events in the recorded signals. The discrimination criterion is based on the calculation of the ratio between intra-class variance and total variance (sum of the intra-class and inter-class variances), calculated directly from the coefficients of the selected WP. We evaluated the performance of the algorithm on real signals by using the classification methods Neural Networks (NN) and Support Vector Machines (SVM). Subband energies of the best selected WP are used as effective features. The determined best basis is applicable to a wide range of uterine EMG signals from large range of patients. In most cases, more than 85% of events are well classified whatever the term of gestation.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

Chendeb, M. , Khalil, M. , Hewson, D. and Duchên, J. (2010) Classification of non stationary signals using multiscale decomposition. Journal of Biomedical Science and Engineering, 3, 193-199. doi: 10.4236/jbise.2010.32025.

References

[1] Gretton, A. et al. (2001) Nonstationary signal classification using support vector machines. 11th IEEE Workshop on Statistical Signal Processing Proceedings, pp. 305–308.
[2] Devedeux, D., Marque, C., Mansour, S., Germain, G., Duchene, J. (1993) Uterine electromyography: a critical review. Amer. J. Obstet. Gynecol. 169(6), 1636–1653.
[3] Khalil, M., Duchene, J. (2000) Uterine EMG analysis: a dynamic approach for change detection and classification. IEEE Trans. Biomed. Eng, 47(6), 748–756.
[4] Garfield, R.E., Saade, G., Buhimschi, C., Buhimschi, I. et al. (1998) Control and assessment of the uterus and cervix during pregnancy and labour. European Society of Human and Embryology, 4(5), 673–695.
[5] Doret, M., Bukowski, R., Longo, M., Maul, H., Maner, W.L., Garfield, R.E. and Saade, G.R. (2005) Uterine electromyography characteristics for early diagnosis of mifepristone-induced preterm labor. Obstetrics and Gynecology, 15(4), 822–830.
[6] Lu, N., Wang, J., McDermott, I., Thornton, S. et al. (2009) Uterine electromyography signal feature extraction and classification. International Journal of Modelling, Identification and Control, 6(2), 136–146.
[7] Eswaran, H., Wilson, J.D., Murphy, P., Preissl, H., Lowery C.L. (2002) The Journal of Maternal-Fetal & Neonatal Medicine, 11(3), 158–166.
[8] Ravier, P. and Amblard, P.O. (2001) Wavelet packets and de- noising based on higher-order-statistics for transient detection. Signal Processing, 81(9), 1909–1926.
[9] Hitti, E. and Lucas, M.F. (1998) Wavelet-packet basis selection for abrupt changes detection in multicomponent signals. Proc. EUSIPCO, Island of Rhodes, Greece, 1841–1844.
[10] Leman, H. and Marque, C. (2000) Rejection of the maternal electrocardiogram in the electrohysterogram signal, IEEE Trans Biomed Eng. 47(8), 1010–1017.
[11] Yang, B., Yan, G., Yan, R., Wu, T. (2007) Adaptive subject-based feature extraction in brain–computer interfaces using wavelet packet best basis decomposition. Medical Engineering & Physics, 29(1), 48–53.
[12] Saito, N. and Coifman, R.R. (1994) Local discriminant bases. A. F. Laine and M. A. Unser Editors, Wavelet Applications in Signal and Image Processing {II}, Proc. SPIE 2303, 2–14.
[13] Zhao, J., Yang, X.W., Li, J.P. and Tang, Y.Y. (2001) DNA sequences classification based on wavelet packet analysis from “wavelet analysis and its applications book”. Springer Berlin / Heidelberg editor. Rajpoot, N.M. (2002) Texture classification using discriminant wavelet packet subbands. 45th Midwest Symposium on Circuits and Systems, 3, 300–303.
[14] Chendeb, M., Khalil, M. and Duchêne J. (2006) Methodology of wavelet packet selection for event detection. Signal Processing, 86, 3826–3841.
[15] Nadernejad, E. (2008) μPET data de-noising using wavelet packets. Contemporary Engineering Sciences, 1(2), 91–104.
[16] Mallat, S. (1999) a wavelet tour of signal processing, Academic Press, San Diego, CA.
[17] Sheng, S., Yanda, L. (1997) “Best Bases for Classification”. Technical Report of pattern Recognition and Signal Processing Lab, department of Automation, Tsinghua University.
[18] Fukunaga, K. (1990) “Introduction to statistical pattern recognition”. Academic Press, Inc.
[19] Dubuisson, B. (1990) “Diagnostic et reconnaissance des formes”. Editions Hermes.
[20] Turkoglu, I., Arslan, A. and Ilkay, E. (2003) An intelligent system for diagnosis of the heart valve diseases with wavelet packet neural networks. Computers in Biology and Medicine, 33(4), 319–331.
[21] Gunn, S.R. (1998) Support vector machines for classification and regression. Technical Report, School of Electronics and Computer Science, University of Southampton.
[22] Mayoraz, E. and Alpaydin, E. (1998) Support vector machines for multi-class classification. Technical Report IDIAP.
[23] Haykin, S.S., Gwynn, R. (2008) Neural networks and learning machines. Prentice Hall, 3rd edition.

  
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