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Cardiac Disorder Classification Based On Extreme Learning Machine

Authors: Chul Kwak, Oh-Wook Kwon


In this paper, an extreme learning machine with an automatic segmentation algorithm is applied to heart disorder classification by heart sound signals. From continuous heart sound signals, the starting points of the first (S1) and the second heart pulses (S2) are extracted and corrected by utilizing an inter-pulse histogram. From the corrected pulse positions, a single period of heart sound signals is extracted and converted to a feature vector including the mel-scaled filter bank energy coefficients and the envelope coefficients of uniform-sized sub-segments. An extreme learning machine is used to classify the feature vector. In our cardiac disorder classification and detection experiments with 9 cardiac disorder categories, the proposed method shows significantly better performance than multi-layer perceptron, support vector machine, and hidden Markov model; it achieves the classification accuracy of 81.6% and the detection accuracy of 96.9%.

Keywords: extreme learning machine, heart sound classification

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[1] D. Barschdorff, S. Ester, T. Dorsel, and E. Most, "Neural network based multi-sensor heart sound analysis," Computers in Cardiology, pp. 303-306, Sep. 1990.
[2] O. Abdel-Alim, N. Hamdy, and M. A. El-Hanjouri, "Heart diseases diagnosis using heart sounds," Proc. 19th NRSC, Alexandria, pp.19-21, Mar. 2002.
[3] D. Barschdorff, U. Femmer, and E. Trowitzsch, "Automatic phonocardiogram signal analysis in infants based on wavelet transforms and artificial neural networks," Computers in Cardiology, pp. 753-756, 1995.
[4] M. El-Hanjouri, W. Alkhaldi, N. Hamdy, and O.A. Alim, "Heart diseases diagnosis using HMM," Proc. IEEE MELECON 2002, Cairo, Egypt, pp. 489-492, May 2002.
[5] A. Ricke, R. Provinelli, and M. Johnson, "Automatic segmentation of Heart sound signals using hidden Markov models," Computer in Cardiology, 9, pp.953-956, 2005.
[6] G.-B. Huang, Q.-Y. Zhu and C.-K. Siew, "Extreme learning machine: Theory and applications," Neurocomputing, vol. 70, pp. 489-501, 2006.
[7] H. Liang, S. Lukkarinen, and I. Hartimo, "Heart Sound Segmentation Algorithm Based on Heart Sound Envelogram," Computers in Cardiology, IEEE, vol. 24, pp. 105-108, 1997.
[8] E.R. Davies, Machine Vision, 3rd ed, Elsevier, pp.103-125, 2005.
[9] Daniel Mason, Listening to the Heart: A Comprehensive Collection of Heart Sounds and Murmurs, F. A. Davis Company, Philadelphia, 2000.
[10] S. Young, et al., Hidden Markov Model Toolkit v3.4, Cambridge University, 2006.
[11] M. Leshno, V. Y. Lin, A. Pinkus, and S. Schocken, "Multilayer feedforward networks with a nonpolynomial activation function can approximate any function," Neural Networks, vol. 6, pp. 861-867, 1993.
[12] Y. Liu and Y.F. Zheng, "One-against-all multi-class SVM classification using reliability measures." IEEE International Joint Conference on Neural Networks, vol. 2, pp. 849-854, 2005.
[13] R.O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification, 2nd ed, John Wiley & Sons, Inc, pp. 482-486, 2000.