Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 31108
In Search of an SVD and QRcp Based Optimization Technique of ANN for Automatic Classification of Abnormal Heart Sounds

Authors: Samit Ari, Goutam Saha


Artificial Neural Network (ANN) has been extensively used for classification of heart sounds for its discriminative training ability and easy implementation. However, it suffers from overparameterization if the number of nodes is not chosen properly. In such cases, when the dataset has redundancy within it, ANN is trained along with this redundant information that results in poor validation. Also a larger network means more computational expense resulting more hardware and time related cost. Therefore, an optimum design of neural network is needed towards real-time detection of pathological patterns, if any from heart sound signal. The aims of this work are to (i) select a set of input features that are effective for identification of heart sound signals and (ii) make certain optimum selection of nodes in the hidden layer for a more effective ANN structure. Here, we present an optimization technique that involves Singular Value Decomposition (SVD) and QR factorization with column pivoting (QRcp) methodology to optimize empirically chosen over-parameterized ANN structure. Input nodes present in ANN structure is optimized by SVD followed by QRcp while only SVD is required to prune undesirable hidden nodes. The result is presented for classifying 12 common pathological cases and normal heart sound.

Keywords: Optimization, ANN, SVD, phonocardiogram, murmurs, Classification of heart diseases, QRcp

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1670


[1] Ibrahim R. Hanna, Mark E. Silverman, "A history of cardiac auscultation and some of its contributors", The American Journal of Cardiology, vol. 90, pp. 259-267, Aug. 1, 2002
[2] J. R. Bender, "Yale University School of Medicine Heart Book", New York: William Morrow and Company, Inc., 1992, ch. 13, pp. 167-175.
[3] S. Mangione, L. Nieman, "Cardiac auscultatory skills of internal medicine and family practice trainees", Journal of the American Medical Association, vol. 278, pp. 717-722, 1997.
[4] L. G. Durand, P. Pibarot, "Digital signal processing of the phonocardiogram: review of the most recent advancements", Critical Reviews in Biomedical Engineering, vol. 22, no. (3/4), pp. 163-219, 1995.
[5] S. Lukkarinen, A. Nopanen, K. Sikio, A. Angerla, "A new phonocardiographic recording system", Computers in Cardiology, vol. 27, pp. 117-120, 1997.
[6] Leslie Cromwell, Fred J. Weibell, Erich A. Pfeiffer, "Biomedical Instrumentation and Measurements", 2nd ed, June, 2002, PHI Publication, Ch. 6: 169-172.
[7] Rangaraj M. Rangayyan, "Biomedical Signal Analysis", 2002, IEEE Press, John Willy & Sons Inc., Ch. 1:34-38.
[8] Z. Sharif, M. S. Zainal, A. Z. Sha-ameri, S. H. S. Salleh, "Analysis and classification of heart sounds and murmurs based on the instantaneous energy and frequency estimations", in Proceedings TENCON 2000, vol. 2, pp. 130-134, Sept., 2000.
[9] Ian Cathers, "Neural network assisted cardiac auscultation", Artificial Intelligence in Medicine, vol. 7, pp. 53-66, 1995.
[10] T. Ölmez, Z. Dokur, "Classification of heart sounds using an artificial neural network", Pattern Recognition Letters,vol. 24, pp. 617-629, Jan., 2003.
[11] T. R. Reed, N. E. Reed, P. Fritzson, "Heart sound analysis for symptom detection and computer-aided diagnosis", Simulation Modelling Practice and Theory, vol. 12, pp. 129-146, 2004.
[12] C. N. Gupta, R. Palaniappan, S. Swaninathan, S. M. Krishnan, "Neural Netork classification of homo-morphic segmented heart sounds", Applied Soft Computing, vol. 7, pp. 286-297, 2007.
[13] S. Omran, M. Tayel, "A heart sound segmentation and feature extraction algorithm using wavelets", in First international symposium on control, communication and signal processing, pp. 235-238, 2004.
[14] B. El-Asir, L. Khadra, A. H. Al-Abbasi, M. M. J. Mohammed, "Timefrequency analysis of heart sounds", TENCON -96 Proceedings on Digital Signal Processing Applications, vol. 2, pp. 26-29, Nov., 1996.
[15] J. C. Wood, D. T. Barry, "Time-frequency analysis of the first heart sound", Engineering in Medicine and Biology Magazine, IEEE, vol. 14, no. (2), pp. 144-151, March- April, 1995.
[16] Jung Jun Lee, S. M. Lee, I. Y. Kim, H. K. Min, and S. H. Hong, "Comparison between the short time Fourier and wavelet transform for feature extraction of heart sounds", in Proceedings of IEEE Tencon-99, pp. 1547-1550, 1999.
[17] S. M. Debbal, F. Bereksi-Reguig, "Time-frequency analysis of the first and the second heartbeat sounds", Applied Mathematics and Computation (2006), doi: 10.1016/j.amc.2006.07.005.
[18] P. P. Kanjilal, G. Saha, T. J. Koickal "On Robust Nonlinear Modelling of a Complex Process with Large Number of Inputs Using m-Qrcp Factorization and Cp Statistics", IEEE transaction on systems,Man and Cybernatics, vol. 29, pp. 1-12, 1999.
[19] S. Ari, P. Kumar, G. Saha, "A Robust Heart Sound Segmentation Algorithm for Commonly occurring Heart Valve Diseases ", Journal of Medical Engineering & Technology(2006), doi: 10.1080 / 03091900601015162.
[20] S. Ari, K. Sensharma, G. Saha, "A DSP implementation of heart valve disorder detection system from phonocardiogram signal", Journal of Medical Engineering & Technology(2006), doi: 10.1080 / 03091900600861574.
[21] P. P. Kanjilal, "Adaptive Prediction and Predictive Control", Peter Peregrinus Ltd., 1995, ch. 10, Appendix 3B.
[22] H. Liang, S. Lukkarinen, I. Hartimo, "Heart Sound Segmentation Algorithm based on Heart Sound Envelogram", Computers in Cardiology, vol. 24, pp. 105-108, 1997.
[23] H. Liang, S. Lukkarinen, I. Hartimo, "A heart sound segmentation algorithm using wavelet decomposition and reconstruction", in Proceedings of the 19th Annual International Conference of the Engineering in Medicine and Biology society, IEEE, vol. 4, pp. 1630- 1633, 30 Oct.-2 Nov., 1997.
[24] N. P. Archer and S. Wang, "fuzzy set representation of Neural network classification boundaries", IEEE transaction on systems,Man and Cybernatics, pp. 735-742, 1991.
[25] Symon Haykin, "Neural Networks", Pearson education Asia, 2002, ch. 4: 156-256.