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Automated Diagnosis of Heart Sounds Using Rule-Based Classification Tree

  • Image & Signal Processing
  • Published:
Journal of Medical Systems Aims and scope Submit manuscript

Abstract

In order to assist the diagnosis procedure of heart sound signals, this paper presents a new automated method for classifying the heart status using a rule-based classification tree into normal and three abnormal cases; namely the aortic valve stenosis, aortic insufficient, and ventricular septum defect. The developed method includes three main steps as follows. First, one cycle of the heart sound signals is automatically detected and segmented based on time properties of the heart signals. Second, the segmented cycle is preprocessed with the discrete wavelet transform and then largest Lyapunov exponents are calculated to generate the dynamical features of heart sound time series. Finally, a rule-based classification tree is fed by these Lyapunov exponents to give the final decision of the heart health status. The developed method has been tested successfully on twenty-two datasets of normal heart sounds and murmurs with success rate of 95.5%. The resulting error can be easily corrected by modifying the classification rules; consequently, the accuracy of automated heart sounds diagnosis is further improved.

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References

  1. Syed, Z., Leeds, D., Curtis, D., Nesta, F., Levine, R.A., and Guttag, J., A framework for the analysis of acoustical cardiac signals. Biomedical Engineering, IEEE Transactions on. 54(4):651–662, 2007. doi:10.1109/tbme.2006.889189.

    Article  Google Scholar 

  2. Webster, J.G., The physiological measurement handbook. CRC Press, Boca Raton, 2014.

    Google Scholar 

  3. Safara, F., Doraisamy, S., Azman, A., Jantan, A., and Ranga, S., Wavelet packet entropy for heart murmurs classification. Adv. Bioinforma. 2012:6, 2012. doi:10.1155/2012/327269.

    Article  Google Scholar 

  4. Brusco, M., and Nazeran, H., Development of an Intelligent PDA-based Wearable Digital Phonocardiograph. In: Engineering in Medicine and Biology Society, 2005. IEEE-EMBS 2005. 27th annual international conference of the, 17–18 Jan. 2006 2005, pp 3506–3509. doi:10.1109/iembs.2005.1617235.

  5. Kao, W.-C., and Wei, C.-C., Automatic phonocardiograph signal analysis for detecting heart valve disorders. Expert Syst. Appl. 38(6):6458–6468, 2011. doi:10.1016/j.eswa.2010.11.100.

    Article  Google Scholar 

  6. Reed TR, Reed NE, Fritzson P (2004) Heart Sound Analysis for Symptom Detection and Computer-Aided Diagnosis. Simulation Modeling Practice and Theory 12. doi:10.1016/j.simpat.2003.11.005

  7. Chauhan, S., Wang, P., Sing Lim, C., and Anantharaman, V., A computer-aided MFCC-based HMM system for automatic auscultation. Comput. Biol. Med. 38(2):221–233, 2008. doi:10.1016/j.compbiomed.2007.10.006.

    Article  PubMed  Google Scholar 

  8. Debbal, S.M., and Bereksi-Reguig, F., Automatic measure of the split in the second cardiac sound by using the wavelet transform technique. Comput. Biol. Med. 37(3):269–276, 2007. doi:10.1016/j.compbiomed.2006.01.005.

    Article  CAS  PubMed  Google Scholar 

  9. Übeyli, E.D., Adaptive neuro-fuzzy inference system for classification of ECG signals using lyapunov exponents. Comput. Methods Prog. Biomed. 93(3):313–321, 2009. doi:10.1016/j.cmpb.2008.10.012.

    Article  Google Scholar 

  10. Eckmann, J., Kamphorst, S.O., Ruelle, D., and Ciliberto, S., Liapunov exponents from time series. Phys Rev A Gen Phys. 34(6):4971–4979, 1986.

    Article  CAS  PubMed  Google Scholar 

  11. Maglogiannis, I., Loukis, E., Zafiropoulos, E., and Stasis, A., Support vectors machine-based identification of heart valve diseases using heart sounds. Comput. Methods Prog. Biomed. 95(1):47–61, 2009. doi:10.1016/j.cmpb.2009.01.003.

    Article  Google Scholar 

  12. Melillo, P., Luca, N.D., Bracale, M., and Pecchia, L., Classification tree for risk assessment in patients suffering from congestive heart failure via long-term heart rate variability. IEEE Journal of Biomedical and Health Informatics. 17(3):727–733, 2013. doi:10.1109/jbhi.2013.2244902.

    Article  PubMed  Google Scholar 

  13. Faizan, J., Venkatachalam, P.A., and Ahmad Fadzil, M.H., A signal processing module for the analysis of heart sounds and heart murmurs. J. Phys. Conf. Ser. 34(1):1098, 2006.

    Google Scholar 

  14. Tang, H., Li, T., Qiu, T., and Park, Y., Segmentation of heart sounds based on dynamic clustering. Biomedical Signal Processing and Control. 7(5):509–516, 2012. doi:10.1016/j.bspc.2011.09.002.

    Article  Google Scholar 

  15. Gill, D., Gavrieli, N., Intrator, N., Detection and identification of heart sounds using homomorphic envelogram and self-organizing probabilistic model. In: Computers in Cardiology, 2005, 25–28 Sept. 2005 2005, pp 957–960. doi:10.1109/cic.2005.1588267.

  16. Ari, S., Kumar, P., Saha, G., On An Algorithm for Boundary Estimation of Commonly Occurring Heart Valve Diseases in Time Domain. In: 2006 Annual IEEE India conference, Sept. 2006, pp 1–5. doi:10.1109/indcon.2006.302758.

  17. Choi, S., and Jiang, Z., Comparison of envelope extraction algorithms for cardiac sound signal segmentation. Expert Syst. Appl. 34(2):1056–1069, 2008. doi:10.1016/j.eswa.2006.12.015.

    Article  Google Scholar 

  18. Daubechies, I., Orthonormal bases of compactly supported wavelets II. Variations on a theme. SIAM J. Math. Anal. 24(2):499–519, 1993. doi:10.1137/0524031.

    Article  Google Scholar 

  19. Babaei, S., and Geranmayeh, A., Heart sound reproduction based on neural network classification of cardiac valve disorders using wavelet transforms of PCG signals. Comput. Biol. Med. 39(1):8–15, 2009. doi:10.1016/j.compbiomed.2008.10.004.

    Article  PubMed  Google Scholar 

  20. Gergely, S., Roman, M. N., Fort, C., Multirate Sampling in PCG Signal Correlation. In: Vlad, S., Ciupa, R. (Eds.), International Conference on Advancements of Medicine and Health Care through Technology. Vol. 36. IFMBE Proceedings. Springer Berlin Heidelberg, pp 198–201, 2011. doi:10.1007/978–3–642-22586-4_43

  21. Karar, M. E., El-Brawany, M., Embedded heart sounds and murmurs generator based on discrete wavelet transform. In: 2016 Fourth International Japan-Egypt Conference on Electronics, Communications and Computers (JEC-ECC), May 31 2016–June 2 2016 2016, pp 34–37. doi:10.1109/jec-ecc.2016.7518962

  22. Gray, J.B., and Fan, G., Classification tree analysis using TARGET. Computational Statistics & Data Analysis. 52(3):1362–1372, 2008. doi:10.1016/j.csda.2007.03.014.

    Article  Google Scholar 

  23. Stasis, A.C., Loukis, E.N., Pavlopoulos, S.A., and Koutsouris, D., A multiple decision trees architecture for medical diagnosis: the differentiation of opening snap, second heart sound split and third heart sound. Comput. Manag. Sci. 1(3):245–274, 2004. doi:10.1007/s10287-004-0015-8.

    Article  Google Scholar 

  24. Sokolova, M., and Lapalme, G., A systematic analysis of performance measures for classification tasks. Inf. Process. Manag. 45(4):427–437, 2009. doi:10.1016/j.ipm.2009.03.002.

    Article  Google Scholar 

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Authors and Affiliations

  1. Faculty of Electronic Engineering (FEE), Menoufia University, Menouf, 32952, Egypt

    Mohamed Esmail Karar, Sahar H. El-Khafif & Mohamed A. El-Brawany

Authors
  1. Mohamed Esmail Karar
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  2. Sahar H. El-Khafif
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  3. Mohamed A. El-Brawany
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Correspondence to Mohamed Esmail Karar.

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This article does not contain any studies with human participants or animals performed by any of the authors.

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This article is part of the Topical Collection on Image & Signal Processing

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Karar, M.E., El-Khafif, S.H. & El-Brawany, M.A. Automated Diagnosis of Heart Sounds Using Rule-Based Classification Tree. J Med Syst 41, 60 (2017). https://doi.org/10.1007/s10916-017-0704-9

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