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Peer-Reviewed Academic JournalInternational Journal of Applied Methods in Electronics and Computers
ISSN: 3023-4409DOI Prefix: 10.58190/ijamec
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Pages: 39-44

Classification of segmented heart sounds with Artificial Neural Networks

Ömer DEPERLIĞLU
Publication DateDecember 31, 2018
Volume / IssueVol. 6, No. 4 (pp. 39-44)
subject

Abstract

Nowadays heart diseases are the first cause of human deaths. For this reason, many studies have been carried out to reduce early diagnosis and death of heart diseases. These studies are mostly about developing computer-aided diagnosis systems by utilizing the developing technology. Some computer aided systems are clinical decision support systems developed to more easily detect heart diseases from heart sounds. These systems are used in the automatic analysis of heart sounds based on the classification of heart sounds in general. Much of the work done to diagnose heart diseases is to increase the success of classification. Segmentation of heart sound signals is also one of the frequently used methods to increase classification performance. In this study, S1-S2 sounds were segmented using the resampled energy method and the contribution to segmentation performance of the segment was examined. In practice PASCAL Btraining data set which is widely used for heart diseases application is used. The PASCAL Btraining data set contains three different heart sounds such as normal, murmur, and extrasystole. Artificial Neural Networks (ANN) were used to classify these sounds. For the comparison of the obtained results, two classifications were made for the segmented and the non-segmented sounds. As a result of the classification studies, the average all accuracy of classification 84% was achieved in the non-segmented ANN study, and the average all accuracy of classification 88.6% was obtained in the segmented S1-S2 sounds ANN study. Thus, segmentation of heart sounds increased the accuracy of classification by about 4.6%.
Keywords:Heart sounds classificationartificial neural networkheart sounds segmentationre-sampled signal energyheart sounds.
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References (25)

Cited Literature
  1. 1

    O. Deperlioglu, “Intelligent Techniques Inspired by Nature and Used in Biomedical Engineering “, Chapter 3 in Nature-Inspired Intelligent Techniques for Solving Biomedical Engineering Problems, 2018, ISBN13: 9781522547693.

  2. 2

    M. Zabihi, , A. B. Rad, S. Kiranyaz, , M. Gabbouj, and A. K. Katsaggelos, “Heart Sound Anomaly and Quality Detection using Ensemble of Neural Networks without Segmentation”, in Computing in Cardiology; Volume: 43, pp. 613-616, 2016.

  3. 3

    D. B. Springer, L. Tarassenko, and G. D. Clifford, “Logistic Regression-HSMM-Based Heart Sound Segmentation”, in IEEE Transactions on Biomedical Engineering, Volume: 63, No: 4, pp. 822-832. 2016.

  4. 4

    N. Shankar, M.S. Sangeetha, “Analysis of Phonocardiogram for Detection of Cardiac Murmurs Using Wavelet Transform”, International Journal of Advanced Scientific and Technical Research, Vol. 1, Issue 3, pp. 350- 357, January-February 2013.

  5. 5

    L. Bahekar, A. Mishal, M. Bisen, D. Koche, “Alone, S., Heart Valve Diseases Detection Using Anfis and Wavelet Transform, International” Journal of Research in Science & Engineering, Vol. 3, Issue: 2, pp. 279-291, 2017.

  6. 6

    P. K. Sharma, S. Saha, S. Kumari, “Study and Design of a Shannon-Energy-Envelope based Phonocardiogram Peak Spacing Analysis for Estimating Arrhythmic Heart-Beat”. International Journal of Scientific and Research Publications, Vol. 4, Issue: 9, pp. 1-5, 2014

  7. 7

    A. K. Kumar, G. Saha, “Improved computerized cardiac auscultation by discarding artifact contaminated PCG signal sub-sequence”, Biomedical Signal Processing and Control, Vol. 41, pp. 48–62, 2018.

  8. 8

    S. Choi, Z. Jiang, “Comparison of envelope extraction algorithms for cardiac sound signal segmentation”, Expert Systems with Applications, Vol. 34, pp. 1056–1069, 2008.

  9. 9

    M. Saini, “Proposed Algorithm for Implementation of Shannon Energy Envelope for Heart Sound Analysis”, International Journal of Electronics & Communication Technology IJECT, 7(1), pp. 15-19, 2016.

  10. 10

    M. El-Segaier, O. Lilja, S. Lukkarinen, L. Srnmo, R. Sepponen & E. Pesonen, “Computer-Based Detection and Analysis of Heart Sound Murmur”, Annals of Biomedical Engineering, Vol. 33, Issue: 7, pp. 937-942, 2005.

  11. 11

    P. Carvalho, P. Gil, J. Henriques, M. Antunes & L. Eug´enio, Low Complexity Algorithm for Heart Sound Segmentation using the Variance Fractal Dimension, Proc. Of the Int. Sym. on Intelligent Signal Processing, pp. 593-595, 2005.

  12. 12

    M. V. Shervegar, and G. V. Bhat, “Principal Automatic segmentation of Phonocardiogram using the occurrence of the cardiac events”. Informatics in Medicine Unlocked, Volume: 9, ıssue: 1, pp. 6-10, 2017.

  13. 13

    M.Elgendi, S. Kumar, L. Guo, J. Rutledge, J.Y. Coe, R. Zemp et al. (2015). Detection of Heart Sounds in Children with and without Pulmonary Arterial Hypertension―Daubechies Wavelets Approach. PLoS ONE, Vol. 10, issue:12, pp. 143-146.

  14. 14

    O. Deperlioglu, “Segmentation of Heart Sounds by Re-Sampled Signal Energy Method”, BRAIN. Broad Research in Artificial Intelligence and Neuroscience, Volume 9, Issue 1, 2018.

  15. 15

    W. Zhang, J. Han, S. Deng, “Heart sound classification based on scaled spectrogram and tensor decomposition”. Expert Systems with Applications, Vol. 84, pp. 220–231, 2017.

  16. 16

    S-W. Denga, and J.-Q. Hanb, “Adaptive overlapping-group sparse denoising for heart sound signals”. Biomedical Signal Processing and Control, Vol. 40, pp. 49–57, 2018.

  17. 17

    B.A. Shenoi, Introduction to Digital Signal Processing and Filter Design, John Wiley & Sons Inc. 2005.

  18. 18

    L. Thede, Analog & Digital Filter Design Using C, Prentice Hall; 1 edition, 1995.

  19. 19

    G.E. Guraksin, U. Ergun, O. Deperlioglu, “Performing discrete fourier transform of the heart sounds on the pocket computer”. 14th National Biomedical Engineering Meeting, BIYOMUT 2009, pp. 1-4, 2009.

  20. 20

    K. Gurney, An introduction to neural networks, Taylor & Francis e-Library, UCL Press Limited, London, 2004.

  21. 21

    Tutorials Point, Artificial Neural Network, Tutorials Point (I), Pvt. Ltd. 2017.

  22. 22

    M. Kayri, “Predictive Abilities of Bayesian Regularization and Levenberg–Marquardt Algorithms in Artificial Neural Networks: A Comparative Empirical Study on Social Data”. Mathematical and Computational Applications, Vol. 21, No: 20, pp. 1-12, 2016.

  23. 23

    K.K. Aggarwal, Y. Singh, P. Chandra and M. Puri, “Bayesian Regularization in a Neural Network Model to Estimate Lines of Code Using Function Points”. Journal of Computer Sciences, Vol. 1, Issue: 4, pp. 505-509, 2005.

  24. 24

    O. Deperlioglu, The Effects of Different Training Algorithms on the Classification of Medical Databases Using Artificial Neural Networks, European Conference on Science, Art & Culture ECSAC 2018, Antalya, Turkey.

  25. 25

    Bentley, P. and Nordehn, G. and Coimbra, M. and Mannor, S. (2011) The PASCAL Classifying Heart Sounds Challenge 2011 (CHSC2011) Results, http://www.peterjbentley.com/heartchallenge/index.html.

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How to Cite This Article

Ö. D. (2018). Classification of segmented heart sounds with Artificial Neural Networks. International Journal of Applied Methods in Electronics and Computers, 39-44.