Skip to main content
SpringerLink
Log in

RenyiBS: Renyi entropy basis selection from wavelet packet decomposition tree for phonocardiogram classification

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

Wavelet packet transform (WPT) is a powerful mathematical tool for analyzing nonlinear biomedical signals, such as phonocardiogram (PCG). WPT decomposes a PCG signal into a full binary tree of details and approximation coefficients. Appropriate nodes of the tree could be selected as a basis for generating features. Motivated by this, we propose the Renyi entropy basis selection (RenyiBS) method. In RenyiBS method, we use the Renyi entropy as an information measure to choose the best basis of the wavelet packet tree of PCG signals for feature selection and classification. The Renyi entropy estimates the spectral complexity of a signal, which is vital for characterizing nonlinear signals such as PCGs. After selecting the best basis, we define features on the coefficients of the selected nodes. Then, we classify PCGs using the support vector machine (SVM) classifier. In the simulation, we examine a set of 820 heart sound cycles, including normal heart sounds and three types of heart murmurs. The three murmurs examined include aortic regurgitation, mitral regurgitation, and aortic stenosis. We achieved the promising result of 99.74% accuracy, confirming the ability of Renyi entropy to select an appropriate basis of the wavelet packet tree and extracting the nonlinear behavior of particular heart sounds. Besides, the superiority of our proposed information measure in comparison with other information measures reported before is shown.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

(adapted from [16])

Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Dwivedi AK, Imtiaz SA, Rodriguez-Villegas E (2018) Algorithms for automatic analysis and classification of heart sounds-a systematic review. IEEE Access 7:8316–8345

    Article  Google Scholar 

  2. Coifman RR, Wickerhauser MV (1992) Entropy-based algorithms for best basis selection. IEEE Trans Inf Theory 38(2):713–718

    Article  Google Scholar 

  3. Gradolewski D, Magenes G, Johansson S, Kulesza WJ (2019) A wavelet transform-based neural network denoising algorithm for mobile phonocardiography. Sensors 19(4):957

    Article  Google Scholar 

  4. Boutana D et al (2017) Heart murmurs detection and characterization using wavelet analysis with Renyi entropy. J Mech Med Biol 17(06):1750093

    Article  Google Scholar 

  5. Shi M, Zhan C, He H, Jin Y, Wu R, Sun Y (2019) Renyi distribution entropy analysis of short-term heart rate variability signals and its application in coronary artery disease detection. Front Physiol 10(June):1–14

    Google Scholar 

  6. Rizal A, Hidayat R, Nugroho HA (2019) Comparison of multilevel wavelet packet entropy using various entropy measurement for lung sound classification. Int J Adv Comput Sci Appl 10(2):77–82

    Google Scholar 

  7. Kristomo D, Hidayat R, Soesanti I (2017) Classification of the syllables sound using wavelet, Renyi entropy and AR-PSD features. In: Proceedings of the 2017 IEEE 13th International Colloquium on Signal Processing & its Applications (CSPA) 2017, vol 2, No. March, pp 94–99

  8. Acharya UR et al (2017) Biomedical signal processing and control application of higher-order spectra for the characterization of coronary artery disease using electrocardiogram signals. Biomed Signal Process Control 31:31–43

    Article  Google Scholar 

  9. Acharya UR et al (2017) Application of higher-order spectra for the characterization of coronary artery disease using electrocardiogram signals. Biomed Signal Process Control 31:31–43

    Article  Google Scholar 

  10. Gupta V, Priya T, Yadav AK, Pachori RB, Rajendra Acharya U (2017) Automated detection of focal EEG signals using features extracted from flexible analytic wavelet transform. Pattern Recognit Lett 94:180–188

    Article  Google Scholar 

  11. Xiao B, Xu Y, Bi X, Zhang J, Ma X (2019) Heart sounds classification using a novel 1-D convolutional neural network with extremely low parameter consumption. Neurocomputing, vol in press, No. xxxx, pp 1–7

  12. Bozkurt B, Germanakis I, Stylianou Y (2018) A study of time-frequency features for CNN-based automatic heart sound classification for pathology detection. Comput Biol Med 100:132–143

    Article  Google Scholar 

  13. Hamidi M, Ghassemian H, Imani M (2018) Classification of heart sound signal using curve fitting and fractal dimension. Biomed Signal Process Control 39:351–359

    Article  Google Scholar 

  14. Xu L, Wang Y, Yao Y, Feng C, Zhao YM, Meng Q-H (2010) Comparison of six envelope extraction methods based on abnormal heart sounds. In: 2010 3rd International Conference on Biomedical Engineering and Informatics, No. Bmei, pp 813–817

  15. Saeidi A, Almasganj F, Shojaeifard M (2017) Automatic cardiac phase detection of mitral and aortic valves stenosis and regurgitation via localization of active valves. Biomed Signal Process Control 36:11–19

    Article  Google Scholar 

  16. Choi S, Jiang Z (2008) Comparison of envelope extraction algorithms for cardiac sound signal segmentation. Expert Syst Appl 34(2):1056–1069

    Article  Google Scholar 

  17. Abo-Zahhad M, Ahmed SM, Abbas SN (2016) Biometrics from heart sounds: evaluation of a new approach based on wavelet packet cepstral features using HSCT-11 database. Comput Electr Eng 53:1–13

    Article  Google Scholar 

  18. Li J, Ke LI, Du Q, Ding X, Chen X, Wang D (2019) Heart sound signal classification algorithm: a combination of wavelet scattering transform and twin support vector machine. IEEE Access 7:179339–179348

    Article  Google Scholar 

  19. Li J, Ke L, Du Q (2019) Classification of heart sounds based on the wavelet fractal and twin support vector machine. Entropy 21(5):472

    Article  MathSciNet  Google Scholar 

  20. Fern R, Montalvo A, Calvo R, Hern G (2008) Selection of the best wavelet packet nodes based on mutual information for speaker identification. In: Iberoamerican Congress on Pattern Recognition (CIARP) 2008, LNCS 5197, pp 78–85

  21. She B, Tian F, Liang W, Zhang G (2018) De-noising method based on dual-tree complex wavelet packet transform and principal manifold and its application for fault diagnosis. In: 2018 2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC), No. Imcec, pp 486–491

  22. Cardinali A, Nason GP, Issue S (2017) Locally stationary wavelet packet processes: basis selection and model fitting. J Time Ser Anal 38(2):151–174

    Article  MathSciNet  Google Scholar 

  23. Zhang D, He J, Yao J, Wu Y, Du M (2012) Noninvasive detection of mechanical prosthetic heart valve disorder. Comput Biol Med 42(8):785–792

    Article  Google Scholar 

  24. Safara F (2015) Cumulant-based trapezoidal basis selection for heart sound classification. Med Biol Eng Comput 53(11):1153–1164

    Article  Google Scholar 

  25. Safara F, Doraisamy S, Azman A, Jantan A, Abdullah Ramaiah AR (2013) Multi-level basis selection of wavelet packet decomposition tree for heart sound classification. Comput Biol Med 43(10):1407–1414

    Article  Google Scholar 

  26. Wang D, Miao D, Xie C (2011) Best basis-based wavelet packet entropy feature extraction and hierarchical EEG classification for epileptic detection. Expert Syst Appl 38(11):14314–14320

    Google Scholar 

  27. Li L, Han Y, Chen W, Lv C, Sun D (2016) An improved wavelet packet-chaos model for life prediction of space relays based on volterra series. PLoS ONE 11(6):1–13

    Google Scholar 

  28. Choi S, Shin Y, Cheong Y, Lee G-J, Park H-K (2010) Wavelet packet based features for insufficient murmur identification. In: International Conference on Control, Automation and Systems 2010, vol 2, pp 1184–1187

  29. Sugondo H, Suci A, Achmad R (2019) Quantitative EEG based on Renyi entropy for epileptic classification. J Electr Electron Eng 12(1):15–20

    Google Scholar 

  30. Venkataraman V, Arunkumar N, Kumar KR (2018) Entropy features for focal EEG and non focal EEG. J Comput Sci 27:440–444

    Article  Google Scholar 

  31. Zhang T, Chen W, Li M (2018) Fuzzy distribution entropy and its application in automated seizure detection technique. Biomed Signal Process Control 39:360–377

    Article  Google Scholar 

  32. Göbl C, Tjandra N (2012) Application of solution NMR spectroscopy to study protein dynamics. Entropy 14(12):581–598

    Article  Google Scholar 

  33. Renyi A (1961) On measures of entropy and information. In: Fourth Berkeley Symposium on Mathematical Statistics and Probability, vol 1, pp 547–561

  34. Mirjalili S, Faris H, Aljarah I (2020) Evolutionary machine learning techniques. Springer, Singapore

    Book  Google Scholar 

Download references

Acknowledgements

The authors gratefully acknowledge the financial and other supports of this research, provided by the Islamic Azad University, Islamshahr Branch, Tehran, Iran.

Author information

Authors and Affiliations

  1. Department of Computer Engineering, Islamic Azad University, Islamshahr Branch, Tehran, Iran

    Fatemeh Safara

  2. Cardiology Department, Serdang Hospital, Serdang, Jalan Puchong, 43000, Kajang, Selangor Darul Ehsan, Malaysia

    Asri Ranga Abdullah Ramaiah

Authors
  1. Fatemeh Safara
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Asri Ranga Abdullah Ramaiah
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Fatemeh Safara.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Safara, F., Ramaiah, A.R.A. RenyiBS: Renyi entropy basis selection from wavelet packet decomposition tree for phonocardiogram classification. J Supercomput 77, 3710–3726 (2021). https://doi.org/10.1007/s11227-020-03413-9

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-020-03413-9

Keywords

Search

Navigation