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An Automatic Approach Using ELM Classifier for HFpEF Identification Based on Heart Sound Characteristics

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

Abstract

Heart failure with preserved ejection fraction (HFpEF) is a complex and heterogeneous clinical syndrome. For the purpose of assisting HFpEF diagnosis, a non-invasive method using extreme learning machine and heart sound (HS) characteristics was provided in this paper. Firstly, the improved wavelet denoising method was used for signal preprocessing. Then, the logistic regression based hidden semi-Markov model algorithm was utilized to locate the boundary of the first HS and the second HS, therefore, the ratio of diastolic to systolic duration can be calculated. Eleven features were extracted based on multifractal detrended fluctuation analysis to analyze the differences of multifractal behavior of HS between healthy people and HFpEF patients. Afterwards, the statistical analysis was implemented on the extracted HS characteristics to generate the diagnostic feature set. Finally, the extreme learning machine was applied for HFpEF identification by the comparison of performances with support vector machine. The result shows an accuracy of 96.32%, a sensitivity of 95.48% and a specificity of 97.10%, which demonstrates the effectiveness of HS for HFpEF diagnosis.

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References

  1. Csp, L., Gamble, G. D., Ling, L. H. et al., Mortality associated with heart failure with preserved vs. reduced ejection fraction in a prospective international multi-ethnic cohort study. Eur. Heart J. 39(20):1770–1780, 2018. https://doi.org/10.1093/eurheartj/ehy005.

    Article  CAS  Google Scholar 

  2. Borlaug, B. A., and Paulus, W. J., Heart failure with preserved ejection fraction: Pathophysiology, diagnosis, and treatment. Eur. Heart J. 32(6):670, 2011. https://doi.org/10.1093/eurheartj/ehq426.

    Article  PubMed  Google Scholar 

  3. Shah, K. S., Xu, H. L., Matsouaka, R. A. et al., Heart failure with preserved, borderline, and reduced ejection fraction 5-year outcomes. J. Am. Coll. Cardiol. 70(20):2476–2486, 2017. https://doi.org/10.1016/j.jacc.2017.08.074.

    Article  PubMed  Google Scholar 

  4. Ponikowski, P., Voors, A. A., Anker, S. D. et al., 2016 ESC guidelines for the diagnosis and treatment of acute and chronic heart failure: The task force for the diagnosis and treatment of acute and chronic heart failure of the European Society of Cardiology (ESC). Developed with the special contribution of the heart failure association (HFA) of the ESC. Eur. J. Heart Fail. 18(8):891–975, 2016. https://doi.org/10.1002/ejhf.592.

    Article  PubMed  Google Scholar 

  5. Madamanchi, C., Alhosaini, H., Sumida, A. et al., Obesity and natriuretic peptides, BNP and NT-proBNP: Mechanisms and diagnostic implications for heart failure. Int. J. Cardiol. 176(3):611–617, 2014. https://doi.org/10.1016/j.ijcard.2014.08.007.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Telles, F., and Marwick, T. H., Imaging and Management of Heart Failure and Preserved Ejection Fraction. Curr. Treat. Options Cardiovasc. Med. 20(11):90–90, 2018. https://doi.org/10.1007/s11936-018-0689-9.

    Article  PubMed  Google Scholar 

  7. Wu, C. F., Herman, B. A., Retta, S. M. et al., On the closing sounds of a mechanical heart valve. Ann. Biomed. Eng. 33(6):743–750, 2005. https://doi.org/10.1007/s10439-005-3237-1.

    Article  PubMed  Google Scholar 

  8. Gnitecki, J., Moussavi, Z., Ieee, Variance fractal dimension trajectory as a tool for heart sound localization in lung sounds recordings. In: Proceedings of the 25th Annual International Conference of the Ieee Engineering in Medicine and Biology Society, Vols 1–4: A New Beginning for Human Health, vol 25. Proceedings of Annual International Conference of the Ieee Engineering in Medicine and Biology Society. p 2420–2423, 2003.

  9. Kantelhardt, J. W., Zschiegner, S. A., Koscielny-Bunde, E. et al., Multifractal detrended fluctuation analysis of nonstationary time series. Physica A. 316(1–4):87–114, 2002. https://doi.org/10.1016/s0378-4371(02)01383-3.

    Article  Google Scholar 

  10. Zheng, Y., Guo, X., Qin, J. et al., Computer-assisted diagnosis for chronic heart failure by the analysis of their cardiac reserve and heart sound characteristics. Comput. Methods Prog. Biomed. 122(3):372–383, 2015. https://doi.org/10.1016/j.cmpb.2015.09.001.

    Article  Google Scholar 

  11. Sikdar, D., Roy, R., and Mahadevappa, M., Epilepsy and seizure characterisation by multifractal analysis of EEG subbands. Biomed. Signal Process. Control 41:264–270, 2018. https://doi.org/10.1016/j.bspc.2017.12.006.

    Article  Google Scholar 

  12. Lin, J., and Chen, Q., Fault diagnosis of rolling bearings based on multifractal detrended fluctuation analysis and Mahalanobis distance criterion. MSSP 38(2):515–533, 2013. https://doi.org/10.1016/j.ymssp.2012.12.014.

    Article  Google Scholar 

  13. Tang, J., Wang, D., Fan, L. et al., Feature parameters extraction of GIS partial discharge signal with multifractal Detrended fluctuation analysis. ITDEI 22(5):3037–3045, 2015. https://doi.org/10.1109/tdei.2015.004556.

    Article  Google Scholar 

  14. Zheng, Y., Guo, X., and Ding, X., A novel hybrid energy fraction and entropy-based approach for systolic heart murmurs identification. Expert Syst. Appl. 42(5):2710–2721, 2015. https://doi.org/10.1016/j.eswa.2014.10.051.

    Article  Google Scholar 

  15. Lin, S.-W., Ying, K.-C., Chen, S.-C. et al., Particle swarm optimization for parameter determination and feature selection of support vector machines. Expert Syst. Appl. 35(4):1817–1824, 2008. https://doi.org/10.1016/j.eswa.2007.08.088.

    Article  Google Scholar 

  16. Zhang, X., Chen, X., and He, Z., An ACO-based algorithm for parameter optimization of support vector machines. Expert Syst. Appl. 37(9):6618–6628, 2010. https://doi.org/10.1016/j.eswa.2010.03.067.

    Article  Google Scholar 

  17. Zhang, Y., and Zhang, P., Machine training and parameter settings with social emotional optimization algorithm for support vector machine. Pattern Recogn. Lett. 54:36–42, 2015. https://doi.org/10.1016/j.patrec.2014.11.011.

    Article  Google Scholar 

  18. Huang, G.-B., Zhu, Q.-Y., and Siew, C.-K., Extreme learning machine: Theory and applications. Neurocomputing 70(1–3):489–501, 2006. https://doi.org/10.1016/j.neucom.2005.12.126.

    Article  Google Scholar 

  19. Huang, G.-B., Zhou, H., Ding, X. et al., Extreme learning machine for regression and multiclass classification. IEEE Trans Syst Man Cybern B Cybern 42(2):513–529, 2012. https://doi.org/10.1109/tsmcb.2011.2168604.

    Article  PubMed  Google Scholar 

  20. Debbal, S. M., and Bereksi-Reguig, F., Time-frequency analysis of the first and the second heartbeat sounds. Appl. Math. Comput. 184(2):1041–1052, 2007. https://doi.org/10.1016/j.amc.2006.07.005.

    Article  Google Scholar 

  21. Omari, T., and Bereksi-Reguig, F., An automatic wavelet denoising scheme for heart sounds. Int. J. Wavelets Multiresolution Inf. Process. 13(3), 2015. https://doi.org/10.1142/s0219691315500162.

    Article  Google Scholar 

  22. Liang, J., Zhao, J., and Hao, Y., An image denoising and enhancement algorithm for inner and outer ring of wavelet bearings based on improved threshold. In: Tseng, J., Kotenko, I., (Eds.), 3rd Annual International Conference on Information System and Artificial Intelligence, vol 1069. J. Phys. Conf. Ser. 2018. https://doi.org/10.1088/1742-6596/1069/1/012158

    Google Scholar 

  23. Jain, P. K., and Tiwari, A. K., An adaptive thresholding method for the wavelet based denoising of phonocardiogram signal. Biomed. Signal Process. Control 38:388–399, 2017. https://doi.org/10.1016/j.bspc.2017.07.002.

    Article  Google Scholar 

  24. Chourasia, V. S., Tiwari, A. K., and Gangopadhyay, R., A novel approach for phonocardiographic signals processing to make possible fetal heart rate evaluations. DSP 30:165–183, 2014. https://doi.org/10.1016/j.dsp.2014.03.009.

    Article  Google Scholar 

  25. Xiao, S. Z., Guo, X. M., Wang, F. L. et al., Evaluating two new indicators of cardiac reserve. IEEE Eng. Med. Biol. Mag. 22(4):147–152, 2003. https://doi.org/10.1109/memb.2003.1237516.

    Article  PubMed  Google Scholar 

  26. Springer, D. B., Tarassenko, L., and Clifford, G. D., Logistic regression-HSMM-based heart sound segmentation. IEEE Trans. Biomed. Eng. 63(4):822–832, 2016. https://doi.org/10.1109/tbme.2015.2475278.

    Article  PubMed  Google Scholar 

  27. Liu, C., Springer, D., and Clifford, G. D., Performance of an open-source heart sound segmentation algorithm on eight independent databases. Physiol. Meas. 38(8):1730–1745, 2017. https://doi.org/10.1088/1361-6579/aa6e9f.

    Article  PubMed  Google Scholar 

  28. Goldberger, A. L., Amaral, L. A. N., Glass, L. et al., PhysioBank, PhysioToolkit, and PhysioNet - components of a new research resource for complex physiologic signals. Circulation 101(23):E215–E220, 2000. https://doi.org/10.1161/01.CIR.101.23.e215.

    Article  CAS  PubMed  Google Scholar 

  29. Shang, P., Lu, Y., and Kamae, S., Detecting long-range correlations of traffic time series with multifractal detrended fluctuation analysis. Chaos, Solitons Fractals 36(1):82–90, 2008. https://doi.org/10.1016/j.chaos.2006.06.019.

    Article  Google Scholar 

  30. Ihlen, E. A. F., Introduction to multifractal detrended fluctuation analysis in Matlab. Front. Physiol. 3, 2012. https://doi.org/10.3389/fphys.2012.00141.

  31. Rech, M., Aizpurua, A. B., van Empel, V. et al., Pathophysiological understanding of HFpEF: microRNAs as part of the puzzle. Cardiovasc. Res. 114(6):782–793, 2018. https://doi.org/10.1093/cvr/cvy049.

    Article  CAS  PubMed  Google Scholar 

  32. Serne, E. H., de Jongh, R. T., Eringa, E. C. et al., Microvascular dysfunction a potential pathophysiological role in the metabolic syndrome. Hypertension 50(1):204–211, 2007. https://doi.org/10.1161/hypertensionaha.107.089680.

    Article  CAS  PubMed  Google Scholar 

  33. Shang, P., Lu, Y., and Kama, S., The application of holder exponent to traffic congestion warning. Physica A. 370(2):769–776, 2006. https://doi.org/10.1016/j.physa.2006.02.032.

    Article  Google Scholar 

  34. Chattopadhyay, A., Khondekar, M. H., and Bhattacharjee, A. K., Fractality and singularity in CME linear speed signal: Cycle 23. Chaos, Solitons Fractals 114:542–550, 2018. https://doi.org/10.1016/j.chaos.2018.08.008.

    Article  Google Scholar 

  35. Azmy, M. M., Mohamady, R., Heart sounds recognition using multifractal detrended fluctuation analysis and support vector machine. In: AlOqily, I., (Ed.), 2017 Ieee Jordan Conference on Applied Electrical Engineering and Computing Technologies. IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies. 2017.

  36. Choi, S., Detection of valvular heart disorders using wavelet packet decomposition and support vector machine. Expert Syst. Appl. 35(4):1679–1687, 2008. https://doi.org/10.1016/j.eswa.2007.08.078.

    Article  Google Scholar 

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Acknowledgments

This study is funded by the National Natural Science Foundation of China (No. 31570003, 31870980 and 31800823).

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Authors and Affiliations

  1. Key Laboratory of Biorheology Science and Technology, Ministry of Education, College of Bioengineering, Chongqing University, Chongqing, 400044, China

    Yongmin Liu & Xingming Guo

  2. Department of Radiology, the First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China

    Yineng Zheng

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  1. Yongmin Liu
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Correspondence to Xingming Guo.

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Liu, Y., Guo, X. & Zheng, Y. An Automatic Approach Using ELM Classifier for HFpEF Identification Based on Heart Sound Characteristics. J Med Syst 43, 285 (2019). https://doi.org/10.1007/s10916-019-1415-1

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