Advertisement

PCG-Decompositor: A New Method for Fetal Phonocardiogram Filtering Based on Wavelet Transform Multi-level Decomposition

  • Annachiara Strazza
  • Agnese Sbrollini
  • Marica Olivastrelli
  • Agnese Piersanti
  • Selene Tomassini
  • Ilaria Marcantoni
  • Micaela Morettini
  • Sandro Fioretti
  • Laura BurattiniEmail author
Conference paper
Part of the IFMBE Proceedings book series (IFMBE, volume 76)

Abstract

Fetal phonocardiography (FPCG) is a non-invasive acoustic recording of fetal heart sounds (fHS). The fHS auscultation plays an important diagnostic role in assessing fetal wellbeing. Typically, FPCG is a non-stationary signal corrupted by the presence of noise. Thus, high-amplitude noise makes detection of FPCG waveforms challenging. Thus, appropriate filtering procedures have to be applied in order to make FPCG clinically usable. In the recent years, Wavelet transformation (WT) filtering has been proposed. In particular, aim of this study is to propose a new method based on WT multi-level decomposition filtering: PCG-Decompositor. To this aim, PCG-Decompositor based on Coiflets mother Wavelet (4th order, 9 levels of decomposition) was applied to 119 real FPCG tracings, all available in Physionet. PCG-Decompositor is a dependent thresholding technique based on FPCG multi-level decomposition analysis. Performances of PCG-Decompositor are computed against soft-thresholding denoising technique (STDT) in terms of Root Mean Square Error (RMSE) and fetal heart rate (fHR). In terms of fHR, PCG-Decompositor and STDT are compared between themselves and also with the so-called annotations, given by the average fHR using a simultaneous cardiotocography analysis. Original signal to noise ratio (SNR) values ranged from 7.1 dB to 24.4 dB; after application of PCG-Decompositor, SNR increased significantly, ranging from 9.7 dB to 26.9 dB (P < 10−7). Moreover, PCG-Decompositor showed a lower dispersion than STDT (RMSE: 0.7 dB vs. 1.2 dB), introduced no FPCG signal delay and left fHR unaltered. Thus, PCG-Decompositor could be a suitable and robust technique to denoise FPCG signals.

Keywords

Fetal phonocardiography Wavelet transform Multi-level decomposition 

Notes

Acknowledgements

The authors wish to thank Prof. Reza Sameni for sharing the cardiotocographic data, simultaneously recorded to the fetal phonocardiographic data, without which reference values of the fetal heart rate in the experimental study could not be obtained.

References

  1. 1.
    Adithya, P.C., Sankar, R., Moreno, W.A.: Trends in fetal monitoring through phonocardiography: challenges and future directions. Biomed. Signal Process. Control 33, 289–305 (2017)CrossRefGoogle Scholar
  2. 2.
    Chourasia, S., Tiwari, A.K.: A review and comparative analysis of recent advancements in fetal monitoring techniques. Crit. Rev. Biomed. Eng. 36, 335–373 (2008)CrossRefGoogle Scholar
  3. 3.
    Sbrollini, A., Strazza, A., Caragiuli, M., et al.: Fetal phonocardiogram denoising by wavelet transformation: robustness to noise. Comput. Cardiol. 44, 1–4 (2017).  https://doi.org/10.22489/cinc.2017.331-075Google Scholar
  4. 4.
    Messer, S.R., Argzarian, J., Abbott, D.: Optimal wavelet denoising for phonocardiograms. Microelectron. J. 32, 931–941 (2001)CrossRefGoogle Scholar
  5. 5.
    Vaisman, S., Salem, S.Y., Holcberg, G., et al.: Passive fetal monitoring by adaptive wavelet denoising method. Comput. Biol. Med. 42(2), 171–179 (2012)CrossRefGoogle Scholar
  6. 6.
    Mallat, S.G.: A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans. Pattern Anal. Mach. Intell. 11(7), 674–693 (1989)CrossRefGoogle Scholar
  7. 7.
    Choursia, V.S., Mittra, A.K.: Selection of mother wavelet and denoising algorithm for analysis of foetal phonocardiographic signals. J. Med. Eng. Technol. 33(6), 442–448 (2009)CrossRefGoogle Scholar
  8. 8.
    Koutsiana, E., Hadjileontiadis, L.J., Chouvarda, I.: Fetal heart sounds detection using wavelet transform and fractal dimension. Front. Bioeng. Biotechnol. 5, 49 (2017)CrossRefGoogle Scholar
  9. 9.
    Tomassini, S., et al.: Wavelet denoising of fetal phonocardiography: a comparative analysis. Math. Biosci. Eng. 16(5), 6034–6046 (2019).  https://doi.org/10.3934/mbe.2019302CrossRefGoogle Scholar
  10. 10.
    Donoho, D.L.: De-noising by soft-thresholding. IEEE Trans. Inf. Theory 41(3), 613–627 (1995)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Samieinasab, M., Sameni, R.: Fetal phonocardiogram extraction using single channel blind source separation. In: ICEE 2015 - Proceedings of the 23rd Iranian Conference on Electrical Engineering, pp. 78–83 (2015)Google Scholar
  12. 12.
    Sbrollini, A., Agostinelli, A., Burattini, L., et al.: CTG analyzer: a graphical user interface for cardiotocography. In: 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Jeju Island, pp. 2606–2609 (2017)Google Scholar
  13. 13.
    Goldberger, A.L., Amaral, L.A., Glass, L., et al.: PhysioBank, PhysioToolkit, and PhysioNet. Circ. J. 101(23), 215–229 (2000)Google Scholar
  14. 14.
    Strazza, A., Sbrollini, A., Di Battista, V., et al.: PCG-Delineator: an efficient algorithm for automatic heart sounds detection in fetal phonocardiography. Comput. Cardiol. 45, 8743707 (2018).  https://doi.org/10.22489/cinc.2018.045

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Information EngineeringUniversità Politecnica delle MarcheAnconaItaly

Personalised recommendations