Noninvasive Vascular Blood Sound Monitoring Through Flexible Microphone

  • Binit Panda
  • Stephanie Chin
  • Soumyajit Mandal
  • Steve J. A. MajerusEmail author


An arteriovenous vascular access is used to provide hemodialysis to people suffering from acute or chronic kidney disease. Monitoring of vascular access is essential to reduce the risk of sudden clotting caused by vascular stenosis, to maintain patency of the access, and to avoid hospitalization or central catheter placement. Current imaging technologies are unsuitable for widespread monitoring of vascular access at a frequency greater than monthly and cannot detect rapidly progressing access dysfunction. Early detection can identify patients for imaging and enable surgical treatment planning.

In this chapter, we review the signal processing methods of analyzing the sounds of blood flow (bruits) using phonoangiography. This method relies on acoustic features produced from turbulent blood flow. We discuss the custom signal processing techniques used for de-noising of the signal and feature extraction to reduce the dimensionality of wavelet-processed audio signals. To move toward a device suitable for use in dialysis centers, we developed a multichannel-flexible piezoelectric microphone to record bruits from multiple locations. This piezoelectric microphone in combination with the signal processing technique could localize stenosis within 1 cm of the actual location and differentiate degree of stenosis into three grades (mild, moderate, and severe) in vitro. These results were confirmed with statistical analysis and the performance of the feature was also tested with a threshold-based stenosis detection classifier to mimic a clinical screening scenario aimed at identifying at-risk patients.


Phonoangiogram Bruit Vascular access Stenosis Flexible microphone PVDF Microphone Frequency-domain linear prediction Wavelet Spectral centroid Spectral flux 



This work was supported in part by RX001968-01 from US Dept. of Veterans Affairs Rehabilitation Research and Development Service, the Advanced Platform Technology Center of the Louis Stokes Cleveland Veterans Affairs Medical Center, and Case Western Reserve University. The contents do not represent the views of the US Government.


  1. 1.
    Sung, P. H., Kan, C. D., Chen, W. L., Jang, L. S., & Wang, J. F. (2015). Hemodialysis vascular access stenosis detection using auditory spectro-temporal features of phonoangiography. Medical & Biological Engineering & Computing, 53(5), 393–403.CrossRefGoogle Scholar
  2. 2.
    Pisoni, R. L., Zepel, L., Port, F. K., & Robinson, B. M. (2015). Trends in US vascular access use, patient preferences, and related practices: An update From the US DOPPS Practice Monitor With International Comparisons. American Journal of Kidney Diseases, 65(6), 905–915.CrossRefGoogle Scholar
  3. 3.
    Feldman, H. I., Kobrin, S., & Wasserstein, A. (1996). Hemodialysis vascular access morbidity. Journal of Amerian Society of Nephrology, 7(4), 523–535.Google Scholar
  4. 4.
    Cayco, A. V., Abu-Alfa, A. K., Mahnensmith, R. L., & Perazella, M. A. (1998). Reduction in arteriovenous graft impairment: Results of a vascular access surveillance protocol. American Journal of Kidney Diseases, 32, 302–308.CrossRefGoogle Scholar
  5. 5.
    Sehgal, A. R., Dor, A., & Tsai, A. C. (2001). Morbidity and cost implications of inadequate hemodialysis. American Journal of Kidney Diseases, 37(6), 1223–1231.CrossRefGoogle Scholar
  6. 6.
    Lacson, E., Wang, W., Lazarus, J. M., Hakim, R. M., & Hakim, R. M. (2010). Change in vascular access and hospitalization risk in long-term hemodialysis patients. Clinical Journal of the American Society of Nephrology, 5(11), 1996–2003.CrossRefGoogle Scholar
  7. 7.
    Duque, J. C., Tabbara, M., Martinez, L., Cardona, J., Vazquez-Padron, R. I., & Salman, L. H. (2017). Dialysis arteriovenous fistula failure and angioplasty: Intimal hyperplasia and other causes of access failure. American Journal of Kidney Diseases, 69(1), 147–151.CrossRefGoogle Scholar
  8. 8.
    Roy-Chaudhury, P., Sukhatme, V. P., & Cheung, A. K. (2006). Hemodialysis vascular access dysfunction: A cellular and molecular viewpoint. Journal of American Society of Nephrology, 17(4), 1112–1127.CrossRefGoogle Scholar
  9. 9.
    Medicare claims processing manual. Publication # 100-04. Retrieved June 1, 2019, from
  10. 10.
    Hemodialysis | NIDDK. [Online]. Retrieved January 4, 2019, from
  11. 11.
    Seo, J. H., & Mittal, R. (2012). A coupled flow-acoustic computational study of bruits from a modeled stenosed artery. Medical & Biological Engineering & Computing, 50, 1025–1035.CrossRefGoogle Scholar
  12. 12.
    Krivitski, N. (2014). Why vascular access trials on flow surveillance failed. The Journal of Vascular Access, 15(7_Suppl), 15–19.CrossRefGoogle Scholar
  13. 13.
    White, J. J., Ram, S. J., Jones, S. A., Schwab, S. J., & Paulson, W. D. (2006). Influence of luminal diameters on flow surveillance of hemodialysis grafts: insights from a mathematical model. Clinical Journal of the American Society of Nephrology, 1(5), 972–978.CrossRefGoogle Scholar
  14. 14.
    Moist, L., & Lok, C. E. (2019). Con: Vascular access surveillance in mature fistulas: Is it worthwhile? Nephrology, Dialysis, Transplantation, 34, 1106–1111.CrossRefGoogle Scholar
  15. 15.
    Tessitore, N., Bedogna, V., Verlato, G., & Poli, A. (2014). The rise and fall of access blood flow surveillance in arteriovenous fistulas. Seminars in Dialysis, 27(2), 108–118.CrossRefGoogle Scholar
  16. 16.
    Duncan, G. W., Gruber, J. O., Dewey, C. F., Myers, G. S., & Lees, R. S. (1975). Evaluation of carotid stenosis by phonoangiography. The New England Journal of Medicine, 293(22), 1124–1128.CrossRefGoogle Scholar
  17. 17.
    Chen, W.-L., Chen, T., Lin, C.-H., Chen, P.-J., & Kan, C.-D. (2013). Phonoangiography with a fractional order chaotic system-a new and easy algorithm in analyzing residual arteriovenous access stenosis. Medical & Biological Engineering & Computing, 51(9), 1011–1019.CrossRefGoogle Scholar
  18. 18.
    Majerus, S. J. A., Knauss, T., Mandal, S., Vince, G., & Damaser, M. S. (2018). Bruit-enhancing phonoangiogram filter using sub-band autoregressive linear predictive coding. In 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 1416–1419).CrossRefGoogle Scholar
  19. 19.
    Doyle, D. J., Mandell, D. M., & Richardson, R. M. (2002). Monitoring hemodialysis vascular access by digital phonoangiography. Annals of Biomedical Engineering, 30(7), 982.CrossRefGoogle Scholar
  20. 20.
    Allon, M., & Robbin, M. L. (2009). Hemodialysis vascular access monitoring: current concepts. Hemodialysis International, 13(2), 153–162.CrossRefGoogle Scholar
  21. 21.
    Du, Y.-C., Chen, W.-L., Lin, C.-H., Kan, C.-D., & Wu, M.-J. (2015). Residual stenosis estimation of arteriovenous grafts using a dual-channel phonoangiography with fractional-order features. IEEE Journal of Biomedical and Health Informatics, 19(2), 590–600.CrossRefGoogle Scholar
  22. 22.
    Du, Y.-C., Kan, C.-D., Chen, W.-L., & Lin, C.-H. (2014). estimating residual stenosis for an arteriovenous shunt using a flexible fuzzy classifier. Computing in Science & Engineering, 16(6), 80–91.CrossRefGoogle Scholar
  23. 23.
    Wu, M.-J., et al. (2015). Dysfunction screening in experimental arteriovenous grafts for hemodialysis using fractional-order extractor and color relation analysis. Cardiovascular Engineering and Technology, 6(4), 463–473.CrossRefGoogle Scholar
  24. 24.
    Mansy, H. A., Hoxie, S. J., Patel, N. H., & Sandler, R. H. (2005). Computerised analysis of auscultatory sounds associated with vascular patency of haemodialysis access. Medical & Biological Engineering & Computing, 43(1), 56–62.CrossRefGoogle Scholar
  25. 25.
    Shinzato, T., Nakai, S., Takai, I., Kato, T., Inoue, I., & Maeda, K. (1993). A new wearable system for continuous monitoring of arteriovenous fistulae. ASAIO Journal, 39(2), 137–140.CrossRefGoogle Scholar
  26. 26.
    Hsien-Yi Wang, H.-Y., Cho-Han Wu, C.-H., Chien-Yue Chen, C.-Y., & Bor-Shyh Lin, B.-S. (2014). Novel noninvasive approach for detecting arteriovenous fistula stenosis. IEEE Transactions on Biomedical Engineering, 61(6), 1851–1857.CrossRefGoogle Scholar
  27. 27.
    Chen, W.-L., Lin, C.-H., Chen, T., Chen, P.-J., & Kan, C.-D. (2013). Stenosis detection using Burg method with autoregressive model for hemodialysis patients. Journal of Medical and Biological Engineering., 33(4), 356.CrossRefGoogle Scholar
  28. 28.
    Obando, P. V., & Mandersson, B. (2012). Frequency tracking of resonant-like sounds from audio recordings of arterio-venous fistula stenosis. In 2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops (pp. 771–773).CrossRefGoogle Scholar
  29. 29.
    Vesquez, P. O., Marco, M. M., & Mandersson, B. (2009). Arteriovenous fistula stenosis detection using wavelets and support vector machines. In 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (pp. 1298–1301).CrossRefGoogle Scholar
  30. 30.
    Wang, Y.-N., Chan, C.-Y., & Chou, S.-J. (2011). The detection of arteriovenous fistula stenosis for hemodialysis based on wavelet transform. International Journal of Advanced Computer Science, 1(1), 16–22.Google Scholar
  31. 31.
    Sato, T., Tsuji, K., Kawashima, N., Agishi, T., & Toma, H. (2006). Evaluation of blood access dysfunction based on a wavelet transform analysis of shunt murmurs. Journal of Artificial Organs, 9(2), 97–104.CrossRefGoogle Scholar
  32. 32.
    Chen, W.-L., Kan, C.-D., Lin, C.-H., Chen, W.-L., Kan, C.-D., & Lin, C.-H. (2014). Arteriovenous shunt stenosis evaluation using a fractional-order Fuzzy Petri net based screening system for long-term hemodialysis patients. Journal of Biomedical Science and Engineering, 07(05), 258–275.CrossRefGoogle Scholar
  33. 33.
    Gram, M., et al. (2011). Stenosis detection algorithm for screening of arteriovenous fistulae. In K. Dremstrup, S. Rees, & M. Ø. Jensen (Eds.), 15th Nordic-Baltic Conference on Biomedical Engineering and Medical Physics (NBC 2011). IFMBE Proceedings (Vol. 34). Berlin: Springer.Google Scholar
  34. 34.
    Todo, A., et al. (2012). Frequency analysis of shunt sounds in the arteriovenous fistula on hemodialysis patients. In The 6th International Conference on Soft Computing and Intelligent Systems, and The 13th International Symposium on Advanced Intelligence Systems (pp. 1113–1118).CrossRefGoogle Scholar
  35. 35.
    Chen, W.-L., Chen, T., Lin, C.-H., Chen, P.-J., & Kan, C.-D. (2013). Phonographic signal with a fractional-order chaotic system: a novel and simple algorithm for analyzing residual arteriovenous access stenosis. Medical & Biological Engineering & Computing, 51(9), 1011–1019.CrossRefGoogle Scholar
  36. 36.
    Munguia, M. M., & Mandersson, B. (2011). Analysis of the vascular sounds of the arteriovenous fistula’s anastomosis. In 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (pp. 3784–3787).CrossRefGoogle Scholar
  37. 37.
    Grochowina, M., Leniowska, L., & Dulkiewicz, P. (2014). Application of artificial neural networks for the diagnosis of the condition of the arterio-venous fistula on the basis of acoustic signals. Brain Informatics and Health, pp. 400–411. Scholar
  38. 38.
    Rousselot, L. (2014). Acoustical monitoring of model system for vascular access in haemodialysis. Master thesis. Retrieved from
  39. 39.
    Gaupp, S., Wang, Y., How, T. V., & Fish, P. J. (2000). Characterization of vortices using pulsed-wave Doppler ultrasound. Proceedings of the Institution of Mechanical Engineering Part H: Journal of Engineering in Medicine, 214(6), 677–684.CrossRefGoogle Scholar
  40. 40.
    Gårdhagen, R. (2013). Turbulent flow in constricted blood vessels: Quantification of wall shear stress using large Eddy simulation. PhD dissertation, Linköping.
  41. 41.
    Athineos, M., & Ellis, D. P. W. (2003). Frequency-domain linear prediction for temporal features. In 2003 IEEE Workshop on Automatic Speech Recognition and Understanding (IEEE Cat. No.03EX721), St Thomas, VI, (pp. 261–266).
  42. 42.
    Athineos, M., & Ellis, D. P. W. (2007). Autoregressive modeling of temporal envelopes. IEEE Transactions on Signal Processing, 55(11), 5237–5245.MathSciNetzbMATHCrossRefGoogle Scholar
  43. 43.
    Tzanetakis, G., & Cook, P. (2002). Musical genre classification of audio signals. IEEE Transactions on Speech and Audio Processing, 10(5), 293.CrossRefGoogle Scholar
  44. 44.
    Chin, S., Panda, B., Damaser, M. S., & Majerus, S. J. A. (2018). Stenosis characterization and identification for dialysis vascular access. In IEEE Signal Processing in Medicine and Biology Symposium 2018.
  45. 45.
    Panda, B., Chin, S., Mandal, S., & Majerus, S. (2018). Skin-coupled pvdf microphones for noninvasive vascular blood sound monitoring. In 2018 IEEE Signal Processing in Medicine and Biology Symposium (SPMB) (pp. 1–4).Google Scholar
  46. 46.
    Gould, K. L. (2013). Effects of stenosis on coronary flow. Cleveland Clinic Journal of Medicine, 47(3), 140–144.CrossRefGoogle Scholar
  47. 47.
    Bluestein, D., Gutierrez, C., Londono, M., & Schoephoerster, R. T. (1999). Vortex shedding in steady flow through a model of an arterial stenosis and its relevance to mural platelet deposition. Annals of Biomedical Engineering, 27(6), 763–773.CrossRefGoogle Scholar
  48. 48.
    Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861–874.MathSciNetCrossRefGoogle Scholar
  49. 49.
    Ruopp, M. D., Perkins, N. J., Whitcomb, B. W., & Schisterman, E. F. (2008). Youden Index and optimal cut-point estimated from observations affected by a lower limit of detection. Biometrical Journal, 50(3), 419–430.MathSciNetCrossRefGoogle Scholar
  50. 50.
    Schisterman, E. F., Perkins, N. J., Liu, A., & Bondell, H. (2005). Optimal cut-point and its corresponding Youden Index to discriminate individuals using pooled blood samples. Epidemiology, 16(1), 73–81.CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Binit Panda
    • 1
    • 2
  • Stephanie Chin
    • 1
    • 3
  • Soumyajit Mandal
    • 2
  • Steve J. A. Majerus
    • 3
    Email author
  1. 1.Department of Biomedical EngineeringCase Western Reserve UniversityClevelandUSA
  2. 2.Department of Electrical Engineering and Computer ScienceCase Western Reserve UniversityClevelandUSA
  3. 3.Advanced Platform Technology Center, Louis Stokes Cleveland Veterans Affairs Medical CenterClevelandUSA

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