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Neural network based real-time heart sound monitor using a wireless wearable wrist sensor

  • W. Y. Shi
  • J.-C. Chiao
Article

Abstract

A new method is presented using a wearable wrist sensor to estimate acoustic parameters S1 and S2 of the heart sounds based on the neural network technique. Using the signal processing method, the heart conditions can be analyzed and monitored in real time and potentially in a long term with a wrist device. The velocities and time delays of the cardiac pulse waves in blood vessels were experimentally acquired and calculated at different artery locations on the human body. Signal attenuation of the pulses from the heart to the wrist radial artery was analyzed and a pulse-waveform travel model in blood vessels was proposed. A band-pass filter is applied to the pulse waves at various artery locations to reveal the heart sound features S1 and S2 existed in the pulse waves. In order to obtain accurate acoustic parameters, a neural network with two layers and 500 nonlinear tansig neurons was employed to estimate the heart sounds using the pulse waveforms from the wrist radial artery. It is encouraging to find that the acoustic parameters of estimated heart sounds by the trained neural network have only 1% average errors compared with the original heart sounds. The effects of various analog-to-digital conversion resolutions and sample rates were empirically analyzed. When the maximum value of errors is allowed within 2.15%, a 10,000-Hz sample rate and 12-bit resolution should be an appropriate selection for lower power consumption. Using the trained neural network, the new estimation method has been verified by a sensor with Bluetooth communication strapped on the wrist, thus mobility is not limited for the person whose heart sounds need to be monitored.

Keywords

Wireless sensor networks Stethoscope Digital signal processing Neural network 

Notes

Acknowledgements

Authors sincerely thank the technical support by NeoScBio Limited.

References

  1. 1.
    Yoo, J., Yan, L., Lee, S., Kim, H., & Yoo, H. J. (2009). A wearable ECG acquisition system with compact planar-fashionable circuit board-based shirt. IEEE Transactions on Information Technology in Biomedicine, 13(6), 897–902.CrossRefGoogle Scholar
  2. 2.
    Sprague, H. B. (1957). History and present status of phonocardiography. IRE Transactions on Medical Electronics, PGME-9, 2–3.Google Scholar
  3. 3.
    Debbal, S. M., & Bereksi-Reguig, F. (2008). Computerized heart sounds analysis. Computers in Biology and Medicine, 38(2), 263–280.CrossRefMATHGoogle Scholar
  4. 4.
    Gupta, C. N., Palaniappan, R., Rajan, S., Swaminathan, S., & Krishnan, S. M. (2005). Segmentation and classification of heart sounds. In IEEE Canadian Conference on Electrical and Computer Engineering, 2005 (pp. 1674–1677).Google Scholar
  5. 5.
    Shi, W. Y., Mays, J., & Chiao, J. C. (2016). Wireless stethoscope for recording heart and lung sound. In 2016 IEEE Topical Conference on Biomedical Wireless Technologies, Networks, and Sensing Systems (pp. 1–4).Google Scholar
  6. 6.
    Tang, H., Li, T., Park, Y., & Qiu, T. (2010). Separation of heart sound signal from noise in joint cycle frequency–time–frequency domains based on fuzzy detection. IEEE Transactions on Biomedical Engineering, 57(10), 2438–2447.CrossRefGoogle Scholar
  7. 7.
    Yan, H., Jiang, Y., Zheng, J., Peng, C., & Li, Q. (2006). A multilayer perceptron-based medical decision support system for heart disease diagnosis. Expert Systems with Applications, 30(2), 272–281.CrossRefGoogle Scholar
  8. 8.
    Turkoglu, I., & Arslan, A. (2001). An intelligent pattern recognition system based on neural network and wavelet decomposition for interpretation of heart sounds. In Proceedings of the 23rd Annual International Conference of the IEEE on Engineering in Medicine and Biology Society, 2001 (Vol. 2, pp. 1747–1750).Google Scholar
  9. 9.
    Chen, T. E., Yang, S. I., Ho, L. T., Tsai, K. H., Chen, Y. H., Chang, Y. F., et al. (2017). S1 and S2 heart sound recognition using deep neural networks. IEEE Transactions on Biomedical Engineering, 64(2), 372–380.CrossRefGoogle Scholar
  10. 10.
    Shivhare, V. K., Sharma, S. N., & Shakya, D. K. (2015). Detection of heart sounds S1 and S2 using optimized S-transform and back—Propagation Algorithm. In Bombay Section Symposium (IBSS) (pp. 1–6).Google Scholar
  11. 11.
    Mokhlessi, O., Mehrshad, N., & Rad, H. M. (2010). Utilization of 4 types of Artificial Neural Network on the diagnosis of valve-physiological heart disease from heart sounds. In 2010 17th Iranian Conference of IEEE on Biomedical Engineering (ICBME) (pp. 1–4).Google Scholar
  12. 12.
    de Vos, J. P., & Blanckenberg, M. M. (2007). Automated pediatric cardiac auscultation. IEEE Transactions on Biomedical Engineering, 54(2), 244–252.CrossRefGoogle Scholar
  13. 13.
    Barschdorff, D., Femmer, U., & Trowitzsch, E. (1995). Automatic phonocardiogram signal analysis in infants based on wavelet transforms and artificial neural networks. In Computers in Cardiology 1995 IEEE (pp. 753–756).Google Scholar
  14. 14.
    Bramwell, J. C., & Hill, A. V. (1922). The velocity of the pulse wave in man. Proceedings of the Royal Society of London. Series B, Containing Papers of a Biological Character, 93(652), 298–306.CrossRefGoogle Scholar
  15. 15.
    Mandal, S., Turicchia, L., & Sarpeshkar, R. (2010). A low-power, battery-free tag for body sensor networks. IEEE Pervasive Computing, 9(1), 71–77.CrossRefGoogle Scholar
  16. 16.
    Shi, W. Y, & Chiao J. C. (2016). Neural network based real-time heart sound monitor using a wireless wearable wrist sensor. In 2016 IEEE Dallas Circuits and Systems Conference (DCAS) (pp. 1–4).Google Scholar
  17. 17.
    Ertel, P. Y., Lawrence, M., Brown, R. K., & Stern, A. M. (1966). Stethoscope acoustics. Circulation, 34(5), 889–898.CrossRefGoogle Scholar
  18. 18.
    Nuttall, A. (1981). Some windows with very good sidelobe behavior. IEEE Transactions on Acoustics, Speech, and Signal Processing, 29(1), 84–91.CrossRefGoogle Scholar
  19. 19.
    Sa-ngasoongsong, A., & Bukkapatnam, S. T. (2010). Wireless transmission of sensor signals for phonocardiology applications. In 2010 IEEE Sensors (pp. 1975–1978).Google Scholar
  20. 20.
    Demuth, H., & Beale, M. (2009). Matlab neural network toolbox user’s guide version 6. Natick: The MathWorks Inc.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Department of Electrical EngineeringThe University of Texas at ArlingtonArlingtonUSA

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