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Low Cost Portable Solution for Real-Time Complete Detection and Analysis of Heart Sound Components

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Abstract

Detection of heart sound and analysis is required to determine heart rate, heart sound components (S1, S2 and murmurs), and is an important step to diagnose cardiac disorder. The most important part of detection method is to achieve a noise free signal staring from detection to separation of heart components to improve the accuracy of signal analysis. Though several innovative individual methods are found in the literature related to detection, noise filter or analyse the characteristic patterns of the heart signals, but some of them have computationally complex algorithms, some are equipped with expensive instruments and to the best of our knowledge, it is rare to found a low cost complete solution starting from detection to analysis with simple instruction based methodology to be handled by the literate ordinary people. There is also difficulty to implement them on real-time portable systems and also require medical expertise to handle the equipment and perform the operations. In this work, we propose an innovative simple solution for complete heart sound detection and analysis system with low cost portable heart sound detection devices, best effort wavelet de-noising and computationally less complex start and end point detection algorithms to separate and analyse heart sound components. MATLAB and DSP processor (TMS320C6713) has been used for the real time implementation of the algorithms. This process will be very helpful in a place with low medical establishment to take mass scale measurement and analysis to be finally sent to doctor for advise and treatment. We also provide frequency domain power spectrum density of the detected heart sound components for better diagnosis. At the end, we provide a comparative table with the available literature.

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References

  1. Prabhakaran, D., Jeemon, P., & Roy, A. (2016). Cardiovascular diseases in India: current epidemiology and future directions. Circulation, 133(16), 1605–1620.

    Google Scholar 

  2. Baykal, A., Ider, Y. Z., & Koymen, H. (1995). Distribution of aortic mechanical prosthetic valve closure sound model parameters on the surface of the chest. IEEE Transactions on Biomedical Engineering, 42(4), 358–370.

    Google Scholar 

  3. Xu, J., Durand, L., & Pibarot, P. (2000). Nonlinear transient chirp signal modeling of the aortic and pulmonary components of the second heart sound. IEEE Transactions on Biomedical Engineering, 47(10), 1328–1335.

    Google Scholar 

  4. Chakrabarti, T., et al. (2015). Phonocardiogram signal analysis-practices, trends and challenges: A critical review. In 2015 international conference and workshop on computing and communication (IEMCON). Washington: IEEE.

  5. Watrous, R. (2001). If the heart could speak picture of fall. In 2001 Siemens Webzine.

  6. Tavel, M. E. (1972). Clinical phonocardiography and external pulse recording. Chicago: Year Book Medical Publishers.

    Google Scholar 

  7. Meziani, F., Debbal, S. M., & Atbi, A. (2012). Analysis of phonocardiogram signals using wavelet transform. Journal of Medical Engineering & Technology, 36(6), 283–302.

    Google Scholar 

  8. Lehner, R. J., & Rangayyan, R. M. (1987). A three-channel microcomputer system for segmentation and characterization of the phonocardiogram. IEEE Transactions on Biomedical Engineering, 6, 485–489.

    Google Scholar 

  9. Liang, H., Lukkarinen, S., & Hartimo, I. (1997). Heart sound segmentation algorithm based on heart sound envelogram. In Computers in cardiology 1997. Washington: IEEE.

  10. Sun, S., et al. (2014). Automatic moment segmentation and peak detection analysis of heart sound pattern via short-time modified Hilbert transform. Computer Methods and Programs in Biomedicine, 114(3), 219–230.

    Google Scholar 

  11. Salman, A. H., et al. (2015). Automatic segmentation and detection of heart sound components S1, S2, S3 and S4. In 2015 4th international conference on instrumentation, communications, information technology, and biomedical engineering (ICICI-BME). Washington: IEEE.

  12. Naseri, H., & Homaeinezhad, M. R. (2013). Detection and boundary identification of phonocardiogram sounds using an expert frequency-energy based metric. Annals of Biomedical Engineering, 41(2), 279–292.

    Google Scholar 

  13. Kwak, C., & Kwon, O.-W. (2012). Cardiac disorder classification by heart sound signals using murmur likelihood and hidden Markov model state likelihood. IET Signal Processing, 6(4), 326–334.

    MathSciNet  Google Scholar 

  14. Schmidt, S. E., et al. (2010). Segmentation of heart sound recordings by a duration-dependent hidden Markov model. Physiological Measurement, 31(4), 513.

    Google Scholar 

  15. Sedighian, P., et al. (2014). Pediatric heart sound segmentation using Hidden Markov model. In Engineering in Medicine and Biology Society (EMBC), 2014 36th annual international conference of the IEEE. Washington: IEEE.

  16. Tu, Z., et al. (2010). Improved methods for detecting main components of heart sounds. In 2010 sixth international conference on natural computation (ICNC) (Vol. 7). Washington: IEEE.

  17. Papadaniil, C. D., & Hadjileontiadis, L. J. (2014). Efficient heart sound segmentation and extraction using ensemble empirical mode decomposition and kurtosis features. IEEE Journal of Biomedical and Health Informatics, 18(4), 1138–1152.

    Google Scholar 

  18. Springer, D. B., Tarassenko, L., & Clifford, G. D. (2016). Logistic regression-HSMM-based heart sound segmentation. IEEE Transactions on Biomedical Engineering, 63(4), 822–832.

    Google Scholar 

  19. Debbal, S. M., & Bereksi-Reguig, F. (2007). Time-frequency analysis of the first and the second heartbeat sounds. Applied Mathematics and Computation, 184(2), 1041–1052.

    MathSciNet  MATH  Google Scholar 

  20. Ramović, A., et al. (2017). Wavelet and Teager energy operator (TEO) for heart sound processing and identification. In CMBEBIH 2017 (pp. 495–502). Singapore: Springer.

  21. Varghees, V. N., & Ramachandran, K. I. (2017). Effective heart sound segmentation and murmur classification using empirical wavelet transform and instantaneous phase for electronic stethoscope. IEEE Sensors Journal, 17(12), 3861–3872.

    Google Scholar 

  22. Othman, M. Z., & Khaleel, A. N. (2017). Phonocardiogram signal analysis for murmur diagnosing using Shannon energy envelop and sequenced DWT decomposition. Journal of Engineering Science and Technology, 12(9), 2393–2402.

    Google Scholar 

  23. Leng, S., et al. (2015). The electronic stethoscope. Biomedical Engineering Online, 14(1), 66.

    MathSciNet  Google Scholar 

  24. Nabih-Ali, M., El-Dahshan, E. A., & Yahia, A. S. (2017). A review of intelligent systems for heart sound signal analysis. Journal of Medical Engineering & Technology, 41(7), 553–563.

    Google Scholar 

  25. Ghasemzadeh, H., et al. (2013). Wireless medical-embedded systems: A review of signal-processing techniques for classification. IEEE Sensors Journal, 13(2), 423–437.

    Google Scholar 

  26. Bai, Y.-W., & Lu, C.-L. (2005). The embedded digital stethoscope uses the adaptive noise cancellation filter and the type I Chebyshev IIR bandpass filter to reduce the noise of the heart sound. Enterprise networking and Computing in Healthcare Industry, 2005. In Proceedings of 7th international workshop on HEALTHCOM 2005. Washington: IEEE.

  27. Shin, J. Y., et al. (2013). Development of smartphone-based stethoscope system. In 2013 13th international conference on control, automation and systems (ICCAS). Washington: IEEE.

  28. Kumar, D., et al. (2007). Near real time noise detection during heart sound acquisition. In Signal processing conference, 2007 15th European. Washington: IEEE.

  29. Tang, H., Li, T., & Qiu, T. (2010). Noise and disturbance reduction for heart sounds in cycle-frequency domain based on nonlinear time scaling. IEEE Transactions on Biomedical Engineering, 57(2), 325–333.

    Google Scholar 

  30. Salman, A. H., et al. (2015). Performance comparison of denoising methods for heart sound signal. In 2015 international symposium on intelligent signal processing and communication systems (ISPACS). Washington: IEEE.

  31. Varady, P. (2001). Wavelet-based adaptive denoising of phonocardiographic records. In Engineering in Medicine and Biology Society, 2001. Proceedings of the 23rd annual international conference of the IEEE (Vol. 2). Washington: IEEE.

  32. Vonesch, C., Blu, T., & Unser, M. (2007). Generalized Daubechies wavelet families. IEEE Transactions on Signal Processing, 55(9), 4415–4429.

    MathSciNet  MATH  Google Scholar 

  33. Clark, V. L., & Kruse, J. A. (1990). Clinical methods: The history, physical, and laboratory examinations. JAMA, 264(21), 2808–2809.

    Google Scholar 

  34. Barma, S., et al. (2015). Quantitative measurement of split of the second heart sound (S2). IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB), 12(4), 851–860.

    Google Scholar 

  35. Bunluechokchai, C., & Ussawawongaraya, W. (2009). A wavelet-based factor for classification of heart sounds with mitral regurgitation. International Journal of Applied Biomedical Engineering, 2(1), 44–48.

    Google Scholar 

  36. Xu, J., Durand, L.-G., & Pibarot, P. (2001). Extraction of the aortic and pulmonary components of the second heart sound using a nonlinear transient chirp signal model. IEEE Transactions on Biomedical Engineering, 48(3), 277–283.

    Google Scholar 

  37. Rangayyan, R. M. (2015). Biomedical signal analysis (Vol. 33). New York: Wiley.

    Google Scholar 

  38. DeGroff, C. G., et al. (2001). Artificial neural network-based method of screening heart murmurs in children. Circulation, 103(22), 2711–2716.

    Google Scholar 

  39. All about Heart Rate (Pulse). American Heart Association, 22 August 2017. Retrieved 25 January 2018.

  40. Usui, T., Matsubara, A., & Tanaka, S. (2004). Unconstrained and noninvasive measurement of heartbeat and respiration using an acoustic sensor enclosed in an air pillow. In SICE 2004 annual conference (Vol. 3). Washington: IEEE.

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Correspondence to Arnab Maity.

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Mandal, D., Maity, A. & Saha Misra, I. Low Cost Portable Solution for Real-Time Complete Detection and Analysis of Heart Sound Components. Wireless Pers Commun 107, 523–547 (2019). https://doi.org/10.1007/s11277-019-06287-0

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