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Real-Time PCG Diagnosis Using FPGA

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Intelligent Systems Design and Applications (ISDA 2018 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 940))

Abstract

Phono-Cardio-Gram (PCG) is an effective technique of detecting various heart abnormalities and malfunctions. Several PCG segmentation algorithms, each with its advantages and disadvantages, have been developed and tested. Unfortunately, most of these algorithms fail to diagnose heart conditions in real time. This paper introduces a real-time methodology by utilizing the field programmable gate array (FPGA) hardware to speed up the processing time very successfully. The type of FPGA hardware is Altera DE2 Cyclone II board with NIOS II soft processor. The algorithm of discrete wavelet transforms (DWT) with Shannon energy and spectra signal are used to extract the PCG features. The algorithm is also applied to identify the normal heart condition and nine abnormal cases. The results show that the methodology worked extremely efficient and suitable for real-time heart diagnosis purposes.

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References

  1. Goda, M.A., Hajas, P.: Morphological determination of pathological PCG signals by time and frequency domain analysis. In: 2016 Computing in Cardiology Conference (CinC). IEEE (2016)

    Google Scholar 

  2. Mukherjeea, A., Khanb, A.: A fourier series based template matching approach to detect the splitting of second heart sound. IOSR J. VLSI Signal Process. (IOSR-JVSP) 4, 09–13 (2014)

    Article  Google Scholar 

  3. Clifford, G.D., et al.: Classification of normal/abnormal heart sound recordings: the PhysioNet/computing in cardiology challenge 2016. In: 2016 Computing in Cardiology Conference (CinC). IEEE (2016)

    Google Scholar 

  4. Liu, C., et al.: An open access database for the evaluation of heart sound algorithms. Physiol. Measur. 37(12), 2181 (2016)

    Article  Google Scholar 

  5. Schmidt, S., Graebe, M., Toft, E., Struijk, J.: No evidence of nonlinear or chaotic behavior of cardiovascular murmurs. Biomed. Signal Process. Control 6, 157–163 (2011)

    Article  Google Scholar 

  6. Ari, S., Hembram, K., Saha, G.: Detection of cardiac abnormality from PCG signal using LMS based least square SVM classier. Expert Syst. Appl. 37, 8019–8026 (2010)

    Article  Google Scholar 

  7. Wang, P., Lim, C.S., Chauhan, S., Foo, J.Y., Anantharaman, V.: Phonocardiographic signal analysis method using a modified hidden Markov model. Ann. Biomed. Eng. 35, 367–374 (2007)

    Article  Google Scholar 

  8. Saracoglu, R.: Hidden Markov model-based classification of heart valve disease with PCA for dimension reduction. Eng. Appl. Artif. Intell. 25, 1523–1528 (2012)

    Article  Google Scholar 

  9. Quiceno-Manrique, A.F., Godino-Llorente, J.I., Blanco-Velasco, M., Castellanos-Dominguez, G.: Selection of dynamic features based on time-frequency representations for heart murmur detection from phonocardiographic signals. Ann. Biomed. Eng. 38, 118–137 (2010)

    Article  Google Scholar 

  10. El-Segaier, M., et al.: Detection of cardiac pathology: time intervals and spectral analysis. Acta Paediatr. 96(7), 1036–1042 (2007)

    Article  Google Scholar 

  11. Schmidt, S.E., et al.: Acoustic features for the identification of coronary artery disease. IEEE Trans. Biomed. Eng. 62(11), 2611–2619 (2015)

    Article  Google Scholar 

  12. Leng, S., et al.: The electronic stethoscope. Biomed. Eng. Online 14(1), 66 (2015)

    Article  MathSciNet  Google Scholar 

  13. Yuenyong, S., et al.: A framework for automatic heart sound analysis without segmentation. Biomed. Eng. Online 10(1), 13 (2011)

    Article  Google Scholar 

  14. Choi, S., Jiang, Z.: Comparison of envelope extraction algorithms for cardiac sound signal segmentation. Expert Syst. Appl. 34(2), 1056–1069 (2008)

    Article  Google Scholar 

  15. Karar, M.E., El-Brawany, M.: Embedded heart sounds and murmurs generator based on discrete wavelet transform. In: 2016 Fourth International Japan-Egypt Conference on Electronics, Communications and Computers (JEC-ECC). IEEE (2016)

    Google Scholar 

  16. Randhawa, S.K., Singh, M.: Classification of heart sound signals using multi-modal features. Procedia Comput. Sci. 58, 165–171 (2015)

    Article  Google Scholar 

  17. Altera: DE2. Development and education board user manual. Terasic Technologies (2012)

    Google Scholar 

  18. Moslehpour, S., et al.: Design of the Nios II system for the playing of wave files on an Altera DE2 Board. Int. J. Eng. Technol. 5(3), 361 (2013)

    Article  Google Scholar 

  19. Mondal, A., Bhattacharya, P., Saha, G.: An automated tool for localization of heart sound components S1, S2, S3 and S4 in pulmonary sounds using Hilbert transform and Heron’s formula. SpringerPlus 2(1), 512 (2013)

    Article  Google Scholar 

  20. Choudhary, T., Sharma, L.N., Bhuyan, M.K.: Heart sound extraction from sternal seismocardiographic signal. IEEE Signal Process. Lett. 25(4), 482–486 (2018)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of Information Technology and Communications, Baghdad, Iraq

    Mohammed Abdulraheem Fadhel, Omran Al-Shamma & Laith Alzubaidi

  2. Muthanna University, Muthanna, Iraq

    Sameer Razzaq Oleiwi

  3. University of Sumer, Dhi Qar, Iraq

    Bahaa Hussein Taher

  4. Faculty of Science and Engineering, Queensland University of Technology, Brisbane, Australia

    Laith Alzubaidi

Authors
  1. Mohammed Abdulraheem Fadhel
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  2. Omran Al-Shamma
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  3. Sameer Razzaq Oleiwi
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  4. Bahaa Hussein Taher
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  5. Laith Alzubaidi
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Corresponding author

Correspondence to Mohammed Abdulraheem Fadhel .

Editor information

Editors and Affiliations

  1. Machine Intelligence Research Labs, Auburn, WA, USA

    Ajith Abraham

  2. School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India

    Aswani Kumar Cherukuri

  3. Tijuana Institute of Technology, Tijuana, Mexico

    Patricia Melin

  4. Machine Intelligence Research Labs, Auburn, WA, USA

    Niketa Gandhi

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Fadhel, M.A., Al-Shamma, O., Oleiwi, S.R., Taher, B.H., Alzubaidi, L. (2020). Real-Time PCG Diagnosis Using FPGA. In: Abraham, A., Cherukuri, A.K., Melin, P., Gandhi, N. (eds) Intelligent Systems Design and Applications. ISDA 2018 2018. Advances in Intelligent Systems and Computing, vol 940. Springer, Cham. https://doi.org/10.1007/978-3-030-16657-1_48

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