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International Conference on Intelligent Systems Design and Applications

ISDA 2018 2018: Intelligent Systems Design and Applications pp 518–529Cite as

Real-Time PCG Diagnosis Using FPGA

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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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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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Cite this paper

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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