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Separation of Real Time Heart Sound Signal from Lung Sound Signal Using Neural Network

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Swarm, Evolutionary, and Memetic Computing (SEMCCO 2014)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8947))

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Abstract

While recording lung sounds, an incessant noise source takes place owing to heart sounds. This noise source severely contaminates the breath sound signal and interferes in the analysis of lung sounds. This paper presents a technique for separation of heart sound signal (HSS) from lung sound signal (LSS) using neural network (NN) with real time recorded sound signal. Here two signals are used in neural network noise separation scheme. The two signals are raw signal and reference heart signal. The raw signal is given as input signal to neural network and reference heart signal is used as target signal. The proposed system is applied and the results show the error rate of the desired sound signal (DSS), signal to noise ratio (SNR) and execution time.

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Author information

Authors and Affiliations

  1. Department of ECE, Tejaa Sakthi Institute of Technology for Women, Coimbatore, India

    K. Sathesh

  2. Tejaa Sakthi Institute of Technology for Women, Coimbatore, India

    N. J. R. Muniraj

Authors
  1. K. Sathesh
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  2. N. J. R. Muniraj
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Corresponding author

Correspondence to K. Sathesh .

Editor information

Editors and Affiliations

  1. Department of Electrical Engineering, IIT, New Delhi, India

    Bijaya Ketan Panigrahi

  2. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore

    Ponnuthurai Nagaratnam Suganthan

  3. Electronics and Communication Sciences Unit, Indian Statistical Institute, Kolkata, India

    Swagatam Das

Appendix

Appendix

1.1 Digital Stethoscope Details

Hardware used - digital stethoscope

Sampling frequency used - 44.1 kHz

Open source software used -Thinklabs phonocardiography.

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Sathesh, K., Muniraj, N.J.R. (2015). Separation of Real Time Heart Sound Signal from Lung Sound Signal Using Neural Network. In: Panigrahi, B., Suganthan, P., Das, S. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2014. Lecture Notes in Computer Science(), vol 8947. Springer, Cham. https://doi.org/10.1007/978-3-319-20294-5_25

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  • DOI: https://doi.org/10.1007/978-3-319-20294-5_25

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-20293-8

  • Online ISBN: 978-3-319-20294-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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