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Early Detection of Heart Symptoms with Convolutional Neural Network and Scattering Wavelet Transformation

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Foundations of Intelligent Systems (ISMIS 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11177))

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Abstract

The paper utilizes Convolutional Neural Network (CNN) for preliminary screening of cardiac pathologies by classifying the signal of heartbeat, recorded by digital stethoscope and mobile devices. The Scattering Wavelet Transformation (SWT) was used for the heartbeat representation. The experiments revealed the optimum concatenation size of SWT windows to obtain the state-of-the-art in the majority of metrics, coming from the PASCAL Classifying Heart Sounds Challenge.

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Notes

  1. 1.

    http://new.who.int/news-room/fact-sheets/detail/the-top-10-causes-of-death.

  2. 2.

    https://stat.gov.pl/obszary-tematyczne/ludnosc/ludnosc/statystyka-zgonow-i-umieralnosci-z-powodu-chorob-ukladu-krazenia,22,1.html.

  3. 3.

    http://ecg.mit.edu/.

  4. 4.

    http://www.ahadata.com/.

  5. 5.

    https://www.physionet.org/challenge/.

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Authors and Affiliations

  1. Multimedia Department, Polish-Japanese Academy of Information Technology, Warsaw, Poland

    Mariusz Kleć

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  1. Mariusz Kleć
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Correspondence to Mariusz Kleć .

Editor information

Editors and Affiliations

  1. Università degli Studi di Bari Aldo Moro, Bari, Italy

    Michelangelo Ceci

  2. American University, Washington, DC, USA

    Nathalie Japkowicz

  3. Hong Kong Baptist University, Kowloon, Hong Kong

    Jiming Liu

  4. University of Cyprus, Nicosia, Cyprus

    George A. Papadopoulos

  5. University of North Carolina, Charlotte, NC, USA

    Zbigniew W. Raś

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Kleć, M. (2018). Early Detection of Heart Symptoms with Convolutional Neural Network and Scattering Wavelet Transformation. In: Ceci, M., Japkowicz, N., Liu, J., Papadopoulos, G., Raś, Z. (eds) Foundations of Intelligent Systems. ISMIS 2018. Lecture Notes in Computer Science(), vol 11177. Springer, Cham. https://doi.org/10.1007/978-3-030-01851-1_3

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  • DOI: https://doi.org/10.1007/978-3-030-01851-1_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-01850-4

  • Online ISBN: 978-3-030-01851-1

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