Deep Learning-Based Biological Signal Analysis for Assisting Cardiovascular Disease Diagnosis on Mobile Environment 


Vol. 42,  No. 7, pp. 1470-1476, Jul.  2017
10.7840/kics.2017.42.7.1470


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  Abstract

Human body consists of various organic systems, and each of these generates peculiar biological signals when it functions. In particular, biological sound involves important diagnostic information of relevant organ, which is manually analyzed by professionally well-trained medical staff. In this paper, we proposed deep learning-based biological sound analysis for assisting cardiovascular disease diagnosis. We confirmed the validity of our method through published real cardiac sound dataset. As a result, we convinced potentiality of applying deep learning method to biological signal-based diagnosis for cardiovascular disease. Furthermore, we expect our work to be an important foundation for development of medical diagnostic system on mobile environment.

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  Cite this article

[IEEE Style]

J. Lee, H. Kim, S. Kim, J. Song, S. Yoon, "Deep Learning-Based Biological Signal Analysis for Assisting Cardiovascular Disease Diagnosis on Mobile Environment," The Journal of Korean Institute of Communications and Information Sciences, vol. 42, no. 7, pp. 1470-1476, 2017. DOI: 10.7840/kics.2017.42.7.1470.

[ACM Style]

Jaekoo Lee, Hyunjae Kim, Sungwon Kim, Jongyoon Song, and Sungroh Yoon. 2017. Deep Learning-Based Biological Signal Analysis for Assisting Cardiovascular Disease Diagnosis on Mobile Environment. The Journal of Korean Institute of Communications and Information Sciences, 42, 7, (2017), 1470-1476. DOI: 10.7840/kics.2017.42.7.1470.

[KICS Style]

Jaekoo Lee, Hyunjae Kim, Sungwon Kim, Jongyoon Song, Sungroh Yoon, "Deep Learning-Based Biological Signal Analysis for Assisting Cardiovascular Disease Diagnosis on Mobile Environment," The Journal of Korean Institute of Communications and Information Sciences, vol. 42, no. 7, pp. 1470-1476, 7. 2017. (https://doi.org/10.7840/kics.2017.42.7.1470)

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pISSN : 1226 - 4717
eISSN : 2287 - 3880