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International Conference on Genetic and Evolutionary Computing

GEC 2015: Genetic and Evolutionary Computing pp 331–339Cite as

A Study on sEMG Pattern Classification Method of Muscles of Respiration

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 388))

Abstract

The aim of this paper studies the possibility of new method to diagnose the sleep apnea syndrome. In this paper, we propose analysis method for the pattern classification of breathing from surface electromyogram. First, we measure surface electromyogram that obtained from the surface electrodes attached to crest of neck and mandible muscles. Next, we obtain the peak signal of active from Wavelet transformation of surface electromyogram. We calculate the pattern classification by using the k-nearest neighbor method. From the experimental results, our analysis method was possible to obtain high pattern classification rate when k is 6.

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References

  1. Kizuka, T., Masuda, T., Kiryu, T., Sadoyama, T.: Biomechanism Library -Practical Usage of Surface Electromyogram-

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  2. Okitsu, T., Arita, M., Sonoda, S., Ota, T., Hotta, F., Honda, T., Chino, N.: The Surface Electromyography on Suprahyoid Muscles during Swallowing

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Author information

Authors and Affiliations

  1. Department of Electrical and Electronic Engineering, University of Miyazaki, Gakuenkibanadainishi 1-1, Miyazaki-City, Miyazaki, Japan

    Ryosuke Kokubo & Shogo Okazaki

  2. Department of Environmental Robotics Engineering, University of Miyazaki, Gakuenkibanadainishi 1-1, Miyazaki-City, Miyazaki, Japan

    Misaki Shoitizono & Hiroki Tamura

  3. Department of Electrical and System Engineering, University of Miyazaki, Gakuenkibanadainishi 1-1, Miyazaki-City, Miyazaki, Japan

    Koichi Tanno

Authors
  1. Ryosuke Kokubo
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  2. Shogo Okazaki
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  3. Misaki Shoitizono
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  4. Hiroki Tamura
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  5. Koichi Tanno
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Corresponding author

Correspondence to Ryosuke Kokubo .

Editor information

Editors and Affiliations

  1. Faculty of Engineering, University of Miyazaki, Miyazaki, Japan

    Thi Thi Zin

  2. School of Computer Science and Tech..., Harbin Institute of Technology, Shenzhen Graduate School , Shenzhen, China

    Jerry Chun-Wei Lin

  3. College of Information Science and Engg, Fujian University of Technology, Fuzhou, China

    Jeng-Shyang Pan

  4. Faculty of Engineering, University of Miyazaki, Miyazaki, Japan

    Pyke Tin

  5. Faculty of Engineering, University of Miyazaki, Miyazaki, Japan

    Mitsuhiro Yokota

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© 2016 Springer International Publishing Switzerland

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Cite this paper

Kokubo, R., Okazaki, S., Shoitizono, M., Tamura, H., Tanno, K. (2016). A Study on sEMG Pattern Classification Method of Muscles of Respiration. In: Zin, T., Lin, JW., Pan, JS., Tin, P., Yokota, M. (eds) Genetic and Evolutionary Computing. GEC 2015. Advances in Intelligent Systems and Computing, vol 388. Springer, Cham. https://doi.org/10.1007/978-3-319-23207-2_33

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  • DOI: https://doi.org/10.1007/978-3-319-23207-2_33

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-23206-5

  • Online ISBN: 978-3-319-23207-2

  • eBook Packages: EngineeringEngineering (R0)

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