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EMG Signal Classification with Effective Features for Diagnosis

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Image Processing and Capsule Networks (ICIPCN 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1200))

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Abstract

Electromyography (EMG) signals are broadly used in various clinical or biomedical applications, prosthesis or rehabilitation devices, Muscle-Computer Interface (MCI), Evolvable Hardware Chip (EHW) development and many other applications. Electromyography (EMG) signal records the myopathy from nonlinear subjects in both time domain and frequency domain. It becomes very difficult to classify these various statuses. In this paper, a feature extraction and classification method of healthy and myopathy EMG signals are proposed where two features have been extracted on both healthy and myopathy EMG. Mean Squared Error (MSE) has been calculated to observe which feature will give better classification result. Then SVM is used to classify the extracted results. To evaluate the proposed model, a standard dataset collected from physionet.org is used where it shows higher accuracy than the conventional methods.

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Author information

Authors and Affiliations

  1. Department of Computer Science, South Asian University, New Delhi, India

    Abdul Wadud

  2. Institute of Computer Science, Bangladesh Atomic Energy Commission, Dhaka, Bangladesh

    Md. Imran Hossain Showrov

Authors
  1. Abdul Wadud
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  2. Md. Imran Hossain Showrov
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Corresponding author

Correspondence to Md. Imran Hossain Showrov .

Editor information

Editors and Affiliations

  1. Department of Electrical Engineering, Dayeh University, Changhua, Taiwan

    Joy Iong-Zong Chen

  2. Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial, Departamento de Engenharia Mecânica, Faculdade de Engenharia, Universidade do Porto, Porto, Portugal

    João Manuel R. S. Tavares

  3. Department of Electronics and Computer Engineering, Tribhuvan University, Lalitpur, Nepal

    Subarna Shakya

  4. College of Engineering, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia

    Abdullah M. Iliyasu

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© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG

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Wadud, A., Showrov, M.I.H. (2021). EMG Signal Classification with Effective Features for Diagnosis. In: Chen, J.IZ., Tavares, J.M.R.S., Shakya, S., Iliyasu, A.M. (eds) Image Processing and Capsule Networks. ICIPCN 2020. Advances in Intelligent Systems and Computing, vol 1200. Springer, Cham. https://doi.org/10.1007/978-3-030-51859-2_57

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