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An Ensemble of Long Short-Term Memory Networks with an Attention Mechanism for Upper Limb Electromyography Signal Classification

Author

Listed:
  • Naif D. Alotaibi

    (Communication Systems and Networks Research Group, Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia)

  • Hadi Jahanshahi

    (Institute of Electrical and Electronics Engineers, Toronto, ON M5V 3T9, Canada)

  • Qijia Yao

    (School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China)

  • Jun Mou

    (School of Information Science and Engineering, Dalian Polytechnic University, Dalian 116034, China)

  • Stelios Bekiros

    (FEMA, University of Malta, MSD 2080 Msida, Malta
    LSE Health, Department of Health Policy, London School of Economics and Political Science, London WC2A 2AE, UK
    IPAG Business School, 184 Boulevard Saint-Germain, 75006 Paris, France)

Abstract

Advancing cutting-edge techniques to accurately classify electromyography (EMG) signals are of paramount importance given their extensive implications and uses. While recent studies in the literature present promising findings, a significant potential still exists for substantial enhancement. Motivated by this need, our current paper introduces a novel ensemble neural network approach for time series classification, specifically focusing on the classification of upper limb EMG signals. Our proposed technique integrates long short-term memory networks (LSTM) and attention mechanisms, leveraging their capabilities to achieve accurate classification. We provide a thorough explanation of the architecture and methodology, considering the unique characteristics and challenges posed by EMG signals. Furthermore, we outline the preprocessing steps employed to transform raw EMG signals into a suitable format for classification. To evaluate the effectiveness of our proposed technique, we compare its performance with a baseline LSTM classifier. The obtained numerical results demonstrate the superiority of our method. Remarkably, the method we propose attains an average accuracy of 91.5%, with all motion classifications surpassing the 90% threshold.

Suggested Citation

  • Naif D. Alotaibi & Hadi Jahanshahi & Qijia Yao & Jun Mou & Stelios Bekiros, 2023. "An Ensemble of Long Short-Term Memory Networks with an Attention Mechanism for Upper Limb Electromyography Signal Classification," Mathematics, MDPI, vol. 11(18), pages 1-21, September.
  • Handle: RePEc:gam:jmathe:v:11:y:2023:i:18:p:4004-:d:1244329
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    Cited by:

    1. Alharbi, Njud S. & Bekiros, Stelios & Jahanshahi, Hadi & Mou, Jun & Yao, Qijia, 2024. "Spatiotemporal wavelet-domain neuroimaging of chaotic EEG seizure signals in epilepsy diagnosis and prognosis with the use of graph convolutional LSTM networks," Chaos, Solitons & Fractals, Elsevier, vol. 181(C).

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