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Electromyographic Analysis of Paraspinal Muscles of Scoliosis Patients Using Machine Learning Approaches

Author

Listed:
  • Ruixin Liang

    (Laboratory for Artificial Intelligence in Design, Hong Kong Science Park, New Territories, Hong Kong 999077, China)

  • Joanne Yip

    (Laboratory for Artificial Intelligence in Design, Hong Kong Science Park, New Territories, Hong Kong 999077, China
    Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong 999077, China)

  • Yunli Fan

    (Department of Orthopaedics & Traumatology, LKS Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong 999077, China
    Department of Orthopedics, The University of Hong Kong—Shenzhen Hospital, Futian District, Shenzhen 518000, China
    Physiotherapy Department, The University of Hong Kong—Shenzhen Hospital, Futian District, Shenzhen 518000, China)

  • Jason P. Y. Cheung

    (Department of Orthopaedics & Traumatology, LKS Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong 999077, China
    Department of Orthopedics, The University of Hong Kong—Shenzhen Hospital, Futian District, Shenzhen 518000, China)

  • Kai-Tsun Michael To

    (Department of Orthopaedics & Traumatology, LKS Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong 999077, China
    Department of Orthopedics, The University of Hong Kong—Shenzhen Hospital, Futian District, Shenzhen 518000, China)

Abstract

A large number of studies have used electromyography (EMG) to measure the paraspinal muscle activity of adolescents with idiopathic scoliosis. However, investigations on the features of these muscles are very limited even though the information is useful for evaluating the effectiveness of various types of interventions, such as scoliosis-specific exercises. The aim of this cross-sectional study is to investigate the characteristics of participants with imbalanced muscle activity and the relationships among 13 features (physical features and EMG signal value). A total of 106 participants (69% with scoliosis; 78% female; 9–30 years old) are involved in this study. Their basic profile information is obtained, and the surface EMG signals of the upper trapezius, latissimus dorsi, and erector spinae (thoracic and erector spinae) lumbar muscles are tested in the static (sitting) and dynamic (prone extension position) conditions. Then, two machine learning approaches and an importance analysis are used. About 30% of the participants in this study find that balancing their paraspinal muscle activity during sitting is challenging. The most interesting finding is that the dynamic asymmetry of the erector spinae (lumbar) group of muscles is an important (third in importance) predictor of scoliosis aside from the angle of trunk rotation and height of the subject.

Suggested Citation

  • Ruixin Liang & Joanne Yip & Yunli Fan & Jason P. Y. Cheung & Kai-Tsun Michael To, 2022. "Electromyographic Analysis of Paraspinal Muscles of Scoliosis Patients Using Machine Learning Approaches," IJERPH, MDPI, vol. 19(3), pages 1-12, January.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:3:p:1177-:d:730057
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    References listed on IDEAS

    as
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