Cyclostationary Modeling of Surface Electromyography Signal During Gait Cycles and Its Application for Cerebral Palsy Diagnosis

  • Liang Yu (余亮)Email author
  • Li Yan (严莉)
  • Mengjie Chen (陈梦婕)
  • Liangchao Dong (董良超)


Cerebral palsy (CP) is a group of permanent movement disorders that appear in early childhood. The electromyography (EMG) signal analysis and the gait analysis are two most commonly used methods in the clinic. In this paper, a cyclostationary model of the EMG signal is proposed. The model can combine the aforementioned two methods. The EMG signal acquired during the gait cycles is assumed to be cyclostationary due to the physiological characteristics of the EMG signal production. Then, the spectral correlation density is used to analyze the cyclic frequency (corresponding to the gait cycles) and spectral frequency (the frequency of EMG signal) in a waterfall representation of the two kinds of frequencies. The experiments show that the asymptomatic (normal) subjects and symptomatic subjects (with CP) can be distinguished from the spectral correlation density in a range of cyclic frequencies.

Key words

electromyography (EMG) signal gait cycle spectral correlation density cyclostationary modeling cerebral palsy (CP) diagnosis 



Physical frequency, Hz


Mean of firing rate, Hz


Number of fibers in a given motor unit


Number of motor unit in a given muscle


Distance between endplate and observation point, mm


Area of an element of the cross-sectional disk, mm2


Time, s


Arrival time of each gait cycle, s


Velocity of signal conduction, mm/s


Axial position of the observation point P(x0, y0), mm


Radical position of the observation point P(x0, y0), mm


Gait cycle frequency, Hz


Intracellular conductivity, mm/s


Tissue conductivity, mm/s


Axial conductivity, mm/s


Radical conductivity, mm/s


Each single fiber action potential contained in a given motor unit

CLC number

R 318 

Document code


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Copyright information

© Shanghai Jiaotong University and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Liang Yu (余亮)
    • 1
    • 2
    Email author
  • Li Yan (严莉)
    • 1
    • 2
  • Mengjie Chen (陈梦婕)
    • 3
  • Liangchao Dong (董良超)
    • 3
  1. 1.Institute of Vibration, Shock and NoiseShanghai Jiao Tong UniversityShanghaiChina
  2. 2.State Key Laboratory of Mechanical System and VibrationShanghai Jiao Tong UniversityShanghaiChina
  3. 3.Department of Pediatric OrthopedicsChildren’s Hospital of Shanghai Jiao Tong UniversityShanghaiChina

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