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

• Published in: Shanghai jiao tong da xue xue bao Vol. 23; no. Suppl 1; pp. 56 - 61
• Main Authors: Yu, Liang, Yan, Li, Chen, Mengjie, Dong, Liangchao
• Format: Journal Article
• Language: English
• Published: Shanghai Shanghai Jiaotong University Press 01.12.2018; Springer Nature B.V
• Subjects: Architecture; Cerebral palsy; Children; Computer Science; Correlation analysis; Data processing; Density; Electrical Engineering; Electromyography; Engineering; Gait; Life Sciences; Materials Science; Movement disorders; Paralysis; Signal analysis; Spectra; Spectral correlation; A; gait cycle; spectral correlation density; cyclostationary modeling; electromyography (EMG) signal; R 318; cerebral palsy (CP) diagnosis
• ISSN: 1007-1172; 1674-8115; 1995-8188
• DOI: 10.1007/s12204-018-2023-9

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.