Classification of surface EMG signal with fractal dimension.
- Author:
Xiao HU
1
;
Zhi-zhong WANG
;
Xiao-mei REN
Author Information
1. Department of Biomedical Engineering, Shanghai Jiaotong University, Shanghai 200030, China. huxiaocz@sjtu.edu.cn
- Publication Type:Clinical Trial
- MeSH:
Algorithms;
Diagnosis, Computer-Assisted;
methods;
Electromyography;
methods;
Fractals;
Humans;
Muscle Contraction;
physiology;
Pattern Recognition, Automated;
methods;
Signal Processing, Computer-Assisted
- From:
Journal of Zhejiang University. Science. B
2005;6(8):844-848
- CountryChina
- Language:English
-
Abstract:
Surface EMG (electromyography) signal is a complex nonlinear signal with low signal to noise ratio (SNR). This paper is aimed at identifying different patterns of surface EMG signals according to fractal dimension. Two patterns of surface EMG signals are respectively acquired from the right forearm flexor of 30 healthy volunteers during right forearm supination (FS) or forearm pronation (FP). After the high frequency noise is filtered from surface EMG signal by a low-pass filter, fractal dimension is calculated from the filtered surface EMG signal. The results showed that the fractal dimensions of filtered FS surface EMG signals and those of filtered FP surface EMG signals distribute in two different regions, so the fractal dimensions can represent different patterns of surface EMG signals.
