Classification of surface EMG signal with fractal dimension
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...
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| Published in | Journal of Zhejiang University. B. Science Vol. 6; no. 8; pp. 844 - 848 |
|---|---|
| Main Author | |
| Format | Journal Article |
| Language | English |
| Published |
China
Department of Biomedical Engineering, Shanghai Jiaotong University, Shanghai 200030, China
01.08.2005
Zhejiang University Press |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1673-1581 1009-3095 1862-1783 |
| DOI | 10.1631/jzus.2005.B0844 |
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| 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. |
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| AbstractList | R318.04; 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. 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. 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. 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.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. |
| Author | 胡晓 王志中 任小梅 |
| AuthorAffiliation | Department of Biomedical Engineering, Shanghai Jiaotong University, Shanghai 200030, China |
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| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/16052721$$D View this record in MEDLINE/PubMed |
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| Keywords | GP algorithm Fractal dimension Correlation dimension Surface EMG signal Self-similarity |
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| References | 8818137 - Med Eng Phys. 1996 Jul;18(5):390-5 12636979 - Artif Intell Med. 2003 Feb;27(2):201-22 8892237 - Med Eng Phys. 1996 Oct;18(7):529-37 9291030 - Int J Med Inform. 1997 Jul;45(3):185-92 10969201 - J Electromyogr Kinesiol. 2000 Aug;10(4):275-81 8468080 - IEEE Trans Biomed Eng. 1993 Jan;40(1):82-94 11876246 - Physiol Meas. 2002 Feb;23(1):R1-38 15913836 - Comput Methods Programs Biomed. 2005 Sep;79(3):189-95 10624739 - Med Eng Phys. 1999 Jul-Sep;21(6-7):431-8 |
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| Snippet | Surface EMG （electromyography） signal is a complex nonlinear signal with low signal to noise ratio （SNR）. This paper is aimed at identifying different... Surface EMG (electromyography) signal is a complex nonlinear signal with low signal to noise ratio (SNR). This paper is aimed at identifying different patterns... R318.04; Surface EMG (electromyography) signal is a complex nonlinear signal with low signal to noise ratio (SNR). This paper is aimed at identifying different... |
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| SubjectTerms | Algorithms Diagnosis, Computer-Assisted - methods Electromyography - methods Fractals Humans Muscle Contraction - physiology Pattern Recognition, Automated - methods Plant & Animal Sciences and Biotechnology Signal Processing, Computer-Assisted 信噪比 图象参数 肌电图学 表面信号 高频噪声 |
| Title | Classification of surface EMG signal with fractal dimension |
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