Xiaoyan Li1, Henry Shin2, Ping Zhou3, Xun Niu4, Jie Liu4, William Zev Rymer5. 1. Sensory Motor Performance Program, Rehabilitation Institute of Chicago, Chicago, IL, USA. Electronic address: xiaoyan-li-1@northwestern.edu. 2. Department of Biomedical Engineering, Northwestern University, Chicago, IL, USA. 3. Sensory Motor Performance Program, Rehabilitation Institute of Chicago, Chicago, IL, USA; Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL, USA; Institute of Biomedical Engineering, University of Science and Technology of China, Hefei, China. 4. Sensory Motor Performance Program, Rehabilitation Institute of Chicago, Chicago, IL, USA. 5. Sensory Motor Performance Program, Rehabilitation Institute of Chicago, Chicago, IL, USA; Department of Biomedical Engineering, Northwestern University, Chicago, IL, USA; Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL, USA.
Abstract
OBJECTIVE: The objective of this study was to help assess complex neural and muscular changes induced by stroke using power spectral analysis of surface electromyogram (EMG) signals. METHODS: Fourteen stroke subjects participated in the study. They were instructed to perform isometric voluntary contractions by abducting the index finger. Surface EMG signals were collected from the paretic and contralateral first dorsal interosseous (FDI) muscles with forces ranging from 30% to 70% maximum voluntary contraction (MVC) of the paretic muscle. Power spectral analysis was performed to characterize features of the surface EMG in paretic and contralateral muscles at matched forces. A Linear Mixed Model was applied to identify the spectral changes in the hemiparetic muscle and to examine the relation between spectral parameters and contraction levels. Regression analysis was performed to examine the correlations between spectral characteristics and clinical features. RESULTS: Differences in power spectrum distribution patterns were observed in paretic muscles when compared with their contralateral pairs. Nine subjects showed increased mean power frequency (MPF) in the contralateral side (>15 Hz). No evident spectrum difference was observed in 3 subjects. Only 2 subjects had higher MPF in the paretic muscle than the contralateral muscle. Pooling all subjects' data, there was a significant reduction of MPF in the paretic muscle compared with the contralateral muscle (paretic: 168.7 ± 7.6 Hz, contralateral: 186.1 ± 8.7 Hz, mean ± standard error, F=36.56, p<0.001). Examination of force factor on the surface EMG power spectrum did not confirm a significant correlation between the MPF and contraction force in either hand (F=0.7, p>0.5). There was no correlation between spectrum difference and Fugl-Meyer or Chedoke scores, or ratio of paretic and contralateral MVC (p>0.2). CONCLUSIONS: There appears to be complex muscular and neural processes at work post stroke that may impact the surface EMG power spectrum. The majority of the tested stroke subjects had lower MPF in the paretic muscle than in the contralateral muscle at matched isometric contraction force. The reduced MPF of paretic muscles can be attributed to different factors such as increased motor unit synchronization, impairments in motor unit control properties, loss of large motor units, and atrophy of muscle fibers. SIGNIFICANCE: Surface EMG power spectral analysis can serve as a useful tool to indicate complex neural and muscular changes after stroke.
OBJECTIVE: The objective of this study was to help assess complex neural and muscular changes induced by stroke using power spectral analysis of surface electromyogram (EMG) signals. METHODS: Fourteen stroke subjects participated in the study. They were instructed to perform isometric voluntary contractions by abducting the index finger. Surface EMG signals were collected from the paretic and contralateral first dorsal interosseous (FDI) muscles with forces ranging from 30% to 70% maximum voluntary contraction (MVC) of the paretic muscle. Power spectral analysis was performed to characterize features of the surface EMG in paretic and contralateral muscles at matched forces. A Linear Mixed Model was applied to identify the spectral changes in the hemiparetic muscle and to examine the relation between spectral parameters and contraction levels. Regression analysis was performed to examine the correlations between spectral characteristics and clinical features. RESULTS: Differences in power spectrum distribution patterns were observed in paretic muscles when compared with their contralateral pairs. Nine subjects showed increased mean power frequency (MPF) in the contralateral side (>15 Hz). No evident spectrum difference was observed in 3 subjects. Only 2 subjects had higher MPF in the paretic muscle than the contralateral muscle. Pooling all subjects' data, there was a significant reduction of MPF in the paretic muscle compared with the contralateral muscle (paretic: 168.7 ± 7.6 Hz, contralateral: 186.1 ± 8.7 Hz, mean ± standard error, F=36.56, p<0.001). Examination of force factor on the surface EMG power spectrum did not confirm a significant correlation between the MPF and contraction force in either hand (F=0.7, p>0.5). There was no correlation between spectrum difference and Fugl-Meyer or Chedoke scores, or ratio of paretic and contralateral MVC (p>0.2). CONCLUSIONS: There appears to be complex muscular and neural processes at work post stroke that may impact the surface EMG power spectrum. The majority of the tested stroke subjects had lower MPF in the paretic muscle than in the contralateral muscle at matched isometric contraction force. The reduced MPF of paretic muscles can be attributed to different factors such as increased motor unit synchronization, impairments in motor unit control properties, loss of large motor units, and atrophy of muscle fibers. SIGNIFICANCE: Surface EMG power spectral analysis can serve as a useful tool to indicate complex neural and muscular changes after stroke.
Authors: Laura Miller McPherson; Francesco Negro; Chris K Thompson; Laura Sanchez; C J Heckman; Jules Dewald; Dario Farina Journal: Conf Proc IEEE Eng Med Biol Soc Date: 2016-08
Authors: Xiaoyan Li; Ales Holobar; Marco Gazzoni; Roberto Merletti; William Zev Rymer; Ping Zhou Journal: IEEE Trans Biomed Eng Date: 2014-11-07 Impact factor: 4.538
Authors: Manuela Corti; Barbara K Smith; Darin J Falk; Lee Ann Lawson; David D Fuller; S H Subramony; Barry J Byrne; Evangelos A Christou Journal: Muscle Nerve Date: 2015-04-24 Impact factor: 3.217