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Characterization of Stroke- and Aging-Related Changes in the Complexity of EMG Signals During Tracking Tasks

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Abstract

To explore the stroke- and aging-induced neurological changes in paretic muscles from an entropy point of view, fuzzy approximate entropy (fApEn) was utilized to represent the complexity of EMG signals in elbow-tracking tasks. In the experiment, 11 patients after stroke and 20 healthy control subjects (10 young and 10 age-matched adults) were recruited and asked to perform elbow sinusoidal trajectory tracking tasks. During the tests, the elbow angle and electromyographic (EMG) signals of the biceps brachii and triceps brachii were recorded simultaneously. The results showed significant differences in fApEn values of both biceps and triceps EMG among four groups at six velocities (p < 0.01), with fApEn values in the following order: affected sides of stroke patients < unaffected sides of stroke patients < age-matched controls < young controls. A possible mechanism underlying the smaller fApEn values in the affected sides in comparison with aged-matched controls and in the aged individuals in comparison with young controls might be the reduction in the number and firing rate of active motor units. This method and index provide evidence of neurological changes after stroke and aging by complexity analysis of the surface EMG signals. Further studies are needed to validate and facilitate the application in clinic.

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Acknowledgments

The project was supported by the National Natural Science Foundation of China (Grant No. 61273359) and the Guangdong Natural Science Foundation (Grant No. S2012010010350).

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Authors and Affiliations

  1. School of Engineering, Sun Yat-sen University, Guangzhou, Guangdong, People’s Republic of China

    Di Ao, Rui Sun & Rong Song

  2. Interdisciplinary Division of Biomedical Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong

    Kai-yu Tong

  3. Key Laboratory of Sensing Technology and Biomedical Instrument of Guangdong Province, Guangzhou, Guangdong, People’s Republic of China

    Di Ao, Rui Sun & Rong Song

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  1. Di Ao
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Correspondence to Rong Song.

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Associate Editor Xiaoxiang Zheng oversaw the review of this article.

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Ao, D., Sun, R., Tong, Ky. et al. Characterization of Stroke- and Aging-Related Changes in the Complexity of EMG Signals During Tracking Tasks. Ann Biomed Eng 43, 990–1002 (2015). https://doi.org/10.1007/s10439-014-1150-1

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