Research article Special Issues

Examining and monitoring paretic muscle changes during stroke rehabilitation using surface electromyography: A pilot study

  • Received: 03 March 2019 Accepted: 18 August 2019 Published: 30 September 2019
  • Complex neuromuscular changes have been reported to occur in paretic muscles following stroke, but whether and how they can recover under rehabilitation therapy remain unclear. A tracking analysis protocol needs to be designed involving multiple sessions of surface electromyography (sEMG) examinations during the rehabilitation procedure. Following such a protocol, this pilot study is aimed to monitor paretic muscle changes using three sEMG indicators namely clustering index (CI), root mean square (RMS) and medium frequency (MDF). Initially, a single sEMG examination was performed on the abductor pollicis brevis (APB) muscle on both sides of 23 subjects with stroke and one side of 18 healthy control subjects. With these data to establish CI diagnostic criterion, the paretic muscles of all subjects with stroke showed a very board CI distribution pattern from abnormally low values through normality to abnormally high values. Afterwards, 9 out of 23 subjects with stroke had their paretic muscles examined at least twice before and after the treatment. Almost all paretic muscles had an increase of the RMS, a change of the MDF approaching to the value of the contralateral muscle, and a change of the CI returning to its normal range after common rehabilitation treatments. Finally, 4 of the 9 subjects with stroke participated into repeated examinations of their paretic muscles. The combined use of three indicators helped to reveal specific neuromuscular processes contributing to recovery of paretic muscles, due to their complementary diagnostic powers. Furthermore, neuromuscular processes were found to vary across subjects in type, order and timing during rehabilitation. In conclusion, given the 4 cases following the tracking analysis protocol, this pilot study preliminarily demonstrates usability of three sEMG indicators as tools for examining and monitoring stroke rehabilitation procedure in terms of improvements of paretic muscle changes. All the revealed complex neuromuscular processes imply the necessity of applying sEMG examinations in monitoring rehabilitation procedure, with the potential of offering important guidelines for designing better and individualized protocols toward improved stroke rehabilitation.

    Citation: Ge Zhu, Xu Zhang, Xiao Tang, Xiang Chen, Xiaoping Gao. Examining and monitoring paretic muscle changes during stroke rehabilitation using surface electromyography: A pilot study[J]. Mathematical Biosciences and Engineering, 2020, 17(1): 216-234. doi: 10.3934/mbe.2020012

    Related Papers:

  • Complex neuromuscular changes have been reported to occur in paretic muscles following stroke, but whether and how they can recover under rehabilitation therapy remain unclear. A tracking analysis protocol needs to be designed involving multiple sessions of surface electromyography (sEMG) examinations during the rehabilitation procedure. Following such a protocol, this pilot study is aimed to monitor paretic muscle changes using three sEMG indicators namely clustering index (CI), root mean square (RMS) and medium frequency (MDF). Initially, a single sEMG examination was performed on the abductor pollicis brevis (APB) muscle on both sides of 23 subjects with stroke and one side of 18 healthy control subjects. With these data to establish CI diagnostic criterion, the paretic muscles of all subjects with stroke showed a very board CI distribution pattern from abnormally low values through normality to abnormally high values. Afterwards, 9 out of 23 subjects with stroke had their paretic muscles examined at least twice before and after the treatment. Almost all paretic muscles had an increase of the RMS, a change of the MDF approaching to the value of the contralateral muscle, and a change of the CI returning to its normal range after common rehabilitation treatments. Finally, 4 of the 9 subjects with stroke participated into repeated examinations of their paretic muscles. The combined use of three indicators helped to reveal specific neuromuscular processes contributing to recovery of paretic muscles, due to their complementary diagnostic powers. Furthermore, neuromuscular processes were found to vary across subjects in type, order and timing during rehabilitation. In conclusion, given the 4 cases following the tracking analysis protocol, this pilot study preliminarily demonstrates usability of three sEMG indicators as tools for examining and monitoring stroke rehabilitation procedure in terms of improvements of paretic muscle changes. All the revealed complex neuromuscular processes imply the necessity of applying sEMG examinations in monitoring rehabilitation procedure, with the potential of offering important guidelines for designing better and individualized protocols toward improved stroke rehabilitation.


    加载中


    [1] S. Mendis, Stroke disability and rehabilitation of stroke: World Health Organization perspective,Int. J. Stroke, 8 (2013), 3-4.
    [2] D. T. Wade, Measurement in neurological rehabilitation, Curr. Opin. Neurol., 5 (1992), 682-686.
    [3] P. Langhorne, F. Coupar and A. Pollock, Motor recovery after stroke: A systematic review, Lancet Neurol., 8 (2009), 741-754.
    [4] G. Kwakkel, B. Kollen and E. Linderman, Understanding the pattern of functional recovery after stroke: facts and theories, Restor. Neurol. Neurosci., 22 (2004), 281-299.
    [5] R. H. Nijland, E. Wegen, H. Wel, et al., Presence of finger extension and shoulder abduction within 72 hours after stroke predicts functional recovery. Early prediction of functional outcome after stroke: the EPOS cohort study, Stroke, 41 (2010), 745-750.
    [6] R. H. Nijland, E. Wegen, J. Verbunt, et al., A comparison of two validated tests for upper limb function after stroke: the Wolf Motor Function Test and the Action Research Arm Test, J. Rehabil. Med., 42 (2010), 694-696.
    [7] L. Brewer, F. Horgan, A. Hickey, et al., Stroke rehabilitation: Recent advances and future therapies, QJM, 106, (2013), 11-25.
    [8] R. Dattola, P. Girlanda, G. Vita, et al., Muscle rearrangement in patients with hemiparesis after stroke: an electrophysiological and morphological study, Eur. Neurol., 33 (1993), 109-114.
    [9] R. P. Segura and V. Sahgal, Hemiplegic atrophy: Electrophysiological and morphological studies, Muscle Nerve, 4 (1981), 246-248.
    [10] C. W. Chang, Evident trans-synaptic degeneration of motor neurons after stroke: A study of neuromuscular jitter by axonal microstimulation, Electroencephalogr. Clin. Neurophysiol, 109 (1998), 199-202.
    [11] M. Lukács, L. Vécsei and S. Beniczky, Changes in muscle fiber density following a stroke, Clin. Neurophysiol., 120 (2009), 1539-1542.
    [12] C. Bérard, C. Payan, J. Fermanian, et al., A motor function measure scale for neuromuscular diseases. Construction and validation study, Neuromuscular Disorders, 15 (2005), 463-470.
    [13] S. Brunnstrom and D. Carson, Movement therapy in hemiplegia: A neurophysiological approach, Gerontologist, 12 (1970).
    [14] A. R. Fugl-Meyer, L. Jaasko, I. Leyman, et al., The poststroke hemiplegic patient. 1. A method for evaluation of physical performance, Scand. J. Rehabil. Med., 7 (1975), 13-31.
    [15] F. I. Mahoney, Functional evaluation (the Barthel Index), Md. State Med. J., 14 (1965), 61-65.
    [16] T. Brott, H. P. Adams, C. P. Olinger, et al., Measurements of acute cerebral infarction: A clinical examination scale, Stroke, 20 (1989), 864-870. doi: 10.1161/01.STR.20.7.864
    [17] T. Brott, J. R. Marler, C. P. Olinger, et al., Measurements of acute cerebral infarction: Lesion size by computed tomography, Stroke, 20 (1989), 871-875.
    [18] P. Lyden, T. Brott, B. Tilley, et al., Improved reliability of the NIH Stroke Scale using video training. NINDS TPA Stroke Study Group, Stroke,25 (1994), 2220-2226.
    [19] A. Fugl-Meyer, L. Jaasko, S. Olsson, et al., The post-stroke hemiplegic patient-Part1: A method for evaluation of physical performance, Scand. J. Rehabil. Med., 7 (1975), 13-31.
    [20] D. Gladstone, C. Danells and S. Black, The Fugl-Meyer assessment of motor recovery after stroke: A critical review of its measurement properties, Neurorehabil. Neural Repair, 16 (2002), 232-240.
    [21] T. Adel and D. Stashuk, Clinical Quantitative Electromyography, in Electrodiagnosis in New Frontiers of Clinical Research,IntechOpen, (2013).
    [22] M. Lukács, Electrophysiological signs of changes in motor units after ischaemic stroke, Clin. Neurophysiol., 116 (2005), 1566-1570.
    [23] T. Artuğ, O. Osman, İ. Göker, et al., Classification of neuromuscular diseases in neuromuscular junction and tendon recordings with needle EMG by using Welch's method, Med. Technol. Nat. Congress IEEE, (2017), 1-4.
    [24] C. S. Bickel, C. M. Gregory and J. C. Dean, Motor unit recruitment during neuromuscular electrical stimulation: a critical appraisal, Eur. J. Appl. Physiol., 111 (2011), 2399.
    [25] C. A. Knight and G. Kamen, Superficial motor units are larger than deeper motor units in human vastus lateralis muscle, Muscle Nerve, 31(2005), 475-480.
    [26] P. Sbriccoli, F. Felici, A. Rosponi, et al., Exercise induced muscle damage and recovery assessed by means of linear and non-linear sEMG analysis and ultrasonography, J. Electromyogr. Kinesiol., 11 (2001), 73-83.
    [27] A. Holtermann, C. Grönlund, J. S. Karlsson, et al., Motor unit synchronization during fatigue: described with a novel sEMG method based on large motor unit samples, J. Electromyogr. Kinesiol., 19 (2009), 232-241.
    [28] F. Palermo, M. Cognolato, A. Gijsberts, et al., Repeatability of grasp recognition for robotic hand prosthesis control based on sEMG data, IEEE Int. Conf. Rehabil. Robot, (2017), 1154.
    [29] J. Y. Hogrel, Clinical applications of surface electromyography in neuromuscular disorders, Neurophysiol. Clin., 35 (2005), 59-71.
    [30] C. J. Luca, Physiology and mathematics of myoelectric signals, IEEE Trans. Biomed. Eng., 26 (1979), 313-325.
    [31] X. Li, A. Suresh, P. Zhou, et al., Alterations in the peak amplitude distribution of the surface electromyogram poststroke, IEEE Trans. Biomed. Eng., 60 (2013), 845-852. doi: 10.1109/TBME.2012.2205249
    [32] B. Yao, X. Zhang, S. Li, et al., Analysis of linear electrode array EMG for assessment of hemiparetic biceps brachii muscles, Front. Hum. Neurosci., 9 (2015), 569.
    [33] S. Karlsson and B. Gerdle, Mean frequency and signal amplitude of the surface EMG of the quadriceps muscles increase with increasing torque-a study using the continuous wavelet transform, J. Electromyogr. Kinesiol.,11 (2001), 131-140.
    [34] X. Li, H. Shin, P. Zhou, et al., Power spectral analysis of surface electromyography (EMG) at matched contraction levels of the first dorsal interosseous muscle in stroke survivors, Clin. Neurophysiol., 125 (2014), 988-994.
    [35] N. S. Arikidis, E. W. Abel and A. Forster, Interscale wavelet maximum-a fine to coarse algorithm for wavelet analysis of the EMG interference pattern, IEEE Trans. Biomed. Eng., 49 (2002), 337-344.
    [36] A. I. Meigal, S. Rissanen, M. P. Tarvainen, et al., Novel parameters of surface EMG in patients with Parkinson's disease and healthy young and old controls, J. Electromyogr. Kinesiol., 19 (2009), 206-213.
    [37] W. Chen, Z. Wang, H. Xie, et al., Characterization of surface EMG signal based on fuzzy entropy, IEEE Trans. Neural Syst. Rehabil. Eng., 15(2007), 266-272.
    [38] P. A. Kaplanis, C. S. Pattichis and D. Zazula, Multiscale entropy-based approach to automated surface EMG classification of neuromuscular disorders, Med. Biol. Eng. Comput., 48 (2010), 773-781.
    [39] X. Zhang, P. E. Barkhaus, W. Z. Rymer, et al., Machine learning for supporting diagnosis of amyotrophic lateral sclerosis using surface electromyogram, IEEE Trans. Neural Syst. Rehabil. Eng., 22 (2014), 96-103. doi: 10.1109/TNSRE.2013.2274658
    [40] M. Higashihara, M. Sonoo, T. Yamamoto, et al., Evaluation of spinal and bulbar muscular atrophy by the clustering index method, Muscle Nerve, 44 (2011), 539-546.
    [41] X. Zhang, Z. Wei, X. Ren, et al., Complex Neuromuscular changes post-stroke revealed by clustering index analysis of surface electromyogram, IEEE Trans. Neural Syst. Rehabil. Eng., 25 (2017), 2105-2112. doi: 10.1109/TNSRE.2017.2707582
    [42] J. M. Gregson, M. Leathley, A. P. Moore, et al., Reliability of the tone assessment scale and the modified ashworth scale as clinical tools for assessing poststroke spasticity, Arch. Phys. Med. Rehabil., 80 (1999), 1013-1016.
    [43] C. Huang, X. Chen, S. Cao, et al., An isometric muscle force estimation framework based on a high-density surface EMG array and an NMF algorithm, J. Neural Eng., 14 (2017), 046005. doi: 10.1088/1741-2552/aa63ba
    [44] W. Tang, X. Zhang, X. Tang, et al., surface electromyographic examination of Poststroke neuromuscular changes in Proximal and Distal Muscles Using clustering index analysis, Front. Neurol., 8 (2018), 731.
    [45] X. Tang, X. Zhang, X. Gao, et al., A novel interpretation of sample entropy in surface electromyographic examination of complex neuromuscular alternations in subacute and chronic stroke, IEEE Trans. Neural Syst. Rehabil. Eng., 26 (2018), 1878-1888. doi: 10.1109/TNSRE.2018.2864317
    [46] P. W. Hodges and B. H. Bui, A comparison of computer-based methods for the determination of onset of muscle contraction using electromyography, Electroencephalogr. Clin. Neurophysiol., 101 (1996), 511-519.
    [47] H. Uesugi, M. Sonoo, E. Stålberg, et al., "Clustering Index method": A new technique for differentiation between neurogenic and myopathic changes using surface EMG, Clin. Neurophysiol., 122 (2011), 1032-1041.
    [48] X. Zhang and P. Zhou, Clustering index analysis of the surface electromyogram poststroke, International IEEE/EMBS Conference on Neural Engineering, (2013), 1586-1589.
    [49] J. H. Lawrence and C. J. Luca, Myoelectric signal versus force relationship in different human muscles, J. Appl. Physiol. Respir. Environ. Exerc. Physiol., 54 (1983), 1653-1659.
    [50] X. L. Hu, K. Y. Tong and L. K. Hung, Firing properties of motor units during fatigue in subjects after stroke, J. Electromyogr. Kinesiol.,16 (2006), 469-476.
    [51] M. S. Fimland, P. M. Moen, T. Hill et al., Neuromuscular performance of paretic versus non-paretic plantar flexors after stroke, Eur. J. Appl. Physiol., 111 (2011), 3041-3049.
    [52] A. C. Martinez, F. Campo, M. R. Mingo, et al., Altered motor unit architecture in hemiparetic patients. A single fibre EMG study, J. Neurol. Neurosurg. Psychiatry, 45 (1982), 756.
    [53] S. Chokroverty, M. G. Reyes, F. A. Rubino, et al., Hemiplegic amyotrophy: Muscle and motor point biopsy study, Arch. Neurol., 33 (1976), 104-110.
    [54] Y. Hara, Y. Masakado and N. Chino, The physiological functional loss of single thenar motor units in the stroke patients: when does it occur? Does it progress?,Clin. Neurophysiol., 115 (2004), 97-103.
    [55] J. J. Gemperline, S. Allen, D. Walk, et al., Characteristics of motor unit discharge in subjects with hemiparesis, Muscle Nerve, 18 (1995), 1101-1114.
    [56] X. Li, A. Holobar, M. Gazzoni, et al., Examination of poststroke alteration in motor unit firing behavior using high-density surface EMG decomposition, IEEE Trans. Biomed. Eng, 62 (2015), 1242-1252.
    [57] A. K. Datta, S. F. Farmer and J. A. Stephens, Central nervous pathways underlying synchronization of human motor unit firing studied during voluntary contractions, J. Physiol., 432 (1991), 401-425.
    [58] I. Hausmanowa-Petrusewicz and J. Kopec, EMG parameters changes in the effort pattern at various loads in diseased muscle, Electromyogr. Clin. Neurophysiol., 23 (1983), 213-228.
    [59] P. Zhou, N. L. Suresh and W. Z. Rymer, Model based sensitivity analysis of EMG-force relation with respect to motor unit properties: applications to muscle paresis in stroke, Ann. Biomed. Eng., 35 (2007), 1521-1531.
    [60] M. Lukacs, L. Vécsei and S. Beniczky, Large motor units are selectively affected following a stroke, Clin. Neurophysiol., 119 (2008), 2555-2558.
    [61] E. Stalberg, Electrogenesis in human dystrophic muscle, In: L. P. Rowland,Pathogenesis of human muscular dystrophies, (1977), 570-587.
    [62] R. Dengler, R. B. Stein and C. K. Thomas, Axonal conduction velocity and force of single human motor units, Muscle Nerve, 11 (1988), 136-145.
    [63] J. Xi, A. Li and M, Wang, A novel unsupervised learning model for detecting driver genes from pan-cancer data through matrix tri-factorization framework with pairwise similarities constraints, Neurocomputing, 296 (2018), 64-73.
    [64] Q. Huang, X. Huang, Z. Kong, et al., Bi-Phase evolutionary searching for biclusters in gene expression data,IEEE Trans. Evol. Comput.,In press, (2018).
    [65] Q. Huang, D. Tao, X. Li, et al., Parallelized evolutionary learning for detection of biclusters in gene expression data, IEEE/ACM Trans. Comput. Biol. Bioinform.,9 (2012), 560-570.
    [66] G. Singh and L. Samavedham, Unsupervised learning based feature extraction for differential diagnosis of neurodegenerative diseases: A case study on early-stage diagnosis of Parkinson disease,J. Neurosci. Methods, 256 (2015), 30-40.
  • Reader Comments
  • © 2020 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(4895) PDF downloads(756) Cited by(10)

Article outline

Figures and Tables

Figures(3)  /  Tables(2)

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog