Relationship of EMG/SMG features and muscle strength level: an exploratory study on tibialis anterior muscles during plantar-flexion among hemiplegia patients

BioMedical Engineering OnLine

Huihui Li¹  Guoru Zhao¹  Yongjin Zhou¹  Xin Chen²  Zhen Ji²  Lei Wang¹ 

[1] Shenzhen Key Lab for Low-cost Healthcare, Shenzhen, China

[2] Shenzhen University, Shenzhen, China

关键词: Muscle strength level;    Surface electromyography;    Tibialis anterior muscle;    Sonomyography;   

Others  :  797218
DOI  :  10.1186/1475-925X-13-5

 received in 2013-12-02, accepted in 2014-01-22,  发布年份 2014

【 摘 要 】

Background

Improvement in muscle strength is an important aim for the rehabilitation of hemiplegia patients. Presently, the rehabilitation prescription depends on the evaluation results of muscle strength, which are routinely estimated by experienced physicians and therefore not finely quantitative. Widely-used quantification methods for disability, such as Barthel Index (BI) and motor component of Functional Independent Measure (M-FIM), yet have limitations in their application, since both of them differentiated disability better in lower than higher disability, and they are subjective and recorded in wide scales. In this paper, to explore finely quantitative measures for evaluation of muscle strength level (MSL), we start with the study on quantified electromyography (EMG) and sonomyography (SMG) features of tibialis anterior (TA) muscles among hemiplegia patients.

Methods

12 hemiplegia subjects volunteered to perform several sets of plantar-flexion movements in the study, and their EMG signals and SMG signals were recorded on TA independently to avoid interference. EMG data were filtered and then the root-mean-square (RMS) was computed. SMG signals, specifically speaking, the muscle thickness of TA, were manually measured by two experienced operators using ultrasonography. Reproducibility of the SMG assessment on TA between operators was evaluated by non-parametric test (independent sample T test). Possible relationship between muscle thickness changes (TC) of TA and muscle strength level of hemiplegia patients was estimated.

Results

Mean of EMG RMS between subjects is found linearly correlated with MSL (R² = 0.903). And mean of TA muscle TC amplitudes is also linearly correlated with MSL among dysfunctional legs (R² = 0.949). Moreover, rectified TC amplitudes (dysfunctional leg/ healthy leg, DLHL) and rectified EMG signals (DLHL) are found in linear correlation with MSL, with R² = 0.756 and R² = 0.676 respectively. Meanwhile, the preliminary results demonstrate that patients’ peak values of TC are generally proportional to their personal EMG peak values in 12 dysfunctional legs and 12 healthy legs (R² = 0.521).

Conclusions

It’s concluded that SMG could be a promising option to quantitatively estimate MSL for hemiplegia patients during rehabilitation besides EMG. However, after this exploratory study, they should be further investigated on a larger number of subjects.

【 授权许可】

   
2014 Li et al.; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

Figure 2.

Figure 3.

Figure 4.

Figure 5.

Figure 6.

Figure 7.

Figure 8.

Figure 9.

Figure 10.

Figure 11.

Figure 12.

【 参考文献 】

• [1]Chan CWY: Motor and sensory deficits following a stroke: relevance to a comprehensive evaluation. Physiother Can 1986, 38:29-34.
• [2]Andrews AW, Bohannon RW: Distribution of muscle strength impairments following stroke. Clin Rehabil 2000, 14:79-87.
• [3]Fugl-Meyer AR, Jaasko L, Leyman I, Olsson S, Steglind S: The post-stroke hemiplegic patient: a method for evaluation of physical performance. Scand J Rehabil Med 1975, 7:13-31.
• [4]Goldstein LB, Bertels C, Davis JN: Interrater reliability of the Nih stroke scale. Arch Neurol-Chicago 1989, 46:660-662.
• [5]Group SSS: Multicenter trial of hemo dilution in ischemic stroke-background and study protocol. Stroke 1985, 16:885-890.
• [6]Guo YB, Wang EY, Cai LH: Application of quality of life scale in stroke. Chin J Rehabil Theory Pract 2009, 15:632-634.
• [7]Yu L, Wang JP, Fang Q, Wang Y: Brunnstrom stage automatic evaluation for stroke patients using extreme learning machine. In IEEE BioCAS Conf. Hsinchu, Taiwan: IEEE; 2012:380-383.
• [8]Kwon S, Hartzema AG, Duncan PW, Lai SM: Disability measures in stroke - relationship among the barthel index, the functional independence measure, and the modified rankin scale. Stroke 2004, 35:918-923.
• [9]Richard WB, Melissa BS: Assessment of strength deficits in eight paretic upper extremity muscle groups of stroke patients with hemiplegia. Phys Ther 1987, 67:522-525.
• [10]Brown M, Sinacore DR, Host HH: The relationship of strength to function in the older adult. J Gerontol a-Biol 1995, 50:55-59.
• [11]Buchner DM: Preserving mobility in older adults. Western J Med 1997, 167:258-264.
• [12]Hyatt RH, Whitelaw MN, Bhat A, Scott S, Maxwell JD: Association of muscle strength with functional status of elderly people. Age Ageing 1990, 19:330-336.
• [13]Brill PA, Macera CA, Davis DR, Blair SN, Gordon N: Muscular strength and physical function. Med Sci Sport Exer 2000, 32:412-416.
• [14]Hof AL: EMG and muscle force: an introduction. Human Movement Sci 1984, 3:119-153.
• [15]Thongpanja AP S, Phukpattaranout P, Limsakul C: Mean and median frequency of EMG signal determine muscle force based on time-dependent power spectrum. Electronics and Electrical Engineering 2013, 19:51-56.
• [16]Guimaraes AC, Herzog W, Allinger TL, Zhang YT: The Emg-force relationship of the Cat soleus muscle and its association with contractile conditions during locomotion. J Exp Biol 1995, 198:975-987.
• [17]Mannion AF, Dolan P: The effects of muscle length and force output on the EMG power spectrum of the erector spinae. Human J Electromyogr Kinesiol 1996, 6:159-168.
• [18]Lee J, Kagamihara Y, Kakei S: Quantitative evaluation of movement disorders in neurological diseases based on EMG signals. In 30th Annual International IEEE EMBS Conference; August 20-24. Vancouver, British Columbia, Canada: IEEE; 2008.
• [19]Shwedyk E, Balasubramanian R, Scott RN: A nonstationary model for the Electromyogram. IEEE Trans Biomed Eng 1977, 24:417-424.
• [20]Bohannon RW, Larkin PA, Smith MB, Horton MG: Relationship between static muscle strength deficits and spasticity in stroke patients with hemiparesis. Phys Ther 1987, 67:1068-1071.
• [21]Perry J, Bekey GA: Emg-force relationships in skeletal-muscle. Crit Rev Biomed Eng 1981, 7:1-22.
• [22]Onishi H, Yagi R, Akasaka K, Momose K, Ihashi K, Handa Y: Relationship between EMG signals and force in human vastus lateralis muscle using multiple bipolar wire electrodes. J Electromyogr Kines 2000, 10:59-67.
• [23]Kuriki HU, Takahashi LSO: Relationship between electromyography and muscle force. In EMG Methods for evaluating muscle and nerve function Edited by Schwartz M. 2012. http://www.intechopen.com/download/get/type/pdfs/id/25852 webcite
• [24]Sabut SK, Lenka PK, Kumar R, Mahadevappa M: Effect of functional electrical stimulation on the effort and walking speed, surface electromyography activity, and metabolic responses in stroke subjects. J Electromyogr Kines 2010, 20:1170-1177.
• [25]Arya BK, Mohapatra J, Subramanya K, Prasad H, Kumar R, Mahadevappa M: Surface EMG analysis and changes in gait following electrical stimulation of quadriceps femoris and tibialis anterior in children with spastic cerebral palsy. In IEEE EMBS Conf. San Diego, CA, United states: IEEE; 2012:5726-5729.
• [26]DeLuca CJ: Surface Electromyography: Detection and Recording. Boston: DelSys Incorporated; 2002:1-10. [Surface Electromyography: Detection and Recording]
• [27]Alemu M, Kumar DK, Bradley A: Time-frequency analysis of SEMG - With special consideration to the interelectrode spacing. Ieee T Neur Sys Reh 2003, 11:341-345.
• [28]Guo JY, Zheng YP, Huang QH, Chen X, He JF: Comparison of sonomyography and electromyography of forearm muscles in the guided wrist extension. In Proc 5th International workshop on Wearable and Implantable body sensor Networks. HongKong, China: IEEE; 2008:235-238.
• [29]Pillen S: Skeletal muscle ultrasound. Eur J Transl Myol 2010, 1:145-155.
• [30]Zheng YP, Chan MMF, Shi J, Chen X, Huang QH: Sonomyography: monitoring morphological changes of forearm muscles in actions with the feasibility for the control of powered prosthesis. Med Eng Phys 2005, 28:405-415.
• [31]Shi J, Zheng YP, Chen X, Huang QH: Assessment of muscle fatigue using sonomyography: muscle thickness change detected from ultrasound images. Med Eng Phys 2007, 29:472-479.
• [32]Shi J, Zheng YP, Huang QH, Chen X: Continuous monitoring of sonomyography, electromyography and torque generated by normal upper arm muscles during isometric contraction: sonomyography assessment for arm muscles. IEEE Trans Biomed Eng 2007, 55:1191-1198.
• [33]Guo JY, Zheng YP, Huang QH, Chen X: Dynamic monitoring of forearm muscles using 1D sonomyography (SMG) system. J Rehabil Res 2008, 45:187-196.
• [34]Chen X, Zheng YP, Guo JY, Zhu ZY, Chan SC, Zhang ZG: Sonomyographic responses during voluntary isometric ramp contraction of the human rectus femoris muscle. Eur J Appl Physiol 2012, 112:2603-2614.
• [35]Li JZ, Zhou YJ, Ivanov K, Zheng YP: Estimation and visualization of longitudinal muscle motion using ultrasonography: a feasibility study. Ultrasonics 2014, 54:779-788.
• [36]Han P, Chen Y, Ao L, Xie G, Li H, Wang L, Zhou Y: Automatic thickness estimation for skeletal muscle in ultrasonography: evaluation of two enhancement methods. Biomed Eng Online 2013., 12doi:10.1186/1475-925X-12-6
• [37]Ling S, Zhou Y, Chen Y, Zhao Y, Wang L, Zheng Y: Automatic tracking of aponeuroses and estimation of muscle thickness in ultrasonography: a feasibility study. IEEE J Biomed Health Inform 2013, 11(6):1031-1038.
• [38]Ling S, Chen B, Zhou Y, Yang W, Zhao Y, Wang L, Zheng Y: An efficient framework for estimation of muscle fiber orientation using ultrasonography. Biomed Eng Online 2013, 12:98. doi:10.1186/1475-925X-12-98 BioMed Central Full Text
• [39]Li J, Zhou Y, Lu Y, Zhou G, Wang L, Zheng Y: Sensitive and efficient detection of quadriceps muscle thickness changes in cross-sectional plane using ultrasonography: a feasibility investigation. IEEE Trans Inf Technol Biomed 2013. doi:10.1109/JBHI.2013.2275002
• [40]Zhou Y, Li J, Zhou G, Zheng Y: Dynamic measurement of pennation angle of gastrocnemius muscles during contractions based on ultrasound imaging. Biomed Eng Online 2012., 11(63) doi:10.1186/1475-925X-11-63
• [41]Zhou Y, Zheng Y: Longitudinal enhancement of the hyperechoic regions in ultrasonography of muscles using a Gabor filter bank approach: a preparation for semi-automatic muscle fiber orientation estimation. Ultrasound Med Biol 2011, 37(4):665-673.
• [42]Zhou Y, Zheng Y: Estimation of muscle fiber orientation in ultrasound images using revoting hough transform (RVHT). Ultrasound Med Biol 2008, 34(9):1474-1481.
• [43]Rokicki LA, Houle TT, Dhingra LK, Weinland SR, Urban AM, Bhalla RK: A preliminary analysis of EMG variance as an index of change in EMG biofeedback treatment of tension-type headache. Appl Psychophysiol Biofeedback 2003, 28:205-215.
• [44]Gray VL, Ivanova TD, Garland SJ: Effects of fast functional exercise on muscle activity after stroke. Neurorehab Neural Re 2012, 26:968-975.
• [45]Bonell CE, Cherniz AS, Tabernig CB: Analysis of EMG temporal parameters from the tibialis anterior during hemiparetic gait. San Juan, Argentina: Journal of Physics:Conference Series; 2007:1-6. [16th Argentine Bioengineering Congress and the 5th Conference of Clinical Engineering] doi:10.1088/1742-6596/90/1/012087
• [46]Hwang S, Chung G, Lee J, Shin J, Lee SJ, Jeong DU, Park K: Sleep/wake estimation using only anterior tibialis electromyography data. Biomed Eng Online 2012, 11:1-15. BioMed Central Full Text