Rehabilitation Assessment Based on the Complexity and Fluctuation of EMG Signal

Abstract:

The assessment after rehabilitation is an important part of rehabilitation medicine, and of great significance for the basic research and clinical application for rehabilitation medicine. Physician can make objective analysis about patients’ present conditions, and then appropriate training program can be developed for patients to recover their body function more effectively. Three kinds of EMG signal, that is, the healthy, patient with myopathy, and patient with neuropathy, are involved. The complexity of the three types of signal is compared and the related variables (embedding dimension and time delay of phase space reconstruction) are determined by singular-value decomposition and mutual information method. Moreover, a novel effective approach improved from Hilbert-Huang Transform (HHT) is proposed here for the further comparision among the three EMG signals. The results show both methods can distinguish the three kinds of EMG signal clearly.

• Periodical: Advanced Engineering Forum (Volumes 6-7)
• Pages: 530-535
• DOI: https://doi.org/10.4028/www.scientific.net/AEF.6-7.530
• Online since: September 2012
• Authors: Qing Song Ai, Ling Chen, Wei Meng
• Keywords: Complexity, Embedding Dimension, EMG, Fluctuation, Rehabilitation Assessment

References

[1] L. Zollo, L. Rossini, M. Bravi, Quantitative evaluation of upper-limb motor control in robot-aided rehabilitation. Med. Biol. Eng. Comput., 2011, 49: 1131–1144.

DOI: 10.1007/s11517-011-0808-1

[2] W. Shi, X. Li, W. Wang, et al., Clinical Effects of Comprehensive Rehabilitation Treatment on Gastronimius Surface Electromyography in Stroke Patients. Chin. J. Rehabil. Med., 2010, 2(25): 103-105.

[3] Y. Y. Huang, K. H. Low, Comprehensive Planning of Robotic Therapy and Assessment of Task-Oriented Functions via Improved QFD Applicable to Hand Rehabilitation. 6th annual IEEE Conference on Automation Science and Engineering. (2010): 252-257.

DOI: 10.1109/coase.2010.5584084

[4] I. Cikajlo, Z. Matjacic. Directionally Specific Objective Postural Response Assessment Tool for Treatment Evaluation in Stroke Patients. IEEE T. Neur. Sys. Reh., 2009, 1(17): 91-100.

DOI: 10.1109/tnsre.2008.2010477

[5] M. Casadio, P. Morasso, A. N. Ide. Measuring functional recovery of hemiparetic subjects during gentle robot therapy. Measurement, (2009): 1176-1187.

DOI: 10.1016/j.measurement.2008.09.012

[6] S. Hussain, S. Q. Xie, G.Y. Liu, Robot assisted treadmill training: Mechanisms and training strategies. Med. Eng. Phys., (2011): 527-533.

DOI: 10.1016/j.medengphy.2010.12.010

[7] X.L. Hu, K.Y. Tong, R. Li, J.J. Xue, S.K. Ho, P. Chen, The effects of electromechanical wrist robot assistive system with neuromuscular electrical stimulation for stroke rehabilitation. J. Electromyogr. Kines., (2012).

DOI: 10.1016/j.jelekin.2011.12.010

[8] M.A. Oskoei, H.S. Hu., Myoelectric control systems - A survey. Biomed. Signal Proces., 2 (2007): 275–294.

[9] T. Yan, W.Y.C. Hui-Chan. Maximum isometric voluntary contraction of ankle dorsiflexors and plantarflexors : comparison of surface electromyography between patients at the acute stage after stroke and age-matched healthy elderly. Chin. J. Phys. Med. Rehabil., (2003).

[10] Q. Cai, D. Sun, Y. tan, et al., SEMG signal change before and after the rehabilitation of patients of hemiplegia. Chin. J. Rehabil. Med., (2008) 4(23): 347-348.

[11] K.M. haithanya, B. Jorge, K. T. Christine. Automatic analysis of EMG during clonus. J. Neurosci. Meth., (2012): 35-43.

[12] N. E. Huang, Introduction to the Hilbert Huang transform and its related mathematical problems, (2005) 1-20.