دوره 9، شماره 1 - ( January & February 2018 1396 )
جلد 9 شماره 1 صفحات 26-15 |
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Zadnia A, Kobravi H R, Sheikh M, Hosseini H A. Generating the Visual Biofeedback Signals Applicable to Reduction of Wrist Spasticity: A Pilot Study on Stroke Patients. BCN 2018; 9 (1) :15-26
URL: http://bcn.iums.ac.ir/article-1-848-fa.html
URL: http://bcn.iums.ac.ir/article-1-848-fa.html
Generating the Visual Biofeedback Signals Applicable to Reduction of Wrist Spasticity: A Pilot Study on Stroke Patients. مجله علوم اعصاب پایه و بالینی. 1396; 9 (1) :15-26
نوع مطالعه: Original |
موضوع مقاله:
Clinical Neuroscience
دریافت: 1395/8/26 | پذیرش: 1396/3/28 | انتشار: 1396/10/11
دریافت: 1395/8/26 | پذیرش: 1396/3/28 | انتشار: 1396/10/11
فهرست منابع
1. Acharya, U. R., Ng, E. Y. K., Swapna, G., & Michelle, Y. S. L. (2011). Classification of normal, neuropathic, and myopathic electromyograph signals using nonlinear dynamics method. Journal of Medical Imaging and Health Informatics, 1(4), 375–80. doi: 10.1166/jmihi.2011.1054 [DOI:10.1166/jmihi.2011.1054]
2. Alibiglou, L., Rymer, W. Z., Harvey, R. L., & Mirbagheri, M. M. (2008). The relation between Ashworth scores and neuromechanical measurements of spasticity following stroke. Journal of NeuroEngineering and Rehabilitation, 5(1), 18. doi: 10.1186/1743-0003-5-18 [DOI:10.1186/1743-0003-5-18]
3. Arjunan, S. P., & Kumar, D. K. (2007). Fractal theory based Non-linear analysis of sEMG. Paper presented at the 3rd International Conference on Intelligent Sensors, Sensor Networks and Information. Melbourne, Australia, 3-6 December 2007. doi: 10.1109/issnip.2007.4496901 [DOI:10.1109/ISSNIP.2007.4496901]
4. Armagan, O., Tascioglu, F., & Oner, C. (2003). Electromyographic biofeedback in the treatment of the hemiplegic hand. American Journal of Physical Medicine & Rehabilitation, 82(11), 856–61. doi: 10.1097/01.phm.0000091984.72486.e0 [DOI:10.1097/01.PHM.0000091984.72486.E0]
5. Bohannon, R. W., & Smith, M. B. (1987). Interrater reliability of a modified ashworth scale of muscle spasticity. Physical Therapy, 67(2), 206–7. doi: 10.1093/ptj/67.2.206 [DOI:10.1093/ptj/67.2.206]
6. Cifrek, M., Medved, V., Tonković, S., & Ostojić, S. (2009). Surface EMG based muscle fatigue evaluation in biomechanics. Clinical Biomechanics, 24(4), 327–40. doi: 10.1016/j.clinbiomech.2009.01.010 [DOI:10.1016/j.clinbiomech.2009.01.010]
7. Doğan Aslan, M., Nakipoğlu Yüzer, G. F., Doğan, A., Karabay, İ., & Özgirgin, N. (2012). The effect of electromyographic biofeedback treatment in improving upper extremity functioning of patients with hemiplegic stroke. Journal of Stroke and Cerebrovascular Diseases, 21(3), 187–92. doi: 10.1016/j.jstrokecerebrovasdis.2010.06.006 [DOI:10.1016/j.jstrokecerebrovasdis.2010.06.006]
8. Fukuda, T. Y., Echeimberg, J. O., Pompeu, J. E., Lucareli, P. R. G., Garbelotti, S., Gimenes, R. O., et al. (2010). Root mean square value of the electromyographic signal in the isometric torque of the quadriceps, hamstrings and rachial biceps muscles in female subjects. The Journal of Applied Research, 10(1), 32-9.
9. Georgakis, A., Stergioulas, L. K., & Giakas, G. (2003). Fatigue analysis of the surface EMG signal in isometric constant force contractions using the averaged instantaneous frequency. IEEE Transactions on Biomedical Engineering, 50(2), 262–5. doi: 10.1109/tbme.2002.807641 [DOI:10.1109/TBME.2002.807641]
10. Gupta, V., Suryanarayanan, S., & Reddy, N. P. (1997). Fractal analysis of surface EMG signals from the biceps. International Journal of Medical Informatics, 45(3), 185–92. doi: 10.1016/s1386-5056(97)00029-4 [DOI:10.1016/S1386-5056(97)00029-4]
11. Harishchandre, M. S., & Singaravelan, R. M. (2012). Effectiveness of EMG biofeedback on improving hand function in hemiplegic stroke patients. Romanian Journal of Physical Therapy/Revista Romana de Kinetoterapie, 18(30), 56-63.
12. Hassan, M., Terrien, J., Marque, C., & Karlsson, B. (2011). Comparison between approximate entropy, correntropy and time reversibility: Application to uterine electromyogram signals. Medical Engineering & Physics, 33(8), 980–6. doi: 10.1016/j.medengphy.2011.03.010 [DOI:10.1016/j.medengphy.2011.03.010]
13. Hu, X., Wang, Z., & Ren, X. (2005). Classification of surface EMG signal with fractal dimension. Journal of Zhejiang University Sciences, 6B(8), 844–8. doi: 10.1631/jzus.2005.b0844 [DOI:10.1631/jzus.2005.B0844]
14. Huang, H., Wolf, S. L., & He, J. (2006). Recent developments in biofeedback for neuromotor rehabilitation. Journal of neuroengineering and rehabilitation, 3(1), 11. doi: 10.1186/1743-0003-3-11 [DOI:10.1186/1743-0003-3-11]
15. Kaufman, M., Zurcher, U., & Sung, P. S. (2007). Entropy of electromyography time series. Physica A: Statistical Mechanics and Its Applications, 386(2), 698–707. doi: 10.1016/j.physa.2007.07.045 [DOI:10.1016/j.physa.2007.07.045]
16. Lei, M., Wang, Z., & Feng, Z. (2001). Detecting nonlinearity of action surface EMG signal. Physics Letters A, 290(5-6), 297–303. doi: 10.1016/s0375-9601(01)00668-5 [DOI:10.1016/S0375-9601(01)00668-5]
17. Merletti, R., & Parker, P. A. (2004). Electromyography: Physiology, engineering, and non-invasive applications. Hoboken, NJ: Wiley & Sons. [DOI:10.1002/0471678384]
18. Nelson, L. A. (2007). The role of biofeedback in stroke rehabilitation: Past and future directions. Topics in Stroke Rehabilitation, 14(4), 59–66. doi: 10.1310/tsr1404-59 [DOI:10.1310/tsr1404-59]
19. Padmanabhan, P., & Puthusserypady, S. (2004). Nonlinear analysis of EMG signals: A chaotic approach. Paper presented at The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. San Francisco, United States, 1-5 September 2004. [DOI:10.1109/IEMBS.2004.1403231] [PMID]
20. Phinyomark, A., Limsakul, C., & Phukpattaranont, P. (2011). Application of wavelet analysis in EMG feature extraction for pattern classification. Measurement Science Review, 11(2), 45-52. doi: 10.2478/v10048-011-0009-y [DOI:10.2478/v10048-011-0009-y]
21. Phung, D. Q., Tran, D., Ma, W., Nguyen, P., & Pham, T. (2014). Using shannon entropy as EEG signal feature for fast person identi cation. Paper presented at the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Bruges, Belgium, 23-5 April 2014.
22. Wolf, S. L. (1983). Electromyographic biofeedback applications to stroke patients. Physical Therapy, 63(9), 1448–59. doi: 10.1093/ptj/63.9.1448 [DOI:10.1093/ptj/63.9.1448]
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