Data Collection of Signals in the Multi-channel sEMG System of Masticatory Muscles and Development and Preliminary Clinical Application of an Analytic System_Journal of Biomedical Engineering

Authors：

DUHongliang ¹ , LIXin ¹ , LIShan ¹ , ZHANGRui ¹ , SONGRong ¹ , LILan ² , WANGWei ² ,  KANGHong ¹

1. Institute of Stomatology, School of Stomatology, Lanzhou University, Lanzhou 730000, China;
2. Institute of Biomedical Engineering, School of Information and Engineering, Lanzhou University, Lanzhou 730000, China;

Corresponding author：

KANGHong, Email: kanghong@lzu.edu.cn

Keywords：

masticatory muscles; surface electromyography; temporomandibular disorders; signal analysis

DOI：

10.7507/1001-5515.20140005

Video：

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The aim of this study was to design a simple, economic, with high Common Mode Rejection Ratio (CMRR), preamplifier and multi-channel masticatory muscle surface electromyography (sEMG) signal acquisition system assisting to diagnose temporomandibular disorders (TMD). We used the USB interface technology in the EMG data with the aid of the windows to operate system and graphical interface. Eight patients with TMD and eight controls were analyzed separately using this system. In this system, we analyzed sEMG by an optional combination of time domain, frequency domain, time-frequency, several spectral analysis, wavelets and other special algorithms under multi-parameter. Multi-channel sEMG System of Masticatory Muscles is a simple, economic system. It has high sensitivity and specificity. The sEMG signals were changed in patients with TMD. The system would pave the way for diagnosis TMD and help us to assess the treatment effect. A novel and objective method is provided for diagnosis and treatment of oral-maxillofacial disease and functional reconstruction.

Citation： DUHongliang, LIXin, LIShan, ZHANGRui, SONGRong, LILan, WANGWei, KANGHong. Data Collection of Signals in the Multi-channel sEMG System of Masticatory Muscles and Development and Preliminary Clinical Application of an Analytic System. Journal of Biomedical Engineering, 2014, 31(1): 23-28. doi: 10.7507/1001-5515.20140005 Copy

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2.	CRARY M A, CARNABY G M, GROHER M E, et al. Functional benefits of dysphagia therapy using adjunctive sEMG biofeedback[J]. Dysphagia, 2004, 19(3):160-164.
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4.	CHOI C, RIM B, KIM J. Development and evaluation of a assistive computer interface by SEMG for individuals with spinal cord injuries[J]. IEEE Int Conf Rehabil Robot, 2011:5975386.
5.	IYER M, VALIATHAN A. Electromyography and its application in orthodontics[J]. Curr Sci, 2001, 80(4):503-507.
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7.	SUVINEN T I, KEMPPAINEN P. Review of clinical EMG studies related to muscle and occlusal factors in healthy and TMD subjects[J]. J Oral Rehabil, 2007, 34(9):631-644.
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16.	CONSTABLE R, THORNHILL R J, CARPENTER D R. Time-frequency analysis of the surface EMG during maximum height jumps under altered-G conditions[J]. Biomed Sci Instrum, 1994, 30:69-74.
17.	加玉涛,罗志增.肌电信号特征提取方法综述[J].电子器件,2007,30(1):326-330.
18.	杨超,王云,王邦安,等.正常(牙合)成人咀嚼肌表面肌电图的参数分析[J].皖南医学院学报,2011,30(5):345-350.
19.	杨广映,罗志增.肌电信号的功率谱分析方法[J].传感技术学报,2004,17(3):355-358.
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21.	HUSSAIN M S, MAMUN M. Effectiveness of the wavelet transform on the surface EMG to understand the muscle fatigue during walk[J]. Meas Sci Rev, 2012, 12(1):28-33.
22.	RAEZ M B, HUSSAIN M S, MOHD-YASIN F. Techniques of EMG signal analysis:detection, processing, classification and applications[J]. Biol Proced Online, 2006, 8:11-35.
23.	KUMAR D K, PAH N D, BRADLEY A. Wavelet analysis of surface electromyography to determine muscle fatigue[J]. IEEE Trans Neural Syst Rehabil Eng, 2003, 11(4):400-406.
24.	ESWARAN H, WILSON J D, MURPHY P, et al. Application of wavelet transform to uterine electromyographic signals recorded using abdominal surface electrodes[J]. J Matern Fetal Neonatal Med, 2002, 11(3):158-166.
25.	KILBY J, GHOLAM H H. Wavelet analysis of surface electromyography signals[J]. Conf Proc IEEE Eng Med Biol Soc, 2004, 1:384-387.
26.	ÖZKAYA U, DURAK L, et al. A wavelet transfom-based analysis of surface EMG signals[C]//IEEE 15th Signal Processing and Communications Applications. Eskisehir:2007:1-4.
27.	SANTANA-MORA U, CUDEIRO J, MORA-BERM?DEZ M J, et al. Changes in EMG activity during clenching in chronic pain patients with unilateral temporomandibular disorders[J]. J Electromyogr Kinesiol, 2009, 19(6):e543-e549.

1. RYAIT H S, ARORA A S, AGARWAL R. SEMG signal analysis at acupressure points for elbow movement[J]. J Electromyogr Kinesiol, 2011, 21(5):868-876.
2. CRARY M A, CARNABY G M, GROHER M E, et al. Functional benefits of dysphagia therapy using adjunctive sEMG biofeedback[J]. Dysphagia, 2004, 19(3):160-164.
3. KWON S, PARK H S, STANLEY C J, et al. A practical strategy for sEMG-based knee joint moment estimation during gait and its validation in individuals with cerebral palsy[J]. IEEE Trans Biomed Eng, 2012, 59(5):1480-1487.
4. CHOI C, RIM B, KIM J. Development and evaluation of a assistive computer interface by SEMG for individuals with spinal cord injuries[J]. IEEE Int Conf Rehabil Robot, 2011:5975386.
5. IYER M, VALIATHAN A. Electromyography and its application in orthodontics[J]. Curr Sci, 2001, 80(4):503-507.
6. HOTTA P T, HOTTA T H, BATAGLION C, et al. Emg analysis after laser acupuncture in patients with temporomandibular dysfunction (TMD). Implications for practice[J]. Complement Ther Clin Pract, 2010, 16(3):158-160.
7. SUVINEN T I, KEMPPAINEN P. Review of clinical EMG studies related to muscle and occlusal factors in healthy and TMD subjects[J]. J Oral Rehabil, 2007, 34(9):631-644.
8. 王建超,梁锐英,吴文慧.咀嚼肌肌电图在口腔研究领域的应用[J].当代医学,2010,16(3):21-22.
9. 陈玉琴,钱海馨,耿屹,等.牙列重度磨耗和磨牙症患者咀嚼肌肌电信号分析[J].上海交通大学学报(医学版),2008,28(2):200-202.
10. STEPP C E. Surface electromyography for speech and swallowing systems:measurement, analysis, and interpretation[J]. J Speech Lang Hear Res, 2012, 55(4):1232-1246.
11. 何庆华,吴宝明,彭承琳.表面肌电信号的分析与应用[J].国外医学生物医学工程分册,2000,23(5):299-303.
12. 吴冬梅,孙欣,张志成,等.表面肌电信号的分析和特征提取[J].中国组织工程研究与临床康复,2010,14(43):8073-8076.
13. ZHOU Y, LI Y, WANG R. Evaluation of exercise-induced muscle damage by surface electromyography[J]. J Electromyogr Kinesiol, 2011, 21(2):356-362.
14. HANSSON G. Modeling nonlinear errors in surface electromyography due to baseline noise:a new methodology[J]. J Biomech, 2011, 44(8):1637; author reply 1637-1638.
15. 吴銮,黄鹏程,鲍官军,等.表面肌电信号分析及其在康复医学中的应用[J].机电工程,2011,28(11):1368-1373.
16. CONSTABLE R, THORNHILL R J, CARPENTER D R. Time-frequency analysis of the surface EMG during maximum height jumps under altered-G conditions[J]. Biomed Sci Instrum, 1994, 30:69-74.
17. 加玉涛,罗志增.肌电信号特征提取方法综述[J].电子器件,2007,30(1):326-330.
18. 杨超,王云,王邦安,等.正常(牙合)成人咀嚼肌表面肌电图的参数分析[J].皖南医学院学报,2011,30(5):345-350.
19. 杨广映,罗志增.肌电信号的功率谱分析方法[J].传感技术学报,2004,17(3):355-358.
20. CANAL M R. Comparison of wavelet and short time Fourier transform methods in the analysis of EMG signals[J]. J Med Syst, 2010, 34(1):91-94.
21. HUSSAIN M S, MAMUN M. Effectiveness of the wavelet transform on the surface EMG to understand the muscle fatigue during walk[J]. Meas Sci Rev, 2012, 12(1):28-33.
22. RAEZ M B, HUSSAIN M S, MOHD-YASIN F. Techniques of EMG signal analysis:detection, processing, classification and applications[J]. Biol Proced Online, 2006, 8:11-35.
23. KUMAR D K, PAH N D, BRADLEY A. Wavelet analysis of surface electromyography to determine muscle fatigue[J]. IEEE Trans Neural Syst Rehabil Eng, 2003, 11(4):400-406.
24. ESWARAN H, WILSON J D, MURPHY P, et al. Application of wavelet transform to uterine electromyographic signals recorded using abdominal surface electrodes[J]. J Matern Fetal Neonatal Med, 2002, 11(3):158-166.
25. KILBY J, GHOLAM H H. Wavelet analysis of surface electromyography signals[J]. Conf Proc IEEE Eng Med Biol Soc, 2004, 1:384-387.
26. ÖZKAYA U, DURAK L, et al. A wavelet transfom-based analysis of surface EMG signals[C]//IEEE 15th Signal Processing and Communications Applications. Eskisehir:2007:1-4.
27. SANTANA-MORA U, CUDEIRO J, MORA-BERM?DEZ M J, et al. Changes in EMG activity during clenching in chronic pain patients with unilateral temporomandibular disorders[J]. J Electromyogr Kinesiol, 2009, 19(6):e543-e549.

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