An endpoint-detection algorithm of surface electromyography insensitive to electrocardiogram interference_Journal of Biomedical Engineering

Authors：

XU Weizhao ,  MO Hongqiang , TIAN Lianfang , OU Demiao

School of Automation Science and Engineering, South China University of Technology, Guangzhou 510641, P.R.China;

Corresponding?author：

MO Hongqiang, Email: hqiangmo@scut.edu.cn

Keywords：

surface electromyography; endpoint detection; short-time energy; short-time zero-crossing rate

DOI：

10.7507/1001-5515.201703084

Video：

Export PDF Favorites Scan Get Citation

Abstract Full text Figures/Tables Video References Cited by

Surface electromyography (sEMG) has been widely used in the study of clinical medicine, rehabilitation medicine, sports, etc., and its endpoints should be detected accurately before analyzing. However, endpoint detection is vulnerable to electrocardiogram (ECG) interference when the sEMG recorders are placed near the heart. In this paper, an endpoint-detection algorithm which is insensitive to ECG interference is proposed. In the algorithm, endpoints of sEMG are detected based on the short-time energy and short-time zero-crossing rates of sEMG. The thresholds of short-time energy and short-time zero-crossing rate are set according to the statistical difference of short-time zero-crossing rate between sEMG and ECG, and the statistical difference of short-time energy between sEMG and the background noise. Experiment results on the sEMG of rectus abdominis muscle demonstrate that the algorithm detects the endpoints of the sEMG with a high accuracy rate of 95.6%.

Citation： XU Weizhao, MO Hongqiang, TIAN Lianfang, OU Demiao. An endpoint-detection algorithm of surface electromyography insensitive to electrocardiogram interference. Journal of Biomedical Engineering, 2018, 35(6): 953-958, 963. doi: 10.7507/1001-5515.201703084 Copy

1.	Jeffrey R C, Glenn S K. Introduction to surface electromyography. Maryland: An Aspen Publication, 1998.
2.	Jalil M, Butt F A, Malik A. Short-time energy, magnitude, zero crossing rate and autocorrelation measurement for discriminating voiced and unvoiced segments of speech signals//2013 International Conference on Technological Advances in Electrical, Electronics and Computer Engineering (TAEECE). Konya, Turkey: IEEE, 2013: 208-212.
3.	陽國清. 連續語音流中咳嗽信號的識別. 廣州: 華南理工大學, 2010.
4.	Hu Y, Li X H, Xie X B, et al. Applying independent component analysis on ECG cancellation technique for the surface recording of trunk electromyography//27th Annual International Conference of the Engineering in Medicine and Biology Society, 2005. IEEE-EMBS 2005. Shanghai, China: IEEE, 2006: 3647-3649.
5.	Hu Y, Mak J, Liu H, et al. ECG cancellation for surface electromyography measurement using independent component analysis//IEEE International Symposium on Circuits and Systems. New Orleans, LA, USA: IEEE, 2007: 3235-3238.
6.	Kadefors R, Broman H, de Luca C J. Units, terms and standards in the reporting of EMG research. Boston: the Ad Hoc Committee of the International Society of Electrophysiogical Kinesiology, 1980.
7.	Redfern M, Hughes R, Chaffin D. High-pass filtering to remove electrocardiographic interference from torso EMG recordings. Clin Biomech (Bristol, Avon), 1993, 8(1): 44-48.
8.	Drake J D M, Callaghan J P. Elimination of electrocardiogram contamination from electromyogram signals: An evaluation of currently used removal techniques. Journal of Electromyography & Kinesiology, 2006, 16(2): 175-187.
9.	Mak J N F, Hu Y, Luk K D K. ICA-based ECG removal from surface electromyography and its effect on low back pain assessment//International IEEE/EMBS Conference on Neural Engineering. Kohala Coast, HI, USA: IEEE, 2007: 646-649.
10.	Costa J D, Seixas J M, de Sa A M F L M. Independent component analysis to remove ECG interference from single channel electromyographic signal//2013 ISSNIP Biosignals and Biorobotics Conference: Biosignals and Robotics for Better and Safer Living (BRC). Rio de Janerio, Brazil: IEEE, 2013: 1-5.
11.	Nougarou F, Massicotte D, Descarreaux M. Efficient combination of DWT and ICA to localize and remove ECG from surface electromyography measurement//201318th International Conference on Digital Signal Processing (DSP). Fira, Greece: IEEE, 2013: 1-6.
12.	周權, 楊智, 范正平, 等. 結合小波變換和能量熵在膈肌肌電中去除心電干擾的應用. 生物醫學工程學雜志, 2013, 30(1): 16-21.
13.	Sano T, Shiozawa T, Uenosono N, et al. Recommendations for standardization of leads and of specifications for instruments in electrocardiography and vectorcardiography (report of Committee on Electrocardiography, American Heart Association). Journal of Japanese Medical Instruments, 1970, 40: 242-258.
14.	Merletti R. Standards for Reporting EMG Data. Journal of Electromyography & Kinesiology, 2014, 24(2): I-II.

1. Jeffrey R C, Glenn S K. Introduction to surface electromyography. Maryland: An Aspen Publication, 1998.
2. Jalil M, Butt F A, Malik A. Short-time energy, magnitude, zero crossing rate and autocorrelation measurement for discriminating voiced and unvoiced segments of speech signals//2013 International Conference on Technological Advances in Electrical, Electronics and Computer Engineering (TAEECE). Konya, Turkey: IEEE, 2013: 208-212.
3. 陽國清. 連續語音流中咳嗽信號的識別. 廣州: 華南理工大學, 2010.
4. Hu Y, Li X H, Xie X B, et al. Applying independent component analysis on ECG cancellation technique for the surface recording of trunk electromyography//27th Annual International Conference of the Engineering in Medicine and Biology Society, 2005. IEEE-EMBS 2005. Shanghai, China: IEEE, 2006: 3647-3649.
5. Hu Y, Mak J, Liu H, et al. ECG cancellation for surface electromyography measurement using independent component analysis//IEEE International Symposium on Circuits and Systems. New Orleans, LA, USA: IEEE, 2007: 3235-3238.
6. Kadefors R, Broman H, de Luca C J. Units, terms and standards in the reporting of EMG research. Boston: the Ad Hoc Committee of the International Society of Electrophysiogical Kinesiology, 1980.
7. Redfern M, Hughes R, Chaffin D. High-pass filtering to remove electrocardiographic interference from torso EMG recordings. Clin Biomech (Bristol, Avon), 1993, 8(1): 44-48.
8. Drake J D M, Callaghan J P. Elimination of electrocardiogram contamination from electromyogram signals: An evaluation of currently used removal techniques. Journal of Electromyography & Kinesiology, 2006, 16(2): 175-187.
9. Mak J N F, Hu Y, Luk K D K. ICA-based ECG removal from surface electromyography and its effect on low back pain assessment//International IEEE/EMBS Conference on Neural Engineering. Kohala Coast, HI, USA: IEEE, 2007: 646-649.
10. Costa J D, Seixas J M, de Sa A M F L M. Independent component analysis to remove ECG interference from single channel electromyographic signal//2013 ISSNIP Biosignals and Biorobotics Conference: Biosignals and Robotics for Better and Safer Living (BRC). Rio de Janerio, Brazil: IEEE, 2013: 1-5.
11. Nougarou F, Massicotte D, Descarreaux M. Efficient combination of DWT and ICA to localize and remove ECG from surface electromyography measurement//201318th International Conference on Digital Signal Processing (DSP). Fira, Greece: IEEE, 2013: 1-6.
12. 周權, 楊智, 范正平, 等. 結合小波變換和能量熵在膈肌肌電中去除心電干擾的應用. 生物醫學工程學雜志, 2013, 30(1): 16-21.
13. Sano T, Shiozawa T, Uenosono N, et al. Recommendations for standardization of leads and of specifications for instruments in electrocardiography and vectorcardiography (report of Committee on Electrocardiography, American Heart Association). Journal of Japanese Medical Instruments, 1970, 40: 242-258.
14. Merletti R. Standards for Reporting EMG Data. Journal of Electromyography & Kinesiology, 2014, 24(2): I-II.

Previous Article
Direct brain-controlled multi-robot cooperation task
Next Article
Biomechanical research progress on sex differences of abdominal aortic aneurysm

Journal of Biomedical Engineering

An endpoint-detection algorithm of surface electromyography insensitive to electrocardiogram interference

Abstract Full text Figures/Tables Video References Cited by

Previous Article

Next Article

Format

Content