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A Selection Method of Optimal Digital Low-pass Differentiator for Spike Detection of Surface Motor Unit Action Potential

표면 운동단위 활동전위 스파이크 검출을 위한 최적의 디지털 저역통과 미분기 선정 방법

  • 이진 (강원대학교 삼척캠퍼스 제어계측공학과) ;
  • 김성환 (서울시립대학교 전자전기컴퓨터공학부)
  • Received : 2011.06.28
  • Accepted : 2011.08.31
  • Published : 2011.10.01

Abstract

The objective of this study is to analyze the performance of digital low-pass differentiators(LPD) and then to provide a method to select effective LPD filter, for detecting spikes of surface motor unit action potentials(MUAP). The successful spike detection of MUAPs is a first important step for EMG signal decomposition. The performances of simple and weighted LPD(SLPD and WLPD) filters are analyzed based on different filter lengths and varying MUAPs from simulated surface EMG signals. The SNR improving coefficient and effective MUAP duration range from the analysis results can be used to select proper LPD filters under the varying conditions of surface EMG.

References

  1. JV. Basmajian and CJ. De Luca, Muscles alive : Their functions revealed by electromyography., Baltimore, MD, Williams & Wilkins, 1985.
  2. RS. LeFever and CJ. DeLuca, "A procedure for decomposing myoelectric signal into its constituent action potentials. Part I," IEEE Trans. BME, vol. BME-29, 149-157, 1982. https://doi.org/10.1109/TBME.1982.324881
  3. KC. McGill, KL. Cummins, LJ. Dorfman, "Automatic decomposition of the clinical electromyogram," IEEE Trans. BME, vol. 32, 470-477, 1985.
  4. 이 진, 조일준, 변윤식, 홍완희, 김성환, "SMUAP의 패턴 분류를 위한 근신호 처리 알고리듬," 대한전자공학회지, 제 26 권, 제 7 호, 106-111, 1989.
  5. 이진, 김종원, 김성환, "Decomposition of EMG Signal using MAMDF Filtering and Digital Signal Processor," 의공학회지, 제15권, 제3호, 281-287, 1994.
  6. GA. Garcia, R. Okuno, K. Akazawa, "A decomposition algorithm for surface electrode-array electromyogram," IEEE Engineering in Medicine & Biology Magazine, July/August, 63-72, 2005. https://doi.org/10.1109/MEMB.2005.1463398
  7. SH. Nawab, SS. Chang, CJ. De Luca, "High-yield decomposition of surface EMG signals," Clinical Neurophysiology, Vol. 121, Issue 10, 1602-1615, 2010. https://doi.org/10.1016/j.clinph.2009.11.092
  8. S. Usui and I. Amidror, "Digital low-pass differentiation for biological signal processing," IEEE Trans. BME, vol. BME-29, 686-693, 1982. https://doi.org/10.1109/TBME.1982.324861
  9. J. Lee, A. Adam, CJ. DeLuca, "A simulation study for a surface EMG sensor that detects distinguishable motor unit action potentials," J. of Neuroscience method, vol. 168, No.1, 54-63, 2008. https://doi.org/10.1016/j.jneumeth.2007.09.007
  10. Z. Xu and S. Xiao, "Digital filter design for peak detection of surface EMG," J. of Electromyography and Kinesiology, vol. 10, 275-281, 2000. https://doi.org/10.1016/S1050-6411(00)00010-9
  11. JH. Blok, DF. Stegeman, AV. Oosterom, "Threelayer volume conductor model and software package for applications in surface electromyography," Ann. Biomed. Eng., vol. 30, 313-326, 2002.
  12. 이진, "골격근의 표면근전도 신호 발생 모델에 관한 연구," 삼척대학교 산업과학기술연구소 논문집, 제 9권, pp.73-81, 2004.
  13. A. Papoulis, Probability, random variables and stochastic processes, Mcgraw-Hill, NY, 1965.
  14. AJ. Fuglevand, DA. Winter, AE. Parla, D. Stashuk, "Detection of motor unit action potentials with surface electrodes: influence of electrode size and space," Biol. Cybernet., vol. 67, 143-153, 1992. https://doi.org/10.1007/BF00201021
  15. V. Oppenheim and RW. Schafer, Digital signal processing, Englewood Cliffs, NJ:Prentice-Hall, 1975.