Time-Frequency Analysis of EMG and the Application

1. Time-Frequency Analysis of EMG and the Application D99945004 林穎聰 Time-Frequency Analysis of Myoelectric Signals During Dynamic Contractions: A Comparative Study. Stefan Karlsson*, Jun Yu, and MetinAkay. P228-238. IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 47, NO. 2, FEBRUARY 2000

2. Skeletal Muscle Tissue Motor unit: a single motor neuron and all of the corresponding muscle fibers it innervates Two types by color: Red fibers: more myoglobin, slow contraction, small muscle force White fibers: fewer myoglobin, fast contraction, large muscle force

3. What is EMG? • Electromyogram (EMG): • the electrical potential generated by muscle cells when these cells are electrically or neurologically activated. • Application: • Detection of medical abnormalities, activation level, recruitment order. • The biomechanics analysis of human or animal movement. • Human–Computer Interface

4. How to Gather EMG Intramuscular EMG: A needle electrode or a needle containing two fine-wire electrodes is inserted through the skin into the muscle tissue. Surface EMG: Surface electrode over the skin.

5. STFT Gabor extended the applicability of Fourier transform method by dividing the input signal into segments. The signal in each window can be assumed to be stationary. Gabor transform:

6. WVD Since energy is a quadratic signal representation, a quadratic time-frequency representation such as the Wigner distribution can be used to represent it. pseudo Wigner-Ville distribution: h(τ): a regular window

7. CWD Choi-Williams Distribution: Running Windowed Exponential Distribution: The larger the value of σis, the better the autoterm resolution, otherwise the cross-term reduction will be worse. (σ:0.1~10)

8. Wavelet Transform Wavelet replaces the frequency shifting operation in STFT by a time scaling operation a: the scale parameter; b: translation parameter Larger a : useful for analysis the low-frequency components of the signal. • Smaller a : useful for analysis the high-frequency components of the signal.

9. Computer Synthesized Signals Linearly decreasing MNF Decreasing MNF from 160Hz to 72Hz with 100 steps in 3.2 secand 50 steps in 1.6 sec. • Burst Different time and frequency ranges with bandpass filter: • 72-160Hz with 512ms & 256ms 54-120Hz with 512ms & 256ms Sampling rates: 1k Hz

10. Surface EMG Signals Four healthy male volunteers • Maximum static voluntary contraction(MVC) • Ramp contraction (RC) • Repeated dynamic contractions until exhaustion (RDC) Vastuslaterials muscle Kin-Com 500H

11. Exam System Electrode Surface EMG(mV) MATLAB DAP 2400 with MYSAS Low pass: 800Hz High pass: 10Hz Signal processing toolbox Time-frequency toolbox Kim-com 500H Force (N) Velocity(Deg/sec) Sampling rates: 2k Hz

12. Estimates of Time-Frequency Analysis Mean frequencies (MNF) as the indicators of spectral changes are M1(t) Dispersion index Skewness index Kurtosis index Relative error: : Estimator of spectral change : indicator of spectral change : # of time points observed

13. Results- Computer Synthesized Signals 100 steps linearly decreasing MNF MNF(solid);Theoretical MNF(dotted) Mean Values (±1 std) of MNF Theoretical MNF(dotted)

14. Results- Linear Decreasing MNF A) Slop -22.97 (Hz/s) with 100 steps • B) Slop -46.40 (Hz/s) with 100 steps A B

15. Results- Burst #1:512ms, 72-160Hz • #2:512ms, 54-120Hz • #3:256ms, 72-160Hz • #4:256ms, 54-120Hz 512ms, 72-160Hz; MNF(solid);Theoretical MNF(dotted)

16. Results- MVC & RC with CWD MVC RC

17. Results- RDC with CWD Beginning Middle End

18. Results- MVC & RC with TFA MVC RC

19. Results- RDC with TFA Beginning Middle End

20. Discussion STFT had larger relative errors when estimating the MNF for stronger nonstationary signals (bursts) than those of the PWVD and RWED methods. PWVD method may overcome the problems associated with the cross-terms by removing them at the expense of smoothing the autoterms of the signal. CWT has been found to be very reliable in the analysis of nonstationary biological signals and does not require any smoothing function like the STFT, PWVD, and RWED

21. Discussion The increase in the MNF magnitudes are due to the recruitment of larger motor units. The MNF values are decreased during the isokinetic test probably due to the decrease in muscle fiber conduction velocity and the decrease in the force. The CWT method are more smoother than those obtained using the STFT, PWVD, and RWED methods.

22. Application Wavelet of EMG in medical detection provides more details on time-frequency domain. Study on Parkinson Disease patient Prosthetic device Speech recognition

23. Study on Parkinson Disease Patient • Effect of medication in Parkinson’s disease: a wavelet analysis of EMG signals. • S.K. Strambi, B. Rossi, G. De Michele, and S. Sello. Medical Engineering and Physics, vol. 26, pp. 279-290, 2004.

24. Prosthetic Device Classification of EMG signals through wavelet analysis and neural networks for controlling an active hand prosthesis. Matteo Arvetti, Giuseppina Gini, and Michele Folgheraiter. P531-536. 2007 IEEE 10th International Conference on Rehabilitation Robotics.

25. Speech Recognition EMG-based speech recognition using hidden Markov models with global control variables. K.S Lee. P531-566. IEEE Transactions on Biomedical Engineering, vol. 55, pp. 930-940, 2008.

26. Conclusion The CWT is a useful tool for the analysis of EMG signals in spite of the computational inefficiency. The discrete wavelet transform (DWT) may provide more efficiency than CWT and keep the advantage of CWT. The S transform with a drifting window may be used in time-frequency analysis of EMG.