**4.2. Influence of the burst and silence in treatment of EMG signal (Second Study)**

Computational Intelligence in Electromyography Analysis – 110 A Perspective on Current Applications and Future Challenges

> **SUBMAXIMAL (n=20) BLAND and ALTMAN TEST(μVolt) TREATMENTS ICC BIAS LD UD RF FRS** and **RF R** 0.927 -0.0030 -0.0120 0.0060 **RF FRS** and **RF F** 1.000 -0.0002 -0.0010 0.0006 **RF FRS** and **RF FS** 1.000 0.0000 0.0000 0.0000 **TREATMENTS RF** 0.980 -------- -------- -------- **VM\_FRS** and **VM\_R** 0.958 -0.0058 -0.0275 0.0154 **VM FRS** and **VM F** 1.000 -0.0008 -0.0023 0.0008 **VM FRS** and **VM FS** 1.000 0.0000 0.0000 0.0000 **TREATMENTS VM** 0.989 -------- -------- -------- **VL FRS** and **VL R** 0.830 -0.0050 -0.0247 0.0143 **VL FRS** and **VL F** 0.999 -0.0008 -0.0023 0.0008 **VL FRS** and **VL FS** 1.000 0.0000 0.0000 0.0000 **TREATMENTS VL** 0.959 -------- -------- --------

**MAXIMAL (n=20) BLAND and ALTMAN TEST (μVolt) TREATMENTS ICC BIAS LD UD RF FRS** and **RF R** 0.950 -0.0038 -0.0151 0.0074 **RF FRS** and **RF F** 1.000 -0.0006 -0.0018 0.0006 **RF FRS** and **RF FS** 1.000 0.0000 0.0000 0.0000 **TREATMENTS RF** 0.987 -------- -------- -------- **VM FRS** and **VM R** 0.994 -0.0039 -0.0075 -0.0004 **VM FRS** and **VM F** 0.998 -0.0022 -0.0047 0.0003 **VM FRS** and **VM FS** 1.000 0.0000 0.0000 0.0000 **TREATMENTS VM** 0,999 -------- -------- -------- **VL FRS** and **VL R** 0.969 -0.0070 -0.0209 0.0067 **VL FRS** and **VL F** 0.999 -0.0015 -0.0032 0.0003 **VL FRS** and **VL FS** 1.000 0.0000 0.0000 0.0000 **TREATMENTS VL** 0.992 -------- -------- -------- **SUPRAMAXIMAL (n=20) BLAND and ALTMAN TEST (μVolt)** 

**TREATMENTS ICC BIAS LD UD RF FRS** and **RF R** 0.970 -0.0022 -0.0063 0.0019 **RF FRS** and **RF F** 0.999 -0.0004 -0.0016 0.0008 **RF FRS** and **RF FS** 1.000 0.0000 0.0000 0.0000 **TREATMENTS RF** 0.993 -------- -------- -------- **VM FRS** and **VM R** 0.992 -0.0048 -0.0114 0.0016 **VM FRS** and **VM F** 0.999 -0.0020 -0.0042 0.0003 **VM FRS** and **VM FS** 1.000 0.0000 0.0000 0.0000 **TREATMENTS VM** 0.998 -------- -------- -------- **VL FRS** and **VL R** 0.992 -0.0039 -0.0087 0.0009 **VL FRS** and **VL F** 0.999 -0.0018 -0.0040 0.0004 **VL FRS** and **VL FS** 1.000 0.0000 0.0000 0.0000 **TREATMENTS VL** 0.998 -------- -------- --------

RF: Rectu Femoris; VM: Vastus Medialis; VL: Vastus Lateralis; FRS: Filtered, Rectified, Smooth; R: Raw; F: Filtered; FS:

**Table 3.** Intraclass Correlation Coefficient (ICC), Bias Level of treatment (BIAS) and Lower Dispersion

(LD) Upper Dispersion (UD) from BIAS in submaximal, maximal and supramaximal exercise.

Filtered, Smooth.

The figure 13 present us the RMS comparison between different kinds of analyze (all signal phase and contraction phase) respectively, among muscles: RF, VM and VL in the Wingate Test, no differences were found between methods (p>0.05).

**Figure 13.** Comparison of root mean square (RMS) between three different muscles from quadriceps femoris (RF = rectus femoris, VM = vastus lateralis, VL = vastus lateralis) in a Wingate Test (p>0.05).

**Figure 14.** Comparison of Median Frequency (MF) between three different muscles from quadriceps femoris (RF = rectus femoris, VM = vastus lateralis, VL = vastus lateralis) in a Wingate Test (p>0.05).

The figure 14 present us the MF comparison between different kinds of analyze (all signal phase and contraction phase) respectively, among muscles: RF, VM and VL in the Wingate test, no differences were found between methods (p>0.05).

Influence of Different Strategies of Treatment Muscle Contraction

and Relaxation Phases on EMG Signal Processing and Analysis During Cyclic Exercise 113

exist among several techniques to data process in EMG analysis and if we use a classical

Although, we should now take the conclusions and think in considerable applicability with our outcomes, we should remain our critical thinking about the theme, about our limitations and keep our considerations related just to our results in this study with these methods and these subjects. We expect that our findings encourage new experiences inside a positive vision in his complete trend to refute ours dogmas and explain in a better way how we should use and respect the recommendations and orientations to electromyography application in human studies, involving different kind of exercise in several intensities and oscillating between isometric and isotonic conditions, testing many aspects that stays around electromyography process and show to the scientific world a great amount of specificities to use this technique taking into account these variables capable to confuse and

change the signal with noises, underestimating or overestimating the final value.

ISEK - International Society of Electrophysiology and Kinesiology

SENIAM – Surface Electromyography for Non-Invasive Assessment of Muscles

Such design, as showed on this chapter should be applied to others tasks with the same characteristics, like running or any other cyclic exercises that has different muscles involved with different activation ranges and other kinds of possible noises, and silence and burst times. Also, different data process should be tested for spectral analyses, like Wavelet families and Fast Fourier Transform (FFT) for different exercises modalities, until it comes to conclusion about the correct use of this technique involving the correct results achievement,

statistic we may not identify with probabilities these modulations.

**6. Future directions** 

signal process and interpretation.

MIT – Maximal Incremental Test MWL – Maximal Work Load CLT – Constant Load Test Rpm – Revolution per Minute

FS – Filtrated and Smoothed signal

ICC – Interclass Correlation Coefficient

BIAS – Bias Level of Treatment

FSR – Signal Filtrated, Smoothed and Rectified

**7. Nomenclature** 

R- Raw signal F – Filtrated Signal

RF – Rectus Femoris VM – Vastus Medialis VL – Vastus Lateralis

EMG – Electromyography RMS – Root Mean Square

The results presented above show us that there were no significant difference between the two analyzes. Probably, these results were found because despite the silence moment has power gradient, the amount of lost energy is not enough to change the EMG signal parameters when the whole signal is analyzed. This result should not be transferred to others activity like the golf or to a martial art kick or punch for example due to the different characteristics, where in this sports the motor activity should be analyzed per complete, because during the whole time there is a contraction, so there is a signal amplitude [21-22].
