**3. Methodology**

#### **3.1. Influence of the treatments (First study)**

The Signal processing in EMG is a complex matter to adopt in determined studies, in several times the signal process used is based on the mainly recommendations and the needs of researcher, but sometimes the instructions are not so clear and are not based in studies that contemplate the new tendencies in contemporary researches. However, the main objective of this chapter is to show the great possibility of using the different treatments to find the same outcome of an EMG signal, using many combinations of process (filtration, rectification and smoothing) in a variable of time domain (RMS) and discover if bursts are capable to interfere in the final result of a dynamic exercise in high intensity that is more capable to induce great noises. In that way, we keep our efforts in test these intriguing questions about the signal processing in EMG with a considerable method to involve the main exercise capable of producing the high amount of noises in the signal and test in this sequence the differences in use several proceedings in dynamic exercise (cycling). To introduce this perspective we assessed in a first period 20 men (27,5 ± 4,1 years old; 83,1 ± 8,2 kg; 184,5 ± 4,5 cm), healthy and active physically.

Briefly the subjects passed for a session of familiarization in the protocols and the instruments of the test, basically to know the cycle simulator and find/keep adjusts in bench and foot pedals. In the next step the men did a maximal incremental test (MIT) until exhaustion to determine the maximal work load (MWL). The information obtained in MIT was used to find the intensity of effort in constant load tests (CLT) in three different intensities in severe domain: 80% MWL, 100% MWL and 110% MWL, see figure 7 for better understanding. The different intensities in severe domain were chosen with the intention to allow us to make affirmations including all domains. Each subject was tested in the same hour of day to minimize the effects of the circadian variations.

Computational Intelligence in Electromyography Analysis – 104 A Perspective on Current Applications and Future Challenges

**Figure 5.** An EMG signal divided in 1 second time windows.

**Figure 6.** The same EMG signal of Figure 5, now divided in 5 seconds windows.

don`t accept too much windows to process.

**3.1. Influence of the treatments (First study)** 

8,2 kg; 184,5 ± 4,5 cm), healthy and active physically.

**3. Methodology** 

In the same signal, the next image has the cuts made in five seconds windows (Figure 6). The biggest importance about using a bigger window is not just because it would be hard for the researcher to divide the signal, but also because some routines that treat the signal

The Signal processing in EMG is a complex matter to adopt in determined studies, in several times the signal process used is based on the mainly recommendations and the needs of researcher, but sometimes the instructions are not so clear and are not based in studies that contemplate the new tendencies in contemporary researches. However, the main objective of this chapter is to show the great possibility of using the different treatments to find the same outcome of an EMG signal, using many combinations of process (filtration, rectification and smoothing) in a variable of time domain (RMS) and discover if bursts are capable to interfere in the final result of a dynamic exercise in high intensity that is more capable to induce great noises. In that way, we keep our efforts in test these intriguing questions about the signal processing in EMG with a considerable method to involve the main exercise capable of producing the high amount of noises in the signal and test in this sequence the differences in use several proceedings in dynamic exercise (cycling). To introduce this perspective we assessed in a first period 20 men (27,5 ± 4,1 years old; 83,1 ±

Briefly the subjects passed for a session of familiarization in the protocols and the instruments of the test, basically to know the cycle simulator and find/keep adjusts in bench and foot pedals. In the next step the men did a maximal incremental test (MIT) until exhaustion to determine the maximal work load (MWL). The information obtained in MIT was used to find the intensity of effort in constant load tests (CLT) in three different intensities in severe domain: 80% MWL, 100% MWL and 110% MWL, see figure 7 for better

**Figure 7.** Illustrative representation of the first study, involving signal treatments for RMS obtainment.

Initially it was realized the MWL with initial load in 100W and 20W of increments each minute until voluntary exhaustion, remain a cadence of 70 revolutions per minute (rpm). The MWL was preceded of a warm-up with a load of 50W, with a period of three minutes, follow by three minutes in rest. The MWL was defined as a higher work load maintained for 30 seconds at least, this was assumed so we could make sure to achieve the MWL and not the peak load.

From the information obtained in the MWL, the subjects were oriented to realize three constant load tests (CLT) in different intensities, these being: submaximal (80%MWL), maximal (100%) and supramaximal (110%). Every test was realized in a cyclesimulator (Computrainer™, Racer Mate®, USA). The tests occur with at least 48 hours between then. The CLT was preceded of three minutes of warm-up with 50W, followed by three minutes of rest. After that the tests occur until exhaustion. The subjects were instructed to keep their cadence in 90 rpm, could not pedal less than that, and the test was interrupted when the subjects reported voluntary exhaustion or showed inability to keep the cadence stipulated on the test. The verbal encouragement was used.

The EMG signal was obtained during all period of realization in CLT using an electromyography with 16 channels, model MP150™ (Biopac System®, USA) with a sampling rate of 2000 samples/second, in agreement with ISEK [15]. Before the beginning of each CLT, the subjects were submitted to asepsis and curettage. The electrodes used were active and bipolar, model TSD 150™ (BIOPAC Systems®, USA), with distance among electrodes fixed in two centimeters, putted above superficial muscles of quadriceps femoral of right leg: vastus lateralis (VL), vastus medialis (VM) and rectus femoris (RF), following the standard of SENIAM [12], as showed by the white circles on the figure 8.

Influence of Different Strategies of Treatment Muscle Contraction

and Relaxation Phases on EMG Signal Processing and Analysis During Cyclic Exercise 107

**3.2. Influence of the burst and silence in treatment of EMG signal (Second** 

**Figure 9.** Illustrative representation of second study protocol, involving burst analyze.

The table 2 present a descriptive analyze referent of subject performance.

Variables **Men**

Note: relative peak power (RPP), relative mean power (RMP), and fatigue index (FI).

**Table 2.** Mean values ± standard deviation of subject performance.

The protocol consisted of 4 minutes warm-up in a mechanic cycle ergometer to lower limbs (MONARK 324E, SWEDEN) with 50 W load, with a pedal cadence in 70 rpm and the beginning of each minute the subjects realized a sprint during 6 seconds. After warm-up, the subjects rest for two minutes and they began the test, with a 0,075 kg.kg-1 load until finish the test in 30 seconds. The same muscles were analyzed with the same EMG protocol and the same equipment's and procedures in the previous study. However, for this study in addition to the RMS also analyzed spectral parameters. To spectral analyses or frequency domain, was obtained the parameters from median frequency (MF), variance and slope, those values were determined using Wavelet Daubechies db4 (DWT) [6,8]. Was considered the analyses of EMG signal in the contraction phase (bursts) and during all signal (bursts +

**n=14** 

**RPP (W.kg-1)** 10.0 ± 0.9 7.7 ± 0,9 **RMP (W.kg-1)** 7.3 ± 0.5 5.6 ± 0.6 **FI (%)** 52.9 ± 9,0 51.1 ± 11.9

**Women n=13** 

To test the possibility of bursts get in the way of an EMG signal and change the final outcome, we used a similar method, assessing 27 healthy students (14 men, age = 28,2 ± 2,7 years and 13 women, age = 23,2 ± 2,7 years). The test proposed was the Wingate supramaximal test (WST) used with a purpose to reach a higher intensity in exercise matched with a short duration. The index of performance was defined in a software (WINGATE TEST®, CEFISE, BRASIL) to determine the power by each second during the test, beyond the relative peak power (RPP) (W.kg-1), relative mean power (RMP) (W.kg-1), fatigue index (FI) (%) and the peak power instant (PPI). The figure 9 represents the second

**Study)** 

study protocol.

silence).

**Figure 8.** Electrode position used; SENIAM recommendations [12].

The relation of rejection common mode was >95dB and the limits of entrance of established signal in ± 5 mV. The reference electrode was positioned in the right elbow (lateral epicondyle). To capture and process the signal was used the software AcqKnowledge 3.8.1™ (BIOPAC Systems®, USA) and the software MatLab 7.0 (Mathworks®, South Natick, MA, USA).

The EMG signal was treated to obtain the RMS (root mean square) values in time windows with five seconds in the first minute of the test in different intensities. The first twenty seconds of each signal were discarded with the intention to avoid possible inertial influences. After that, it was used proceedings recommended to exclude artifacts and noises from EMG signal, divided in conditions: raw (R), Filtration (F), Filtration + smoothing (FS), filtration + smoothing + rectification (FSR). The filtration was done using a pass-band digital filter Butterworth with frequencies of 20 and 500 Hz. The smoothing process was done through a mobile mean with three points. The process of rectification was done considering all signals, without discards of negative part. The table 1 present the mean values of the load used in the constant load test in 80, 100 and 110% of MWL and the respective times to exhaustion.


Note: different letters show significant differences between loads and times to exhaustion, (*P*<0.05).

**Table 1.** Loads and times to exhaustion (mean and standard deviation) on constant load tests in 80, 100 and 110% of MWL.
