**4.2. Evaluation of the proposed filter**

In this section will be presented screen of the original EEG signals containing baseline fluctuations, high-frequency noise, low frequency oscillations and epileptiform events. All the signals were filtered using the function Db4.

#### *4.2.1. Experiments with the proposed filter applied in EEG Signals*

Figure 13 presents a segment of EEG signal contaminated by muscle artifacts. Can be observed the low frequency fluctuations present in the original signal were attenuated in the filtered signal. The high frequencies generated by the muscles suffer little attenuation in relation to the original signal leaving only frequencies below 32 Hz.

**Figure 13.** EEG signal containing muscle artefacts.

Figure 14 shows a segment of the EEG signal which has large fluctuations of base line. It can be observed that the proposed filter attenuated the baseline oscillations and small oscillations of low frequencies apparent at the beginning of the original signal were also attenuated.

**Figure 14.** EEG signal containing base line oscillations.

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RMSE for a epoch of 1 sec.

correlations and RMSE.

**4.2. Evaluation of the proposed filter** 

the signals were filtered using the function Db4.

**Figure 13.** EEG signal containing muscle artefacts.

*4.2.1. Experiments with the proposed filter applied in EEG Signals* 

relation to the original signal leaving only frequencies below 32 Hz.

The experiments showed the function DB4 proved to be the best choice for use with the filter, according to the values of correlation and RMSE. This function is indicated when there is the need to maintain the morphology of the whole epoch of the filtered signal. When there is the need to preserve only the peaks of the filtered signal the RBio2.8 and Coif4 are the Wavelet functions more suitable for this task. If is only necessary to attenuate the normal background activity of the EEG, without concern for preserving the morphology of the epoch the Wavelet function Bior3.1 is the most appropriate to do this. Because this particular function have few coefficients, their use in filtering the EEG signals offers superior performance to other functions presented. However, this function shows greater distortion of the signal after filtering than the others. Table 8 shows the values of correlation and

Wavelet function Coeffs. Energy [%] Correlation epochs 1 sec RMSE epochs 1 sec

In this section will be presented screen of the original EEG signals containing baseline fluctuations, high-frequency noise, low frequency oscillations and epileptiform events. All

Figure 13 presents a segment of EEG signal contaminated by muscle artifacts. Can be observed the low frequency fluctuations present in the original signal were attenuated in the filtered signal. The high frequencies generated by the muscles suffer little attenuation in

db4 8 48.30 0.717658 36.799603 coif4 24 74.03 0.690788 38.034667 rbio2.8 18 75.51 0.666828 39.029143 bior3.1 4 89.57 0.666274 39.463159 **Table 8.** Wavelet functions obtained from the experiments performed with the calculation of the Figure 15 presents a segment of the EEG signal containing high frequency noise and the filtered signal by Wavelet filter.

**Figure 15.** EEG signal containing noise of high frequency.

Figure 16 presents a segment of EEG containing epileptiform events and small lowfrequency oscillations. Can be observed that the low-frequency oscillations were attenuated highlighting the epileptiform events.

**Figure 16.** EEG signal containing some epileptiform events.

#### *4.2.2. Experiments with the proposed filter applied in screens of EEG signals*

In the previous section have been shown figures containing segments of EEG signals, which were filtered by the proposed filter. This section presents individual screens of EEG signals containing the most different conditions found in the used records. These screens show noise of high and low frequencies, fluctuations of base line and some screens with epileptiform events. The screens were processed with the Wavelet function Db4, which was chosen for the proposed filter. Figure 17 presents a plot of EEG containing large fluctuations of base line. At the top right there is the presence of some epileptiform events, featuring a field of potential.

Wavelet Filter to Attenuate the Background Activity and

High Frequencies in EEG Signals Applied in the Automatic Identification of Epileptiform Events 97

**Figure 18.** Screen of EEG signals filtered by proposed filter.

**Figure 19.** Screen of EEG signals containing noise of low and high frequencies.

some frequencies below 32 Hz remained in the processed signal.

Through the Figure 20 can be seen that the low frequency oscillations were attenuated, but

frequency noise.

Figure 19 presents a plot of EEG containing many low-frequency oscillations, as well as high

It can be observed that there was attenuation in the fluctuations of base line and other oscillations present in the original signal, highlighting the epileptiform events (Figure 18).

**Figure 17.** EEG screen containing large base line fluctuations and some epileptiform events.

#### Wavelet Filter to Attenuate the Background Activity and High Frequencies in EEG Signals Applied in the Automatic Identification of Epileptiform Events 97


**Figure 18.** Screen of EEG signals filtered by proposed filter.

96 Practical Applications in Biomedical Engineering

field of potential.

*4.2.2. Experiments with the proposed filter applied in screens of EEG signals* 

In the previous section have been shown figures containing segments of EEG signals, which were filtered by the proposed filter. This section presents individual screens of EEG signals containing the most different conditions found in the used records. These screens show noise of high and low frequencies, fluctuations of base line and some screens with epileptiform events. The screens were processed with the Wavelet function Db4, which was chosen for the proposed filter. Figure 17 presents a plot of EEG containing large fluctuations of base line. At the top right there is the presence of some epileptiform events, featuring a

It can be observed that there was attenuation in the fluctuations of base line and other oscillations present in the original signal, highlighting the epileptiform events (Figure 18).

**Figure 17.** EEG screen containing large base line fluctuations and some epileptiform events.

Figure 19 presents a plot of EEG containing many low-frequency oscillations, as well as high frequency noise.

**Figure 19.** Screen of EEG signals containing noise of low and high frequencies.

Through the Figure 20 can be seen that the low frequency oscillations were attenuated, but some frequencies below 32 Hz remained in the processed signal.

Wavelet Filter to Attenuate the Background Activity and

High Frequencies in EEG Signals Applied in the Automatic Identification of Epileptiform Events 99

**Figure 22.** Screen of EEG signals filtered by proposed filter.

and at the center of the screen some epileptiform events.

epileptiform events.

Figure 23 presents a screen containing blinks, muscle artifacts, noise and high frequencies,

**Figure 23.** Screen containing blinks, muscle artefacts, high frequencies and at the centre of the screen

In Figure 24 it can be seen that the predominant blinks in the front channels and the muscle

artefacts were attenuated and the epileptiform events were highlighted.

**Figure 20.** Screen of EEG signals filtered by proposed filter.

The Figure 21 presents a screen containing epileptiform events in two different periods of time. Can be observed small low frequency oscillations in the higher channels that were attenuated.

**Figure 21.** Screen of EEG signals containing epileptiform events in different periods of time.

In Figure 22 can be seen that the epileptiform events are more prominent in EEG signals, due to the fact the filter have attenuated all oscillations that were present in the signal.

#### Wavelet Filter to Attenuate the Background Activity and High Frequencies in EEG Signals Applied in the Automatic Identification of Epileptiform Events 99


**Figure 22.** Screen of EEG signals filtered by proposed filter.

98 Practical Applications in Biomedical Engineering

**Figure 20.** Screen of EEG signals filtered by proposed filter.

attenuated.

The Figure 21 presents a screen containing epileptiform events in two different periods of time. Can be observed small low frequency oscillations in the higher channels that were

**Figure 21.** Screen of EEG signals containing epileptiform events in different periods of time.

In Figure 22 can be seen that the epileptiform events are more prominent in EEG signals, due to the fact the filter have attenuated all oscillations that were present in the signal.

Figure 23 presents a screen containing blinks, muscle artifacts, noise and high frequencies, and at the center of the screen some epileptiform events.

**Figure 23.** Screen containing blinks, muscle artefacts, high frequencies and at the centre of the screen epileptiform events.

In Figure 24 it can be seen that the predominant blinks in the front channels and the muscle artefacts were attenuated and the epileptiform events were highlighted.


Wavelet Filter to Attenuate the Background Activity and

High Frequencies in EEG Signals Applied in the Automatic Identification of Epileptiform Events 101

This work presented a study about the capability of the Wavelet Transform to be used to develop a digital filter to attenuate the background activity in the EEG signals. Four Wavelet functions were select from 65 evaluated. According to the experiments the Wavelet function Db4 proved to be the best function for the development of a digital filter in this application, according to researches performed by [9-12]. The function Db4 is indicated when there is the need to preserve the epoch of the filtered signal more like the original epoch. When there is the need to preserve the peaks of the epileptiform events the adequate function is the Rbio2.8. If the need is only to attenuate the background activity without the concern of preserves the morphology of the peaks in the epileptiform events the adequate function is the Bior3.1. Due to the fact of this function have reduced number of coefficients its

**Figure 26.** EEG screen processed by the proposed filter.

application improves performance in filtering process.

Practice. São Paulo: Lemos Editorial; 2001.

Geovani Rodrigo Scolaro, Fernando Mendes de Azevedo, Christine Fredel Boos

[1] Blum, A.S., Rutkove, S.B. The Clinical Neurophysiology Primer. Totowa, New Jersey:

[2] Montenegro, M.A., Cendes, F., Guerreiro, M.M., Guerreiro, C.A. EEG in Clinical

*Institute of Biomedical Engineering, Federal University of Santa Catarina, Brazil* 

**5. Conclusion** 

**Author details** 

and Roger Walz

**6. References** 

Humana Press Inc; 2007.

**Figure 24.** Screen of EEG signals filtered by proposed filter.

The Figure 25 presents a EEG screen contaminated by noise of 60 Hz, eyelid blink, some oscillations and at the center of the screen epileptiform events (black arrow) obviously masked due to noise.

**Figure 25.** EEG screen containing noise of 60 Hz and epileptiform events masked by the noise.

In Figure 26 we can observe that the noise from the power supply has been attenuated, as well as the oscillations and eyelid blinks, showing the epileptiform events that previously did not appear.

#### Wavelet Filter to Attenuate the Background Activity and High Frequencies in EEG Signals Applied in the Automatic Identification of Epileptiform Events 101


**Figure 26.** EEG screen processed by the proposed filter.
