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

Geovani Rodrigo Scolaro, Fernando Mendes de Azevedo, Christine Fredel Boos and Roger Walz

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/53585

### **1. Introduction**

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Epilepsy is a chronic brain disease of unknown etiology, characterized by the occurrence of unprovoked epileptic seizures, causing brief disturbances in the normal activity of the brain [1]. In the epilepsy diagnosis process the visual analysis of Electroencephalographic signals (EEG) is still widely used by specialists. Its importance in diagnosis is due to the fact that you can get information about the patient's condition, recording the disturbance caused by epileptiform neuronal dysfunction, in the period that the patient is asymptomatic (without seizures) [2]. The EEG can also be used to help define the type of epileptic syndrome, provide information for planning of drug therapy and also to help in deciding the feasibility of surgery [3]. The most common elements in EEG signals of epileptic patients and important in the diagnosis of epilepsy are the spikes and sharp waves. The primary morphological difference between these two types of events is the duration that each presents. The spikes have durations between 40 and 80ms. The sharp waves have a duration between 80 and 200ms [3-4]. The process of reviewing the records of EEG is performed by trained and experienced specialists. However, this process is still a daunting task. The routine EEG records have durations between 20 and 40 minutes and are recorded from 21 to 32 channels, displayed in screen with 10 to 15 seconds each [3]. Few computer systems for automatic review of EEG have practical application. Many of these tend to identify a relatively large number of non-epileptiform events as positive, resulting in little or no effective economy of time [5]. Among the various non-epileptiform paroxysms, which generate more false positives in the automated detection of epileptiform events stand out

© 2012 Scolaro et al., licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2012 Scolaro et al., licensee InTech. This is a paper distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

high frequency noise, the alpha waves and especially the eyelid blinks (Figure 1). These patterns often occur in the EEG signals and they have characteristics similar to spikes and sharp waves being confused with epileptiform events.

Wavelet Filter to Attenuate the Background Activity and

(1)

(2)

High Frequencies in EEG Signals Applied in the Automatic Identification of Epileptiform Events 83

eliminate interferences caused by the power line. For the experiments were selected 600

The analysis in time-frequency domain by Wavelet Transform is performed by taking a Wavelet prototype function called Mother-Wavelet. This Mother-Wavelet suffers dilations

*t b <sup>t</sup> <sup>a</sup> <sup>a</sup>*

where *ψ(t)* is the Mother-Wavelet and *ψa,b* is the Daughter-Wavelet, *a-1/2* is the constant of

The Continuous Wavelet Transform uses continuous parameters of time and scales [6]. Using

 \* 0 0 0 0 <sup>1</sup> ,

where *k* and *i* are integers, *b0* and *a0* are the parameters of translation and dilation,

The Wavelet Multiresolution Analysis is based in the computational implementation of the Discrete Wavelet Transform. The algorithm decomposes a discrete signal using filter banks, [6-8]. The set of filters H[n] extract the average characteristics, defined as approximations of the signal *x* and added to a set of filters G[n] extract the features of high-frequency defined

*i i t kb a DWT a b x t dt a a* 

*i*

discrete parameters to *a* and *b* (*a*≥1, *b*≥1) determines the Discrete Wavelet Transform (2).

 

, <sup>1</sup> *a b*

energy normalization, *b* is the translation factor and *a* is the dilation factor.

events between spikes and sharp waves.

**2.2. Wavelet multiresolution analysis** 

respectively.

as details of the signal x[n] (Figure 2).

**Figure 2.** Representation of the Wavelet Multiresolution Analysis.

and translations, forming the Daughter-Wavelets (1) [6-7].


**Figure 1.** EEG screen with high frequency noise, eyelid blinks and epileptiform events (black arrow).

The low specificity of automated systems occurs due to variations in the EEG signals from patient to patient. This variation also occurs in the own patient due to different states of consciousness and behavior at the time of acquisition of these signals. Thus, it is difficult to establish a computational model of the epileptiform paroxysms, that can differentiate it from other activities present in the EEG.

The sensitivity of such systems can also be severely compromised by poor quality in the acquisition of an EEG signal. This is due to the large number of existing sources of artifacts. Despite this, most of the proposed automated systems fail to demonstrate they have achieved a rate of false positives per minute (FP / min) acceptable [5].

This work will contribute to the automating process of the epilepsy diagnosis with a digital filter proposal based on Wavelet Transform, checking its feasibility of use to process the EEG signals.
