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    i-EEG: A Software Tool for EEG Feature Extraction, Feature Selection and Classification Baha EN

    Computer Engineering Department, Yldrm Beyazt University, Ulus, Ankara, TURKEY

    Musa PEKER

    Computer Engineering Department, Karabuk University,78050, Karabuk, TURKEY

    Abdulkadir BUT

    Department of Anesthesiology and Reanimation, Yldrm Beyazt University, Cankaya, Ankara, TURKEY

    Abstract

    Computer-aided diagnosis of some brain disorders using EEG signals, is an issue of growing concern in recent years. In this study, a software which is developed from EEG data is presented for depth of anesthesia and diagnosis of disease. Developed software offers a different approach for the aspect of combining processes of data cleaning, feature extraction, feature selection and classification together. Software is developed using the C# programming language which is one of .NET programming languages. With the software, 4-stage procedures are performed on the EEG signal. For the first stage, filtering and cleaning of EEG data is ensured. For this stage, filtering algorithms are used. At second stage, attribute extraction process is done for determination of the significant information from EEG data. At this stage, 40 attribute values that widely preferred in literature can be obtained. At third stage (selection stage), attribute selection is carried out to ensure detection of effective attributes among all the attributes. At the selection stage, the user can select any algorithm that she/he wants among six different algorithms. These algorithms are fisher scoring, t-test, gini, fast correlation based filter (FCBF), minimum redundancy maximum relevance (mRMR) and reliefF, respectively. At final stage, effective attributes are presented as input to the classification algorithms. The user can choose five different classification algorithms for the classification stage. These algorithms are feed-forward neural network, radial basis function neural network, C4.5 decision tree, random forest algorithm and support vector machines, respectively. The software which has a visual interface is very easy to use and designed to be user-friendly. An application sample is given with using the software developed in this study. In the application sample, applications were made on EEG data in order to determine depth of anesthesia. Results can be obtained as statistical in this software. It also is possible to follow the performance results graphically. After user testing completed, it is aimed to share the software free of charge as open source software project.

    Keywords: Computer-aided diagnosis, EEG, software tool, feature extraction, feature selection, classification

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    1. Introduction

    EEG signals are received with the help of electrodes on the scalp and reflect the collective activity of brain cells (Sen & Peker 2013). In general, EEG composed of four main waveforms. These are alpha wave (8 ~ 13 Hz), beta wave (13 ~ 30 Hz), delta wave (0 ~ 4 Hz) and theta wave (4 ~ 7 Hz). These wave types in EEG signal vary in case of any pathology case. Sometimes it is difficult to identify the components of EEG sign showing instant change (Artan & Yazgan 2008, Peker & Sen 2013). EEG data are analyzed visually by a specialist in a clinic. This process is quite stressful and tiring for the eyes and the mind. EEG data were recorded for hours or even for days, are analyzed in the form of 10-second frames passing the computer screen (Artan & Yazgan 2008). EEG data were reviewed by specialists with different educational background, may lead to inconsistent data records (Kolb & Whishaw 2010). For all these reasons, a computer-aided system is of utmost importance to help the specialists. The use of computer-aided diagnosis systems is increasing in recent years for detection of neurological diseases and issues related to the depth of anesthesia. In this study, methods with successful results in the literature were examined for classification of EEG data and software which includes those methods was developed. Unlike studies in the literature, developed software can calculate 44 attribute value in five different categories. Among the main advantages of the software, it includes 6 different algorithms during attribute selection stage. Also doing some operations like cleaning the data, attributes acquisition, effective attribute selection and classification process under a single roof adds a special importance to the study. The software which has a visual interface is very easy to use and designed to be user-friendly.

    2. Data Acquisition

    The data used in this study were obtained from Yldrm Beyazt University Faculty of Medicine Hospital. In this study, 20 isoflurane anesthetic drug used cases were included. Duration of anesthesia and operations carried out are different. Patients' age ranged from 30 to 60. Characteristics of data group are as follows:

    Range Mean Standard Deviation Age 30-60 47.5 26.3

    Weight 45-88 69.5 13.9 Table 1: Characteristics of the data group

    Raw EEG signals were acquired using a BIS VISTA monitor (BIS VISTATM version 3.00, algorithm version BIS 4.1, Aspect Medical systems) with standard BIS sensors. Data were exported to a USB drive and transferred to a portable computer for off-line analysis. EEG signal was filtered between 0.1 - 60 Hz and digitized with sampling rate of 128 Hz. Anesthesia data belongs to the six different levels of anesthesia. Therefore, in this study, applications were conducted in order to determine a six-level depth of anesthesia. During the recordings, in order to compare with extracted attributes, BIS index was also recorded from the depth of anesthesia monitoring device. This device has indexing between 0-100 and wakefulness represents with

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    the value of 100. Classified anesthesia levels in this study and the corresponding levels of BIS values are presented in Table 2.

    Ane

    sthe

    tic

    Dep

    th L

    evel

    s Deep Anesthesia BIS Index: 0-25 6 BIS Index: 25-40 5

    Moderate Anesthesia BIS Index: 40-50 4 BIS Index: 50-60 3 Light Anesthesia BIS Index: 60-80 2

    Awake BIS Index: 80-100 1 Table 2. Scoring criteria state levels of anesthesia (Gifani et. al. 2007)

    3. EEG Signal Processing Software

    Recommended software was developed using C# and MATLAB programming languages. With the software, operations are carried out as 4-stage on the EEG signal. For the first stage, filtering and cleaning of EEG data is assured. At this stage, filtering algorithms are used. At second stage, feature extraction process is carried out. At this stage, 44 feature values can be obtained. At third stage, feature selection stage is carried out to ensure detection of effective feature among all the features. During the feature selection stage, user can select any algorithm that she/he wants among six different algorithms. These algorithms are fisher scoring, t-Test, gini, fast correlation based filter (FCBF), mRMR and reliefF, respectively. At last stage, effective attributes are presented as input to the classification algorithms. The user can choose five different classification algorithms during the classification stage. These algorithms are feed-forward neural network, C4.5 decision tree, random forest algorithm, support vector machines and radial basis neural network, respectively. An application example is given on developed software. In the application example, in order to determine the depth of anesthesia applications were made on EEG data. In this way, it is aimed to do better promotion for the software.

    3.1 The Interface of Developed Software

    The interface of developed software is as in Figure 1. As can be seen from the figure, all operations were assembled on a single window. The user can select any action from this window.

    Figure 1: Screen interface of developed system

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    3.2 Cleaning of EEG Data and Filtering

    MATLAB programming language were used during the preparation of EEG data. Operations carried out with MATLAB codes were integrated into the C # programming language. In the software, varieties of filters are available to reduce the impact of noise and artifacts in EEG signals. These filters are 0.1-60 Hz band-pass filters and the smoothing filter. Segmentation process is implemented at final stage in order to make operations easier on long EEG signals.

    Figure 2 shows the situation of original EEG signal after filtering and cleaning operations. Figure 3 shows the frequency spectrum of the original and the filtered signals. As can be seen in Figure 3, the original signal frequency varies between 0.1-250 Hz. Frequency distribution of the filtered signal varies between 0.1-55 Hz. Figure 4 shows the segmentation process. Long EEG signal is divided into segments with specific example values.

    Figure 2: EEG signal going through stages of filtering and smoothing filter between 0-55 Hz.

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    Figure 3: Frequency distribution of the EEG signal before filtering stage - frequency distribution of EEG signal after filtering stage

    Figure 4: Division of EEG signal sample into 30 s epochs

    3.3 Feature Selection

    The software window developed for the feature selection phase can be seen in Figure 5. Data type can be selected with this software. Feature selection algorithm is selected from method selection section. As shown in Figure 5, there are 6 different feature selection algorithm. Furthermore,