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(IJCSIS) International Journal of Computer Science and Information Security, Vol. 8, No. 6, September 2010 Prediction of Epileptic form Activity in Brain Electroencephalogram Waves using Support vector machine 1 2 Pavithra Devi S T Vijaya M S M.Phil Research Scholar Assistant Professor and Head PSGR Krishnammal College for Women GRG School of Applied Computer Coimbatore Tamilnadu, INDIA Technology pavikrishnamouse@gmail.com PSGR Krishnammal College for Women Coimbatore Tamilnadu, INDIA msvijaya@grgsact.com ABSTRACT hearing things that are not there, or having a sudden flood Human brain is a highly complex structure composed of of emotions. Complex partial epilepsy usually starts in a millions of nerve cells. Their activity is usually well organized small area of the temporal lobe or frontal lobe of the brain. with mechanisms for self-regulation. The neurons are In general epilepsy the patient becomes unconscious the responsible for a range of functions, including consciousness patient has a general tonic contraction of all their muscles, and bodily functions and postures. A sudden temporary followed by alternating colonic contractions. It affects the interruption in some or all of these functions is called a entire brain. seizure. Epilepsy is a brain disorder that causes people to have recurring seizures. Electroencephalogram (EEG) is an Various diagnostic techniques like Computed important diagnostic test for diagnosing epilepsy because it Tomography (CT), Magnetic Resonance Imaging (MRI), records the electrical activity of the brain. This paper Electroencephalogram (EEG), and Positron Emission investigates the modeling of epilepsy prediction using Support Tomography (PET) are commonly presented. Vector Machine, a supervised learning algorithm. The Electroencephalography (EEG) is the recording of prediction model has been employed by training support electrical activity along the scalp produced by the firing of vector machine with evocative features derived from EEG neurons within the brain. In clinical contexts, EEG refers to data of 324 patients and from the experimental results it is the recording of the brain's spontaneous electrical activity observed that the SVM model with RBF kernel produces 86% over a short period of time, usually 20–40 minutes, as of accuracy in predicting epilepsy in human brain. recorded from multiple electrodes placed on the scalp. The Keywords Electroencephalograph (EEG) signal is one of the most Support Vector Machine, Epilepsy, Prediction, Supervised widely signal used in the bioinformatics field due to its rich Learning. information about human tasks for epilepsy identification because of its characteristics like frequency range, spatial 1. INTRODUCTION distributions and peak frequency. EEG waves are observed Epilepsy is a disorder characterized by recurrent by neurologists based on spectra waveform of the signal to seizures of cerebral origin, presenting with episodes of identify the presence of epilepsy. sensory, motor or autonomic phenomenon with or without Machine learning provides methods, techniques and loss of consciousness. Epilepsy is a disorder of the central tools, which help to learn automatically and to make nervous system, specifically the brain [1]. Brain is one of accurate predictions based on past observations. Current the most vital organs of humans, controlling the empirical results prove that machine learning approach is coordination of human muscles and nerves. Epileptic well-matched for analyzing medical data and machine seizures typically lead to an assortment of temporal learning techniques produce promising research results to changes in perception and behavior. Based on the medical domains. physiological characteristics of epilepsy and the abnormality in the brain, the kind of epilepsy is determined. Forrest Sheng Bao carried out the work and developed Epilepsy is broadly classified into absence epilepsy, simple a neural network based model for Epilepsy diagnosis using partial, complex partial and general epilepsy. Absence EEG [1]. Piotr Mirowski carried out the work and epilepsy is a brief episode of staring. It usually begins implemented a model based on classification of patterns of between ages 4 and 14. It may also continue to adolescence EEG synchronization for seizure prediction using neural or even adulthood. Simple partial epilepsy affects only a network [2]. Suleiman A.B. R. proposed a new approach small region of the brain, often the hippocampus. It can for describing and classifying the EEG brain natural also include sensory disturbances, such as smelling or oscillations such as delta, theta, alpha, and beta frequencies 116 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 8, No. 6, September 2010 using Wigner-Ville analysis with Choi-Willians filtering mental activity. Their amplitude is highest in the and neural network [3]. occipital region. When the person is asleep, the alpha waves disappear. When the person is alert The motivation behind the research reported in this and their attention is directed to a specific activity, paper is to predict the presence of epilepsy in human brain. the alpha waves are replaced by asynchronous Supervised learning technique, a kind of machine learning waves of higher frequency and lower amplitude. algorithm is used to model the epilepsy prediction problem as classification task to assist physician for accurate Beta waves have a frequency range of 14 prediction of epilepsy in patients. to 22 Hz, extending to 50 Hz under intense mental activity. It has their maximum amplitude (less than In this paper, the prospective benefits of supervised 20 mV) on the parietal and frontal regions of the learning algorithm namely support vector machine are scalp. There are two types: beta I waves, lower made use of for the computerized prediction of epilepsy. frequencies which disappear during mental The proposed SVM based epilepsy prediction model is activity, and beta II waves, higher frequencies shown in Figure 1. which appear during tension and intense mental activity. Gamma waves have frequencies between Feature Extraction using 22 and 30 Hz with amplitude of less than 2 mV Wavelet Toolbox in MATLAB peak-to-peak and are found when the subject is paying attention or is having some other sensory stimulation. Theta waves have a frequency range between 4 to 7 Hz with amplitude of less than100 mV. It occurs mainly in the parietal and temporal SVM Training regions in sleep and also in children when awake, and during emotional stress in some adults, particularly during disappointment and frustration. Sudden removal of something causing pleasure will cause about 20 s of theta waves. SVM Based Prediction model Delta waves have frequency content between 0.5 and 4 Hz with an amplitude less than 100 mV. It occurs during deep sleep, during infancy and in serious organic brain disease. They will occur after transactions of the upper brain stem separating the reticular activating system Prediction from the cerebral cortex. They are found in the central cerebrum, mostly the parietal lobes. Five sets of images namely Normal Epilepsy, Absence Epilepsy, Simple Partial Epilepsy, Complex Partial Figure 1.Proposed SVM based epilepsy prediction model Epilepsy and General Epilepsy are taken into consideration. 2. DATA ACQUISITION EEGs show continuous oscillating electric activity. The amplitude and the patterns are determined by the overall 3. FEATURE EXTRACTION excitation of the brain which in turn depends on the activity Feature extraction process plays a very important role of the reticular activating system in the brain stem. on the classification. Fourier transformation method, Amplitudes on the surface of the brain can be up to 10 mV, discrete transformation method and continuous those on the surface of the scalp range up to 100 mV. transformation methods are normally available to extract Frequencies range from 0.5 to 100 Hz. The pattern changes features that characterize EEG signals. The wavelet markedly between states of sleep and wakefulness. Distinct transform (WT) provides very general techniques which patterns are seen in epilepsy and five classes of wave can be applied to many tasks in signal processing. Wavelets groups are described as alpha, beta, gamma, delta and are ideally suited for the analysis of sudden short-duration theta. signal changes. Alpha waves contain frequencies In the proposed model, EEG signal analysis and feature between 8 and 13 Hz with amplitude less than 10 extraction have been performed using Discrete Wavelet mV. It found in normal people who are awake and Transform (DWT). The DWT is a extraordinary case of the resting quietly, not being engaged in intense WT that provides a compact representation of a signal in time and frequency that can be computed efficiently. 117 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 8, No. 6, September 2010 The DWT is defined by the following equation: The energy is computed using E is given by n Ψ (a,b) (t) = 2 a/2 ψ (2 a/2 (t-b)) (1) E=∑xi2/N (3) i=1 where a is a scales and b is positions of the wavelet mother ψ (t) is a time function with finite energy. Choosing where xi is signal value, values are present in waves is scales and positions are based on powers of two, which are denoted as n. Total number of signal is N called dyadic scales and positions (a j=2 –j; bj,k=2j k) ( j and k integers). Equation (1) shows that it is possible to build a wavelet for any function by dilating a function ψ (t) with a Maximum Subband – It generate maximum of the coefficient 2 j, and translating the resulting function on a wavelet coefficients in each subband is calculated using grid whose interval is proportional to 2−j. The selection of appropriate wavelet and the number of Max=Max(xi) (4) decomposition levels is very important in analysis of signals using the WT. The number of decomposition levels where max (xi) is maximum number of signal value. is chosen based on the dominant frequency components of the signal. The levels are chosen such that those parts of the signal that correlate well with the frequencies required for Mean – It is defined as average value of a distribution classification of the signal are retained in the wavelet of the wavelet coefficients in each subband which is given coefficients. The smoothing feature of the Daubechies by wavelet of order 2 (db2) made it more suitable to detect changes of the signals. Thus, the wavelet coefficients are n computed using db2. The frequency bands corresponding to different levels of decomposition for db2 with a E=∑xi/N (5) sampling frequency of 256 Hz. The discrete wavelet i=1 coefficients are computed using the MATLAB wavelet where xi is signal and total number of signal is present toolbox. in the wavelet is N The purpose of feature extraction is to reduce the size of the original dataset by measuring certain properties or Minimum Subband – calculate minimum of the features that distinguish one input pattern from another. wavelet coefficients in each subband is defined as The various measurements based on statistical features from EEG are extracted. The extracted features provide the characteristics of the input type to the classifier by Min=Min(xi) (6) considering the description of the relevant properties of the signals into a feature space. Where min (xi) is minimum number of signal value. The statistical feature of the wavelet coefficients in each subband such as energy, entropy, Minimum subband, Standard deviation - standard deviation of each maximum subband, mean, and standard deviation are used subband is defined as σ .This feature provide information to investigate the adequacy for the discrimination of normal about the amount of change of the frequency distribution. and abnormal patients. The following statistical features have been derived using the following. Entropy is the diminished capacity for spontaneous n changes in signals. σ=∑(x-µ)2 (7) i=1 Entropy = P(i, j ) log P(i, j ) i, j (2) where ∑ is sum of squared elements in the wavelet,x is signal value and µ is a mean of the corresponding Where P(i, j) reflects the distribution of the probability signal(xi). of occurrence of each signal (i , j are integer). Thus a total of 21 statistical feature are extracted from EEG signal for each subband for preparing dataset. Energy – Provides the sum of squared elements in the wavelet. This is also known as uniformity or the angular second moment. 118 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 8, No. 6, September 2010 4. SUPPORT VECTOR MACHINE dimension of the real feature space can be very high or Support Vector Machine (SVM) is a kind of learning even infinite. The parameters are obtained by solving the machine based on statistical learning theory. SVM is following non linear SVM formulation (in Matrix form), basically applied to model pattern classification task. SVM first, maps the input vectors into feature vectors in feature space with a higher dimension, either linearly or non- Minimize LD(u)=1/2uT Qu - eT u (9) linearly. Then, within the feature space SVM constructs a hyperplane which separates two classes. SVM training dTu=0 0≤u≤Ce always seeks a global optimized solution and avoids over- fitting, thus it has the ability to deal with a large number of where and K - the Kernel Matrix. Q = DKD. features. The machine is presented with a set of training examples, (xi, yi) where the xi is the real world data The Kernel Function K (AAT) (polynomial or instances and the yi are the labels indicating which class the Gaussian) is used to construct hyperplane in the feature instance belongs to. For the two class pattern recognition space, which separates two classes linearly, by performing problem, yi = +1 or yi = -1. A training example (xi, yi) is computations in the input space. called positive if yi = +1 and negative otherwise. SVMs construct a hyperplane that separates two classes and tries f(x)= sgn(K(x,xiT)*u-γ) to achieve maximum separation between the classes. Separating the classes with a large margin minimizes a Where u - the Lagrangian multipliers. In general the bound on the expected generalization error. larger the margin the lower the generalization error of the The simplest model of SVM called Maximal Margin classifier. classifier, constructs a linear separator (an optimal hyperplane) given by w T x - y= 0 between two classes of 5. EXPERIMENTAL SETUP examples. The free parameters are a vector of weights w The data investigation and epilepsy prediction is carried which is orthogonal to the hyper plane and a threshold out using SVMlight1 for machine learning. Five categories value. These parameters are obtained by solving the of feature vectors are labeled as 1 for Absence, 2 for following optimization problem using Lagrangian duality. General, 3 for Complex Partial Epilepsy, 4 for Normal Epilepsy and 5 for Simple Partial Epilepsy, The training 1 2 dataset used for epilepsy prediction modeling consists of Minimize = w 2 about 324 images, where each category consists of about w x 65. Subject to D ii i 1, i 1,......, l. (8) The dataset has been trained using SVM with linear, polynomial and RBF kernel and with different parameter settings for d, gamma and C–regularization parameter. The where Dii corresponds to class labels +1 and -1. The parameters d and gamma are related with polynomial instances with non-null weights are called support vectors. kernel and RBF kernel respectively. In the presence of outliers and wrongly classified training examples it may be useful to allow some training errors in The 10 fold cross validation method is used for order to avoid over fitting. A vector of slack variables ξi evaluating the performance of the SVM based trained that measure the amount of violation of the constraints is models. The performance of the models is evaluated based introduced and the optimization problem referred to as soft on prediction accuracy of the models and learning time. margin is given below. In this formulation the contribution 6. RESULTS AND DISCUSSION to the objective function of margin maximization and training errors can be balanced through the use of The cross validation outcome of the trained models regularization parameter C. based on support vector machine with linear kernel is shown Table I. The following decision rule is used to correctly predict the class of new instance with a minimum error. Table I. SVM Linear kernel Linear SVM C=0.1 C=0.2 C=0.3 C=0.4 f(x)= sgn[wtx-γ] Accuracy (%) 70 72 76 78 The advantage of the dual formulation is that it permits an efficient learning of non–linear SVM separators, by Time(secs) 0.01 0.02 0.02 0.03 introducing kernel functions. Technically, a kernel function calculates a dot product between two vectors that have been (non- linearly) mapped into a high dimensional 1 feature space. Since there is no need to perform this SVMlight is an open source tool. mapping explicitly, the training is still feasible although the http://www.cs.cornell.edu/people/tj/svm_light/ 119 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 8, No. 6, September 2010 The outcome of the model based on SVM with polynomial kernel and with parameters d and C are shown Learning Time in Table II. 0.9 0.9 0.8 0.8 Table II. SVM Polynomial kernel 0.7 0.6 C=0.1 C=0.2 C=0.3 C=0.4 0.5 Learning d 1 2 1 2 1 2 1 2 0.4 Time(secs) 0.3 Accuracy (%) 70 80 82 80 80 81 74 75 0.2 0.1 0.056 Time(secs) 0.2 0.1 0.2 0.6 0.3 0.1 0.3 0.4 0 Linear Polynomial RBF The predictive accuracy of the non-linear support Figure 3: Learning Time vector machine with the parameter gamma (g) of RBF kernel and the regularization parameter C is shown in Table III. As far as the epilepsy predictions task is anxious, accuracy plays major role in determining the performance of the epilepsy trained model than considering the learning Table III.SVM RBF kernel time. From the above results, it is found that the predictive C=0.1 C=0.2 C=0.3 C=0.4 accuracy shown by SVM with RBF kernel with parameters C=0.2 and g=2 is higher than the SVM with linear and g 1 2 1 2 1 2 1 2 polynomial kernel. Accuracy (%) 80 83 83 81 83 86 85 77 7. CONCLUSION Time(secs) 0.2 0.3 0.4 0.4 0.5 1.5 1.6 1.2 This paper elucidates the modeling of the epileptic seizure prediction task as multi-class classification problem and the implementation of supervised learning algorithm, support vector machine. The performance of SVM based epilepsy prediction models is evaluated using 10 fold cross The average and comparative performance of the SVM validation and the results are analyzed. The results indicate based prediction model in terms of predictive accuracy and that the support vector machine with RBF kernel provide learning time is given in Table IV and shown in Figure 1 the high prediction accuracy compared to other kernels. and Figure 2. SVM is better than conventional methods and show good Table IV. Overall performance of three models performance in all experiments it is very flexible and more powerful because of its robustness. It is hoped that more Kernel type Accuracy Learning time interesting results will follow on further exploration of Linear 84.96% 0.027 secs data. Polynomial 90.12% 0.362 secs 8. ACKNOWLEDGMENT RBF 93.87% 0.787 secs The author would like to thank the Management and Prediction Accuracy Hospital, Coimbatore for providing the EEG data. 100 86 9. REFERENCES 82 78 [1] Forrest Sheng Bao , Jue-Ming Gao, Jing Hu , Donald Y. C. 75 Lie , Yuanlin Zhang , and K. J. Oommen. ―Automated Epilepsy Diagnosis Using Interictal Scalp EEG‖. 31st 50 Accuracy(%) Annual International Conference of the IEEE EMBS Minneapolis, Minnesota, USA, September 2-6, 2009. 25 [2] Piotr Mirowski MSc*, Deepak Madhavan Yann Le Cun, uben Kuzniecky ― Classification of Patterns of 0 Linear Polynomial RBF EEG Synchronization for Seizure Prediction‖. [3] A. R.Sulaiman, ― Joint Time - Frequency Analysis and Figure 2. Prediction Accuracy Its pplication for Non - Stationary Signals‖, Ph.D. Thesis Elect. Eng. Dept., University of Mosul, 2001. [4] Webster, J. G., ―Medical Instrumentation Application and esign‖, 2nd ed., New York: Wiley, 1995. 120 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 8, No. 6, September 2010 [5] Nello Cristianini and John Shawe - Taylor. ―An Introduction to Support Vector Machines and other kernel - based earning methods‖ Cambridge University Press, 2000. [6] K. Crammer and Y. Singer. ―On the Algorithmic implementation of Multi – class SVMs, JMLR, 2001. Vojislav Kecman: "Learning and Soft Computing — Support Vector Machines, Neural Networks, Fuzzy Logic Systems", The MIT Press, Cambridge, MA, 2001. [7] Chui, C.K. (1992a), ―Wavelets: a tutorial in theory and applications‖, Academic Press. [8] lan H. Witten, Eibe Frank, Len Trigg, Mark Hall, Geoffrey Holmes, Sally. [9] Ian H. Witten, Eibe Frank. : Data Mining – Practical Machine Learning Tools and Techniques. 2nd edn. Elsevier. (2005). [10] Joachims T, Schölkopf B, Burges C, Smola A,‖Making large-Scale SVM Learning Practical. Advances in Kernel Methods - Support Vector Learning‖, 1999, MIT Press, Cambridge, MA, USA. [11] John Shawe-Taylor, Nello Cristianini, ―Support Vector Machines and other kernel-based learning methods‖, 2000, Cambridge University Press, UK. [12] Soman K.P, Loganathan R, Ajay V, ―Machine Learning with SVM and other Kernel Methods‖, 2009, PHI, India. [13] Crammer Koby, Yoram Singer,―On the Algorithmic Implementation of Multi-class Kernel-based Vector Machines‖, Journal of Machine Learning Research, MIT Press, Cambridge, MA, USA, 2001, Vol.2 Page 265-292. 121 http://sites.google.com/site/ijcsis/ ISSN 1947-5500

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