Classify and Compare Using S-SVM and LS-SVM for EMD Based Feature Extraction of EEG Signal

Transcription

1 International Journal of Electronics Engineering Research. ISSN Volume 9, Number 7 (2017) pp Research India Publications Classify and Compare Using S-SVM and LS-SVM for EMD Based Feature Extraction of EEG Signal M.Dhivya M.E, Applied Electronics Velammal Engineering College Ms. I.Manju Senior Grade Assistant Professor Velammal Engineering College Abstract Disease identification is major task in the field of biomedical. This research paper presents a feature extraction from Electroencephalogram (EEG) signals using empirical mode decomposition (EMD). It discriminate the EEG signals corresponding to healthy persons and epileptic patients during seizure - free intervals and seizure attacks. It gives an effective time-frequency analysis of non-stationary signals. The intrinsic mode functions (IMF) obtained by the result of EMD give the decomposition of a signal according to its frequency components. This project presents the usage of temporal statistics, and spectral features including spectral centroid, coefficient of variation and the spectral skew of the IMFs for feature extraction from EEG signals. These features extraction is relevant to find out the normal and pathological EEG signal. The normal EEG signals have different temporal and spectral centroids, dispersions and symmetries when compared with the pathological EEG signals. The structured support vector machine (S-SVM) and least square support vector machine (LS- SVM) are used for the classification purposes. In this paper both this two classifier used to classify the EEG signals, Finally compare the performance and accuracy of both this two classifier, determine whether the S-SVM is more suitable for the EEG signals classification. Index terms: EEG signal, Epilepsy,Empirical mode decomposition, feature extraction, classification.

2 946 M.Dhivya and Ms.I.Manju I. INTRODUCTION Electroencephalography (EEG) is a method to record electrical activity of the brain. The EEG signals can be effectively used for various applications such as emotion recognition, brain computer interfaces (BCI) etc. One of the most important applications of the analysis of EEG signals is its use in neuroscience to diagnose diseases and brain disorders. It is typically noninvasive, with the electrodes placed in the scalp, although invasive electrodes are sometimes used in specific applications. EEG measures voltage fluctuations. Epilepsy is a world common neurological disorder in human beings. It is also known as epileptic fit which create sign or symptoms due to abnormal excessive or synchronous neuronal activity in the brain causes the loss of consciousness or a whole body convulsion. The outward effect can vary from uncontrolled jerking movement(tonic-clonic seizure) to as subtle as a momentary loss of awareness(absence seizure).diseases of the brain characterized by an enduring predisposition to generate epileptic seizures are collectively called epilepsy. Seizures can also occur in people who do not have epilepsy for various reasons including brain trauma, drug use, elevated body temperature, low blood sugar and low levels of oxygen. EEG represents a signal containing information about the condition of the brain. Patients are unaware of seizure due to the random nature of them which may increase the risk of physical injury. The algorithm required for automated seizure detection and prediction employs feature computation and subsequent classification. Electroencephalogram is an important tools for diagnosis and analysis of epilepsy. It represents the electrical activity produced by firing of neuron within the brain. Epileptic seizures can make different changes in perception and behavior temporarily. In the human EEG, they are throw back by ictal sequence, where epileptic seizure becomes real as quality, rhythmic signals frequently coinciding with preceding before the usual observable changes in behavior.since the EEG data include the transient signals in noise and non stationary signals, where nonlinear time-series analysis should be carried out with caution. Its detection is typically done by the physicians using a visual scanning of the EEG signals which is a time consuming process and may be inaccurate. These inaccuracies are particularly significant for long time duration EEG signals. The parameters extracted from the EEG signals using various signal processing methods are very useful for diagnostics. The spectral parameters based on the Fourier transform are useful for analyzing the EEG signals and have shown good results on their classification. However, it is important to note that the Fourier domain does not exhibit any time-domain characteristics in the signal giving the features which are sub-optimal for feature extraction from some signal processing scenarios. Several other methods based on time-frequency domain have been developed for the detection of epileptic seizures from EEG signals. These methods include the use of short time Fourier transform (STFT). Although good results are obtained using these methods,

3 Classify and Compare Using S-SVM and LS-SVM for EMD Based Feature 947 the STFT does not yield a multiresolution analysis of the signals. This is because of the fact that the STFT uses the filters of the same bandwidth for signal decomposition at all frequencies. This limitation is typically resolved using the wavelet analysis in which a multiresolution time-frequency analysis is facilitated by forming band pass filters with varying bandwidths. Researchers have found the wavelet analysis to be a very useful tool for various signal processing applications In this paper the wavelet transform is used to solve multiresolution problem. In this analysis multiresolution time-frequency analysis is facilitated by forming band pass filters with varying bandwidths. The artifact in the EEG signals are removed using a wavelet ICA(Independent Component Analysis) based method giving good results on suppression of artifacts in EEG signals. EEG signals, which shown that their frequency components change over a period of time making them non-stationary. Hence, the signal processing methods which are more suitable for such signals are desired. Recently, new techniques for the analysis of non-stationary and non-linear signals have been proposed which are mainly based on empirical mode decomposition (EMD).The EMD is a time-frequency based method to decompose a signal into a number of intrinsic mode functions(imfs)which are the oscillatory components. The EMD is effective for the time-frequency analysis of the non-stationary signals. In this paper, the classification of EEG signals involving three stages. In the first stage, the EMD is used to decompose the signal into the number of IMFs. the IMFs is nothing but the oscillatory component. The second stage involves in the first three number of IMFs are used for the feature extraction.in the feature extraction method both the third order temporal statistics and spectral statistics are involved. In the third stage, the calculated features are finally applied to both structured support vector machine (S-SVM) and least square support vector machine(ls-svm),which is used to classify the normal and pathological EEG signal. After the comparition of both two classifiers and determine that the S-SVM is more suitable for the classification of eeg signals. Fig 1. Block diagram of proposed system

4 948 M.Dhivya and Ms.I.Manju II. DATASET In this study, we have used an EEG dataset that is publicly available online The dataset consists of three subsets(denoted as sets A -C) each containing 50 single channel EEG signals, each one having a duration of 23.6 seconds. signals have been selected from continuous multichannel EEG recording after visual inspection of artifacts. The Sets A and B consist of surface EEG segments collected from five healthy volunteers in awaken and relaxed state with their eyes opened and closed respectively.set C contains signals corresponding to seizure attacks (i.e., ictal EEG), recorded using all the electrodes. The signals are recorded in a digital format at a sampling rate of Hz. Thus, the sample length of each segment is Figure 2: Sample EEG signals from three different sets from rows 1to 3(A, B and C respectively) II. METHODS A. Empirical Mode Decomposition EMD The EMD is a method of decomposing a signal without leaving a time domain. It can be compared to other analysis methods like fourier transforms and wavelet decomposition. This process is useful for analyzing natural signals,which are most often non-linear and non-stationary. EMD filters out functions which form a complete and nearly orthogonal basis for the original signal. completeness is based on the method of the EMD, the way it is decomposed implies completeness. This functions, known as Intrinsic Mode Functions(IMFs),are therefore sufficient to describe the signal, even though they are not necessarily orthogonal. consider x(t) is a given EEG signal,the calculation of its IMFs involves the following steps. 1) Identifying all the extrema (maxima and minima) in x(t). 2) Interpolate between minima and maxima generating the envelopes el (t) and em (t).

5 Classify and Compare Using S-SVM and LS-SVM for EMD Based Feature 949 3) Determine the local mean as a(t) = e m (t) + e1 (t). 2 4) Extract the detail i.e h1(t) x(t) a(t). 5) Decide whether h1(t) is an IMF or not based on two basic conditions for IMFs mentioned above. 6) Repeat step 1 to 4 until An IMF is obtained. After the first IMFs is obtained, then we have to define the c1(t) h1(t),which is the smallest temporal scale in x(t).a residual signal obtained as r1(t) x1(t) c1 (t). At the end of the decomposition, the original signal can be represented as follows M x(t) c m (t) + r M (t) m 0 1 Where M is the number of IMFs, cm(t) is the m th IMFs and rm(t) is the final residue. B. Analytic representation of IMFs: After the extraction of IMFs is done from the EEG signals, their analytic representation is obtained. This representation the DC offset from the spectral component of the signals, Which is the aspect to compensate for the non-stationary of the signals. Then the IMFs cm(t) is obtained and the analytic representation is y(t) = c m (t) + ih{c m (t)} where H{cm(t)} is the Hilbert transform of cm(t), which is the m th IMF extracted from the signal x(t). After find the EMD of the signal, the IMFs are used for feature extraction purposes. C. Temporal statistics of analytic IMFs: The statistical features of the IMFs are useful for discriminating between normal and pathological EEG signals. The distribution of samples in the data are characterized by their asymmetry, dispersion and concentration around the mean. In the IMFs the visual analysis is done from healthy and epilepsy patients during interictal and ictal periods after Hilbert transform. By using HT, the difference between appropriately captured using the statistics of the IMFs. For an IMF, these statistics can be obtained by

6 950 M.Dhivya and Ms.I.Manju σ μ t N = 1 N y i i=1 t= 1 N N t=1 (y i μ t )2 N β t = 1 N (y i μ t )3 i=1 σ t Where N is the number of samples in the IMF µt is the mean, ơt is the variance and tβt is skewness of the corresponding IMF. D. Spectral statistics of analytic IMFs: EMD has the features to perform, a spectral analysis of the signals. A frequency based analysis can therefore be useful for feature extraction from EEG signals. The EMD helps to decompose a signal into number of components(imfs) which are response to filters having narrow pass bands. The spectral analysis is done using the calculation of instantaneous frequencies (IF).The calculation of IF has the physical meaning only for monocomponent signals. The discrimination power of the PSD features can be analysed by their respective plots for three IMFs from the no α p(w) = r y [n]e jwn α where ry[n] represents the autocorrelation of y[n], defined as ry[n] = E(y[m]y_[m]). Visual analysis of the PSD of IMFs shows that the statistics of the PSD can be used as relevant features for feature extraction. 1. Spectral centroid The researchers have shown that thecentroid frequencies of the IMFs extracted from EEG signalsform distinct groups when supervised clustering is applied onthe EEG signals. These respective groups are indicativeof the seizure and non-seizure EEG signals. The centroidfrequency is therefore a distinctive feature that can be usedfor the characterization of EEG signals. The discrimination power of thepsd features can be visually analysed by their respective plots for three IMFs from the normal and pathological EEG signals. The PSD can be calculated as follows:

7 Classify and Compare Using S-SVM and LS-SVM for EMD Based Feature 951 c s = w wp(w) w p(w) where P (w) is the amplitude of wth frequency bin in thespectrum. 2. Variation coefficient: Since the spectral variation in theimfs is different for normal and pathological EEG signals,therefore it can be used for their characterization. This variation can be calculated as follows: where Cs is the spectral centroid. σ2 s= w(w c s )2p(w) wp(w) 3. Spectral skew: Skewness is the third order moment andit measures the symmetry/asymmetry of a distribution. EEG signals differs thus potentially yielding a useful featurefor the classification of EEG signals. Skewness of the PSDcan be calculated as: βn= w(w c )2p(w) σ w p(w) After the extraction of temporal and spectral features of each IMF, its feature vector can be obtained by F = [ μ t σ t β t c s σ s β s ] The feature vectors obtained from several IMFs can than be used for classification purposes. E. Classification Feature extraction is followed by the classification of EEG signals using S-SVM and LS-SVM. The S-SVM is the generalization of SVM.whereas SVM depends upon the binary classification, multiclass classification and regression,while the S-SVM allows training of a classifier for general structured output labels. It depends upon the correct and previous output samples.least sqaures support vector machine is the set of related supervised learning method that helps to analyze data and recognize patterns,which is used for the classification and regression analysis.it is the class of kernal based learning methods.kernel methods are also called as kernal functions. This function

8 952 M.Dhivya and Ms.I.Manju determine the similarity in the input traning data and split out the training data by using hyperplane. III. EXPERIMENTAL RESULTS The performance of the proposed methodology for feature extraction from EEG signals is studied using standard measures such as overall accuracy and area under receiver operating characteristics (ROC) curve. A. Performance and analysis of proposed system. (a) (b) (c) (d) (e) Fig 2 (a) Original input signal (b)calculated IMFs from the original signal (c) power spectral density (d) ROC Curve for S-SVM (e) Receiver Operating Characteristic curve for LS-SVM.

9 Classify and Compare Using S-SVM and LS-SVM for EMD Based Feature 953 In this paper 50 signals are tested for calculating the accuracy. The accuracy of a test is its ability to differentiate the normal and epilepsy signal correctly.to estimate the accuracy of the test,we should calculate the proportion of true positive and true negative in all evaluated cases. True positive and true negative is also called as sensitivity and specificity. Mathematically, this can be started as ACCURACY = TP+TN/TN+TP+FP+FN SIGNAL ACCURACY FOR S-SVM ACCURACY FOR LS-SVM Normal signal Abnormal signal Total accuracy 94% 87% IV. DISCUSSON AND CONCLUSION This project paper presented an EEG data classification algorithm, which based on a large number of feature extraction after wavelet transform.the foundation of this method lies on the extraction of temporal and spectral features from Empirical Mode Decomposition (EMD) of the EEG signals. The usage of EMD is motivated by the fact that EEG signals are non-stationary and EMD is a data dependent method exhibiting a better adaptability towards non-stationary in the EEG signals. The main advantage of the algorithm are (a) the ability of the algorithm to run robustly in a clinical setting with noised EEG;(b) feature extraction with highly meaningful wavelet transform because hidden EEG information can be revealed and the noise effort reduced as certain data under some scales are omitted;(c) simplicity and low computational cost guaranteeing real clinical application;(d)very good sensitivity and specificity. Both the S-SVM and LS-SVM are used for the classification of EEG signals but according to performance and accuracy S-SVM is more suitable for EEG than LS-SVM. S-SVM helps to find the normal and abnormal signals in a accurate way.

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