Reading response Essay Example | Topics and Well Written Essays - 500 words - 32

Reading response - Essay Example The amazing thing is that most of the ideas extended by the original work and its critics are in the nature of fictitious blames and lack any substantial material to base them on. The article also shows how the nation has a long standing history of being scared of minorities and diversity. It was not without a reason that the book, The Awful Disclosures of Maria Monk won an avid readership, not only in her days, but even today. The irony that the American masses do have a history of being afraid of the foreign and the different does ensue from this article. The writer in a way does succeed in bringing out the fact that the work The Awful Disclosures of Maria Monk did intend to present the Catholic Church as a symbol of evil and it strongly intended to present the Catholic priesthood as embodiment of carnal pleasures and immorality. The author brings out the fact that Maria Monks never resided in a Catholic convent and that her mental capacities and morality were never beyond doubt. There is other side of the coin also. It is obvious that the writer Ruth Hughes is a Catholic and intends to unravel the lying and falsehood resorted to by Maria Monks and her supporters, to debase and vilify the Catholic Church. However, while doing so, Hughes did fail many times to show the kindness, compassion and forgiveness that so typically define the Catholic faith. Instead one is resorting to the kind of aggressive and base tactics that were resorted to by one’s opponents. Ruth Hughes is trying to do away with the accusation made by Maria Monks and her supporters by vilifying their character and sense of morality and ethics. It would have been much saner if Hughes had come down on this vilification of the Catholic by taking a higher ground. This would have presented one’s faith to the readers in a more favorable and benign light and would have brought out the irresponsible

Tony Blair and Liberal Democracy Ideology Essay

Tony Blair and Liberal Democracy Ideology - Essay Example Tony Blair played a major role in Unifying the Labour Party a fact that made the popularity of the Labour Party to rise in the United Kingdom. It is through this unification that the country experienced a balance between the two major parties the country and that is the Conservative Party and the Labour Party. This is referred to by some experts as the Blair effect. This shows that Tony Blair was a good leader by all means and this also brings about the question of liberalization. It is important to note that Tony Blair highly advocated for the liberalization of various aspects in the country as well as other parts of the world especially the developing countries and countries that were having political, social and economical problems. Tony Blair was the Prime Minister of the United Kingdom from the year 1997 to the year 2007 and during his premiership, he adopted various policies which have been seen by many as to advocate for Liberal Democracy and Nation State. Many people will remember him for the foreign wars that he was involved in when he was the Prime Minister and also his doctrines of military intervention in various conflicts in the world. When he was resigning, he argued in parliament that his successors should learn to use his foreign policy. Although these doctrines and policies were not instrumental in his first election to the premiership and were not cited in his campaign, the policies that were cited in his first campaign still remained instrumental in his leadership throughout his premiership1. The most prevalent political model of Tony Blair was the Liberal Democratic Nation State. This was his initial political ideology and while he exercised this at the domestic politics, his foreign policy leaned very much to the Liberal Democracy ideology. It is important to note that even though the Liberal Democracy was a prevalent policy in the United Kingdom long before Tony Blair was elected to the premiership but he also embraced this policy during his premiership2. On the international side, the global crusade for the policy of liberal democracy was taken by Tony Blair as his personal crusade during the conflict of Kosovo. Although he faced significant criticism from various quarters, he did not shy away from implementing this policy in the subsequent wars that included Afghanistan and Iraq. He was also very instrumental in bringing about neoliberalism to be the country's dominant social and moral philosophy. This does not meant that neoliberalism is just a synonym of capitalism because the society and the political culture of the country were transformed by numerous policies which sought to eliminate the ideal of equality from the political policies and this policies also encourage the establishment and acceptance of an underclass which had the outlook of permanency and hereditary social group. Under the administration of Tony Blair, the core electorate group also known as Middle Britain had the opportunity to dominate the country's politics and in so doing they excluded the disadvantaged and non-voting underclass from the politics of the country3. Blair also attempted to implement the Mazzinian Nationalism but this failed

CJUS 330 Book Review Essay Example | Topics and Well Written Essays - 750 words

CJUS 330 Book Review - Essay Example The so called â€Å"liberalized† people have foregone the cultures and traditions of the American society and embraced new ways of living. For instance, gay marriages, pornography, abortion, and radical feminism have come to be accepted in the American society. Such actions are fuelled by an American Supreme Court that has lost faith in the norms of the society and continues to make decisions that will end up destroying the society. The author maintains that there is a faction of Americans who still believe in their traditions of low taxes, purity of marriage, justice, and the rule of law. However, this faction is ignored and the judges of the Supreme Court make rulings on cases based on what suits them (the judges) best. Robertson (2004) categorically states that the past fifty years have seen the Supreme Court in America become radicalized; resulting in the distortion of the very justice, law, and order the court is meant to uphold. In my opinion, the book is very engaging as the author takes us through the foundations of the American constitution. The author gives us an insight of what the framers of the constitution had in mind when they decided that the government should have three arms. According to Robertson (2004) the role of the judiciary is to interpret laws without being influenced by either the legislature or the judiciary. Today, however, decisions made by the Supreme Court are based on the political will of the government, with complete disregard for the ordinary citizen. The author gives many examples of how the Supreme Court has disregarded American culture in most of its rulings. There are examples in the book about how the Supreme Court voted for the exclusion of the phrase â€Å"under God† in the Pledge, though polls showed most Americans wanted the phrase to remain. Cases of rulings in favor of gay relationships and internet pornography are also used by the author to justify his claims about a wayward Supreme Court (Robertson,

Tony Blair and Liberal Democracy Ideology Essay

Tony Blair and Liberal Democracy Ideology - Essay Example Tony Blair played a major role in Unifying the Labour Party a fact that made the popularity of the Labour Party to rise in the United Kingdom. It is through this unification that the country experienced a balance between the two major parties the country and that is the Conservative Party and the Labour Party. This is referred to by some experts as the Blair effect. This shows that Tony Blair was a good leader by all means and this also brings about the question of liberalization. It is important to note that Tony Blair highly advocated for the liberalization of various aspects in the country as well as other parts of the world especially the developing countries and countries that were having political, social and economical problems. Tony Blair was the Prime Minister of the United Kingdom from the year 1997 to the year 2007 and during his premiership, he adopted various policies which have been seen by many as to advocate for Liberal Democracy and Nation State. Many people will remember him for the foreign wars that he was involved in when he was the Prime Minister and also his doctrines of military intervention in various conflicts in the world. When he was resigning, he argued in parliament that his successors should learn to use his foreign policy. Although these doctrines and policies were not instrumental in his first election to the premiership and were not cited in his campaign, the policies that were cited in his first campaign still remained instrumental in his leadership throughout his premiership1. The most prevalent political model of Tony Blair was the Liberal Democratic Nation State. This was his initial political ideology and while he exercised this at the domestic politics, his foreign policy leaned very much to the Liberal Democracy ideology. It is important to note that even though the Liberal Democracy was a prevalent policy in the United Kingdom long before Tony Blair was elected to the premiership but he also embraced this policy during his premiership2. On the international side, the global crusade for the policy of liberal democracy was taken by Tony Blair as his personal crusade during the conflict of Kosovo. Although he faced significant criticism from various quarters, he did not shy away from implementing this policy in the subsequent wars that included Afghanistan and Iraq. He was also very instrumental in bringing about neoliberalism to be the country's dominant social and moral philosophy. This does not meant that neoliberalism is just a synonym of capitalism because the society and the political culture of the country were transformed by numerous policies which sought to eliminate the ideal of equality from the political policies and this policies also encourage the establishment and acceptance of an underclass which had the outlook of permanency and hereditary social group. Under the administration of Tony Blair, the core electorate group also known as Middle Britain had the opportunity to dominate the country's politics and in so doing they excluded the disadvantaged and non-voting underclass from the politics of the country3. Blair also attempted to implement the Mazzinian Nationalism but this failed

Save the world proposal Essay Example | Topics and Well Written Essays - 750 words

Save the world proposal - Essay Example In this proposal, the threats facing this animal will be looked at especially the species belonging to the giant panda. Extinction results into complete eradication of an animal species from the earth surface which has a number of consequences to the ecosystem. The proposal will finally analyze some of the possible ways of saving this species of panda from the imminent eradication and extinction (Gong & Reid 246). The giant panda belongs to the bear family and occupies large parts of china and other areas of New Zealand and Australia. It is an omnivore eating both bamboo leaves and soft tissues sections and small animals found within its habitat. Moreover, it is one of the major sources of tourism revenue in china and New Zealand and foreigners troop these countries to be with this friendly animal. Furthermore, it presents many opportunities for the country that makes it essential for the world life conservancy authorities and groups to develop mechanisms of protecting the animal (Ou yang et al 622). The giant panda is considered as one of the rare species of bear currently available that depends on bamboos and soft tissue plants to survive. Bamboo has however attracted a number of economic applications across different levels of economic activities in the world. As a result, bamboo cutting has significantly increased as people use them for economic purposes or clear the land for farming activities due to increased human population. This deprives the giant panda of its main source of food, thus leaving the animal with small animals as the only alternative source of food. Additionally, the giant panda is slow to reproduce which means that the animal has minimal number of offspring during its lifetime, further increasing its vulnerability to extinction (Entwistle & Dunstone 87). The giant panda should be saved from extinction considering the significant role it plays in reinforcing the efforts of conservation of the flora and fauna. It is considered as one of the most loved animal species not just in china but also in other parts of the country. The region where the giant panda is found is considered as the heartland of Chinese which makes it essential to ensure sustainability in the region. Encouraging sustainability in this region will not only protect the giant panda from extinction but also improve the lifelines of the people around the Yangtze Basin in China. This area acts as the heartland of economic activities in china, being home to both tourist activities, subsistence fisheries and a number of economic activities essential for the growth of the country (Li et al 48). The extinction of the giant panda has a number of ecological, economical and agricultural impacts not just to china but also to the rest of the world. In the event of this extinction, China will end up losing a symbol of its national pride and conservancy efforts. The rate of bamboo consumption in the country will increase tremendously as there will be no concern arisi ng from the existence of the giant panda. This will create significant ecological and agricultural implications to the areas where bamboo is widely grown (Entwistle & Dunstone 87). Despite the widespread concerns on animal and plant conservancy, the benefits achieved maybe are overshadowed by the challenges. Extinction to some scholars is created by natural forces as explained by Darwin theories in relations to the available natural resources. The continued

Study Guide Questions for Fahrenheit 451 Essay Example for Free

Study Guide Questions for Fahrenheit 451 Essay Answer the following questions in paragraph form. These questions should act as a reading guide and are not intended to replace careful reading of the novels themes and development. Part I: The Hearth and the Salamander (pages 3-14) 1. What do the fireman do for a living? For a living the â€Å"fireman† burns books and occasionally some people, if they are with the book. It’s quite different that what firemen do today. 2. In the opening scene, why are the books compared to birds? In the opening scene, the books are refered to as flapping pigeon-winged books because the burning pages look as if they are wings of a bird flapping up and down. 3. What does Montag think of his job? Montag enjoys his job burning books and takes great pride in it. At the beginning of the novel, it largely defines his character. The opening passage describes the pleasure he experiences while burning books. He loves the spectacle of burning and seeing things â€Å"changed† by the fire. 4. Who does Montag meet on the way home? He meets his new neighbor, an inquisitive and unusual seventeen-year-old named Clarisse McClellan. She immediately recognizes him as a fireman and seems fascinated by him and his uniform. She explains that she is â€Å"crazy† and proceeds to suggest that the original duty of firemen was to extinguish fires rather than to light them. She asks him about his job and tells him that she comes from a strange family that does such peculiar things as talk to each other and walk places. Clarisse’s strangeness makes Guy nervous, and he laughs repeatedly and involuntarily. 5. During his conversation, Montag says that You never wash it off completely referring to the kerosene. What could this mean symbolically? This could mean that Montag always acts and thinks like a fireman, even when hes not working; that being a fireman affects the way you see the world. It could also mean that Montag doesnt want to wash off the smell completely, that he likes and is proud of it. 6. Speculate: Why do you think that Bradbury would introduce Clarisse before Montags wife, Mildred? I think that Bradbury introduces Clarisse before Mildred because of the impact she has on Montags way of thought and sense of being in the world. Even though she is his wife, Millie really has little bearing on causing change in Montag. She is representative of the Status Quo, the way things are. Yet, Clarisse is a voice, the first real and definitive voice that represents how things should be for Montag. She is the first voice to challenge him and compel him to think and reflect about how what he is doing needs to stop.

Wavelet Packet Feature Extraction And Support Vector Machine Psychology Essay

Wavelet Packet Feature Extraction And Support Vector Machine Psychology Essay ABSTRACT- The aim of this work is an automatic classification of the electroencephalogram (EEG) signals by using statistical features extraction and support vector machine. From a real database, two sets of EEG signals are used: EEG recorded from a healthy person and from an epileptic person during epileptic seizures. Three important statistical features are computed at different sub-bands discrete wavelet and wavelet packet decomposition of EEG recordings. In this study, to select the best wavelet for our application, five wavelet basis functions are considered for processing EEG signals. After reducing the dimension of the obtained data by linear discriminant analysis and principal component analysis, feature vectors are used to model and to train the efficient support vector machine classifier. In order to show the efficiency of this approach, the statistical classification performances are evaluated, and a rate of 100% for the best classification accuracy is obtained and is compa red with those obtained in other studies for the same data set. Keywords- EEG; Discrete Wavelet Transform, Wavelet Packet Transform, Support Vector Machine, Statistical analysis, classification. 1. Introduction In neurology, the electroencephalogram (EEG) is a non-invasive test of brain function that is mostly used for the diagnosis and classification of epilepsy. The epilepsy episodes are a result of excessive electrical discharges in a group of brain cells. Epilepsy is a chronic neurological disorder of the brain that affects over 50 million people worldwide and in developing countries, three fourths of people with epilepsy may not receive the treatment they need [1]. In clinical decisions, the EEG is related to initiation of therapy to improve quality of epileptic patients life. However, EEG signals occupy a huge volume and the scoring of long-term EEG recordings by visual inspection, in order to classify epilepsy, is usually a time consuming task. Therefore, many researchers have addressed the problem of automatic detection and classification of epileptic EEG signals [2, 3]. Different studies have shown that EEG signal is a non-stationary process and non-linear features are extracted fr om brain activity recordings in order to specific signal characteristics [2, 4, 5, 6]. Then these features are used as input of classifiers [11]. Subasi in [7] used the discrete wavelet transform (DWT) coefficient of normal and epileptic EEG segments in a modular neural network called mixture of expert. For the same EEG data set, Polat and Gà ¼nes [8] used the feature reduction methods including DWT, autoregressive and discrete Fourier transform. In Subasi and Gursoy [9], the dimensionality of the DWT features was reduced using principal component analysis (PCA), independent component analysis (ICA) and linear discriminant analysis (LDA). The resultant features were used to classify normal and epilepsy EEG signals using support vector machine. Jahankhani, Kodogiannis and Revett [10] have obtained feature vectors from EEG signals by DWT and performed the classification by multilayer perceptron (MLP) and radial basis function network. Wavelet packet transform (WPT) appears as one of most promising methods as shown by a great number of works in the literature [11] particularly for ECG signals and relatively fewer, for EEG signals. In [12], Wang, Miao and Xie used wavelet packet entropy method to extract features and K-nearest neighbor (K-NN) classifier. In this work, both DWT and WPT split non stationary EEG signals into frequency sub-bands. Then a set of statistical features such as standard deviation, energy and entropy from real database EEG recordings were computed from e ach decomposition level to represent time-frequency distribution of wavelet coefficients. LDA and PCA are applied to these various parameters allowing a data reduction. These features were used as an input to efficient SVM classifier with two discrete outputs: normal person and epileptic subject. A measure of the performances of these methods is presented. The remaining of this paper is organized as follows: Section 2 describes the data set of EEG signals used in our work. In Section 3, preliminaries are presented for immediate reference. This is followed by the step up of our experiments and the results in section 4. Finally, some concluding remarks are given in Section 5. 2. DATA SELECTION We have used the EEG data taken from the artifact free EEG time series database available at the Department of Epileptology, University of Bonn [23]. The complete dataset consists of five sets (denoted A-B-C-D-E). Each set contains100 single-channel EEG signals of 23,6s. The normal EEG data was obtained from five healthy volunteers who were in the relaxed awake state with their eyes open (set A). These signals were obtained from extra-cranially surface EEG recordings in accordance with a standardized electrode placement. Set E contains seizure activity, selected from all recording sites exhibiting ictal activity. All EEG signals were recorded with the same 128 channel amplifier system and digitized at 173.61Hz sampling. 12 bit analog-to-digital conversion and band-pass (0.53-40 Hz) filter settings were used. For a more detailed description, the reader can refer to [13]. In our study, we used set A and set E from the complete dataset. Raw EEG signal Feature extraction: Energy, Entropy and Standard deviation from DWT and WPT decom-position coefficients Dimensionality reduction by LDA and PCA Classification and Performance measure Healthy Epileptic Figure 1 The flow chart of the proposed system 3. methods The proposed method consists of three main parts: (i) statistical feature extraction from DWT and from WPT decomposition coefficients, (ii) dimensionality reduction using PCA and LDA, and (iii) EEG classification using SVM. The flow chart of the proposed method is given in figure 1. Details of the pre-processing and classification steps are examined in the following subsections. 3.1 Analysis using DWT and WPT Since the EEG is a highly non-stationary signal, it has been recently recommended the use of time-frequency domain methods [14]. Wavelet transform can be used to decompose a signal into sub-bands with low frequency (approximate coefficients) and sub-bands with high frequency (detailed coefficients) [15, 16, 17]. Under discrete wavelet transform (DWT), only approximation coefficients are decomposed iteratively by two filters and then down-sampled by 2. The first filter h[.] is a high-pass filter which is the mirror of the second low pass filter l[.]. DWT gives a left recursive binary tree structure. We processed 16 DWT coefficients. Wavelet packet transform (WPT) is an extension of DWT that gives a more informative signal analysis. By using WPT, the lower, as well as the higher frequency bands are decomposed giving a balanced tree structure. The wavelet packet transform generates a full decomposition tree, as shown in figure 2. In this work, we performed five-level wavelet packet deco mposition. The two wavelet packet orthogonal bases at a parent node (i, p) are obtained from the following recursive relationships Eq. (1) and (2), where l[n] and h[n] are low (scale) and high (wavelet) pass filter, respectively; i is the index of a subspaces depth and p is the number of subspaces [15]. The wavelet packet coefficients corresponding to the signal x(t) can be obtained from Eq. (3), l (3,0) (3,1)†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦(3,6) (3,7) h l h l h l h h l h l h l SIGNAL (0,0) (1,0) (1,1) (2,0) (2,1) (2,2) (2,3) Figure 2 Third level wavelet packet decomposition of EEG signal Table 1 gives the frequency bands for each level of WPT decomposition. Figures 3 and 4 show the fifth level wavelet packet decomposition of EEG segments, according to figure 2. We processed 32 WPT coefficients. Therefore, in this study, three statistical parameters: energy feature (En), the measure of Shannon entropy (Ent) and standard deviation (Std) are computed, (4) (5) (6) 3.2 Principal component analysis To make a classifier system more effective, we use principal component analysis (PCA) for dimensionality reduction. The purpose of its implementation is to derive a small number of uncorrelated principal components from a larger set of zero-mean variables, retaining the maximum possible amount of information from the original data. Formally, the most common derivation of PCA is in terms of standardized linear projection, which maximizes the variance in the projected space [18, 19]. For a given p-dimensional data set X, the m principal axes W1,†¦,Wm where 1≠¤ m≠¤ p, are orthogonal axes onto which the retained variance is maximum in the projected space. Generally, W1,†¦,Wm can be given by the m leading eigenvectors of the sample Table1 Frequency band of each wavelet decomposition level. Decomposition level Frequency band (Hz) 1 2 3 4 5 0-86.8; 86.8-173.6 0-43.5; 43.5-86.8; 86.3-130.2 ;130.2-173.6 0-21.75; 21.75-43.5; 43.5-54.375; 54.375-86.3; 86.3-108.05; 108.05-130.2; 130.2 130.2-151.95; 151.95-173.6; 0-10.875; 10.875-21.75; 21.75-32.625; 32.625-43.5; 43.5-54.375; 54.375-65.25; 65.25-76.125; 76.125-87; 87-97.875; 97.875-108.75; 108.75-119.625; 119.625-130.5; 130.5-141.375; 141.375-152.25; 152.25-163.125; 163.125-173.6 0-5.44; 5.44-10.875; 10.875-16.31; 16.31-21.75: 21.75-27.19; 27.19-32.625; 32.625-38.06; 38.06-43.5; 43.5-48.94; 48.94-54.375; 54.375-59.81; 59.81-65.25; 65.25-70.69; 70.69-76.125; 76.125-81.56;81.56-87; 87-92.44; 92.44-97.87; 97.87-103.3; 103.3-108.75; 108.75-114.19; 114.19-119.625; 119.625-125.06; 125.06-130.5; 130.5-135.94; 135.94-141.38; 141.38-146.81; 146.81-152.25; 152.25-157.69; 157.69-163.125; 163.125-168.56; 168.56-173.6 covariance matrix where is the sample mean and N is the number of samples, so that SWi= ÃŽ »iWi, where ÃŽ »i is the ith largest eigenvalue of S. The m principal components of a given observation vector xi are given by the reduced feature vector . 3.3 Linear discriminant analysis Linear discriminant analysis (LDA) projects high-dimensional data onto a low-dimensional space where the data can achieve maximum class separability [19]. The aim of LDA is to create a new variable that is a combination of the original predictors, i.e. the derived features in LDA are linear combinations of the original variables, where the coefficients are from the transformation matrix i.e. LDA utilizes a transformation matrix W, which can maximizes the ratio of the between-class scatter matrix SB to the within-class scatter matrix SW, to transform the original feature vectors into lower dimensional feature space by linear transformation. The linear function y= WTx maximizes the Fisher criterion J(W) [19], where xj(i) represents the jth sample of the ith of total c classes. k is the dimension of the feature space, and  µi is the Figure 3 Fifth level wavelet packet decomposition of healthy EEG signal (set A). Figure 4 Fifth level wavelet packet decomposition of epileptic EEG signal (set E). mean of the ith class. Mi is the number of samples within classes i in total number of classes. where is the mean of the entire data set. As a dimensionality reduction method, LDA has also been adopted in this work. 3.4 SVM classifier In this work, SVM [20] has been employed as a learning algorithm due to its superior classification ability. Let n examples S={xi,yi}i=1n, yià Ã‚ µ{-1,+1}, where xi represent the input vectors, yi is the class label. The decision hyperplane of SVM can be defined as (w, b); where w is a weight vector and b a bias. The optimal hyperplane can be written as, where w0 and b0 denote the optimal values of the weight vector and bias. Then, after training, test vector is classified by decision function, To find the optimum values of w and b, it is required to solve the following optimization problem: subject to where ÃŽ ¾i is the slack variable, C is the user-specified penalty parameter of the error term (C>0), and φ the kernel function [21]. A radial basis function (RBF) kernel defined as, was used, where ÏÆ' is kernel parameter defined by the user. 4. results and discussion Before we give the experimental results and discuss our observations, we present three performance measures used to evaluate the proposed classification method. (i) Sensitivity, represented by the true positive ratio (TPR), is defined as (ii) Specificity, represented by the true negative ratio (TNR), is given by, (iii) and average classification accuracy is defined as, (16) where FP and FN represent false positive and false negative, respectively. All the experiments in this work were undertaken over 100 segments EEG time series of 4096 samples for each class set A and set E. There were two diagnosis classes: Normal person and epileptic patient. To estimate the reliability of the proposed model, we utilize ten-fold cross validation method. The data is split into ten parts such that each part contains approximately the same proportion of class samples as in the classification dataset. Nine parts (i.e. 90%) are used for training the classifier, and the remaining part (i.e. 10%) for testing. This procedure is repeated ten times using a different part for testing in each case. As illustrated in Fig.3 and 4, feature vectors were computed from coefficient of EEG signals. Taking energy as feature vector, figure 5 shows that the features of both normal and epileptic EEG signals are mixed. The proposed analysis using wavelets was carried out using MATLAB R2011b. In literature, there is no common suggestion to select a particular wavelet. Therefore, a very important step before classifying EEG signals is to select an appropriate wavelet for our application. Then, five wavelet functions namely Daubechies, Coiflets, Biorthogonal, Symlets and Discrete Meyer wavelets are examined and compared, in order to evaluate the performance of various types of wavelets. Figure 6 shows accuracy, sensitivity and specificity from different wavelets. We see that the best wavelet giving good correct rate is the Db2, Db4, coif3 and Bior1.1.The choice of the mother wavelet is focused on daubechies where the length of the filter is 2N, while coifflet wavelet filter is 6N and biorthogonal wavelet (2N +2). After EEG signal Db2 wavelet decomposition and dimensionality reduction, results of correct rate classification are showed in Table 2. The classification accuracy varies from the optimum value (100%) to a lowest value (87%). The results using standard deviation are the best results obtained and using entropy is better than using energy in EEG signals classification. In this study, experimental results show that linear discriminant analysis based on wavelet packet decomposition improves classification and the optimum SVM results are obtained by using standard deviation feature computed from wavelet packet coefficient and LDA reduction method. For this proposed scheme, the accuracy of the classification is 100%. This method presents a novel contribution and has not yet been presented in the literature. Figure 7 shows the average rate of classification (accuracy, sensitivity, specificity) obtained with different methods of decomposition (DWT or WPT), two reduction methods (LDA or PCA) and three characteristic features (standard deviation, energy, entropy) using the four best wavelet (Db2, Db4, coif3 and Bior1.1). We see that the combination of LDA with standard deviation have an optimum average accuracy rate of 99.90% and combination of standard deviation with PCA reaches 99.50 %. Table 3 gives a summary of the accuracy results obtained by other studies from the same dataset (set A and set E) using extraction of features from EEG signal and their classification. 5. conclusion In this paper, EEG signals were decomposed into time-frequency representations using discrete wavelet transform, wavelet packet transform and statistical features were Figure 5 Energy feature vector coefficient D3versus D2 (adapted from [22]). Table 3 Epilepsy classification accuracies evaluation obtained in literature from the same data sets Authors Method Accuracy (%) [7] Subasi DWT + Mixture of Expert 94.50 [8] Polat and Gà ¼nes DWT+DFT+ Auto-regres-sive model + Decision Tree 99.32 [9] Subasi and Gursoy DWT+PCA+ LDA+ICA +SVM 98.75(PCA) 100(LDA) 99.5(ICA) [12] Wang, Miao and Xie WPT+ Entropy-hierarchical K-NN classification 99,44 [14] ÃÅ"beylà ¯ Burg autoregressive + LS-SVM 99.56 Our method WPT + Standard deviation+ LDA + SVM 100 computed to represent their distribution. The most suitable mother wavelets for feature extraction and classification were found. The selection of the suitable mother wavelet and using reduction methods lead to the improvement of performance of EEG signal classification. It has been shown by experiments that for the SVM and the combination of the standard deviation with LDA have the highest correct classification rate of 100% in comparison with other techniques. The interest in expert systems for detection and classification of epileptic EEG signal is expected to grow more and more in order to assist and strengthen the neurologist in numerous tasks, especially, to reduce the number of selection for classification performance. These promising results encourage us to continue with more depth our study and to apply it to other databases recorded with other diseases.