CHAPTER 2 LITERATURE REVIEW

9 CHAPTER 2 LITERATURE REVIEW In this chapter, a review of literature on Epileptic Seizure Detection, Wavelet Transform techniques, Principal Component Analysis, Artificial Neural Network, Radial Basis Function Neural network, Particle Swarm Optimisation Neural network and Adaptive Neuro Fuzzy Inference System analysis of the physiological parameters of epileptic EEG is presented. 2.1 EPILEPTIC SEIZURE DETECTION The topic of seizure detection has gained much attention in recent years due to evidence that it may be possible to predict epileptic seizures in adults (Niedermeyer and De Silva 1993). Niedermeyer (1999) explains the need of EEG which is an important clinical tool for diagnosing neurological disorders related to epilepsy. Teplan (2002) has discussed the clinical applications of the EEG in humans to investigate epilepsy and locate seizure origin. Nuwer (1997) reports that routine EEG is an established test commonly used in the clinical evaluation of patients with epilepsy and can help to locate an epileptic focus or suggest the type of epilepsy. Epilepsy is diagnosed by recording EEG from patient s scalp drawn on sheets of paper. To go through an hour of recording the electroencephalographer have to spend 2 to 3 hours looking for patterns. Even now the gold standard for registering seizures is through visual inspection by electroencephalographer during the recording (Subasi 2006). However an automatic method for epileptic seizure EEG signal analysis can provide an attractive alternative to

10 visual analysis. One of the main objectives of this work is to avoid the need for the presence of an expert so that continuous monitoring for days becomes easier 2.2 CONVENTIONAL SEIZURE DETECTION METHODS Pioneering research on the automatic detection of seizure in epileptic patients has evolved into attempts to detect the seizure in epileptic EEG. EEG analysis was mainly based on two significant characteristics extracted from EEG: frequency and amplitude (Carlos Guerrero Mosquera et al., 2010). Gotman and Gloor (1976) paved way for decomposing EEG into half waves. Their method compares relative amplitude in the decomposed half waves during spikes and short waves to that during background. These approaches, which include EEG epoch analysis, spike detection, parametric models, methods of clustering, quantitative analysis, and spectral EEG signal analysis, assume quasi-stationarity, require long recordings and present relatively high false detection rates due to the presence of typical EEG artifacts (Carlos Guerrero-Mosquera et al., 2010). The time domain techniques include Instantaneous power, Energy of EEG (Litt et al., 2001), Template matching (Qu and Gotman 1997), correlation, Average amplitude and duration (Qu and Gotman 1997), and some frequency domain techniques include power spectral density (Roessgen et al., 1998) and dominant frequency (Qu and Gotman 1997) were studied. These features were calculated from Gotmans wave decomposition method which breaks the EEG down into half-waves and perform some smoothing. Feature vectors from both ictal and non-ictal EEG epochs were used as templates in the classifier. New EEG patterns were classified according to the closest template vector in feature space. Using patient specific classifiers the paper claims a 100% detection rate with a false alarm rate of 0.02per hour.

11 However, attempts to use this method as a generic system gave very poor results. The simplest linear statistics is used for investigating the dynamics underlying the EEG is the variance of the signal, calculated in consecutive non overlapping windows (McSharry et al., 2003). Esteller et al., (1999) suggest measuring the energy of the signal in consecutive windows of the EEG signal. Subasi et al., (2005) used Fast Fourier transform (FFT) and Auto Regressive (AR) model with Maximum Likelihood Estimation (MLE) as features. These methods give frequency and energy information but they do not provide temporal information about when seizure discharges begin. The inability to accurately detect and quantify these changes and to automatically and efficiently analyze such long-time series has limited the understanding of epilepsy as well as the application of automatic detection systems in the clinical practice. Conventional signal processing methods are much simpler and offer realtime detection with relative ease over other methods like non-linear dynamic methods. But due to the presence of numerous transients and artifacts no definite information is available as to the generation of seizures in humans. Therefore more sophisticated methods which yield increasingly accurate results like wavelet transform technique can be used to analyse the properties of EEG manifestations of epilepsy. 2.3 SEIZURE DETECTION USING WAVELET TRANSFORM TECHNIQUES Motivated by the time-frequency changes during seizures, signal decomposition has been one of the popular methods for seizure detection. Attempts were made by Gabor et al., (1996) using wavelets to extract features for feeding a neural network for seizure detection. Adeli et al., (2003) applied discrete Daubechies with order four and harmonic wavelets for analysis of

12 epileptic EEG records. They found that the analysis of EEG signals by wavelet transform improved understanding of the mechanisms causing epileptic disorders, and this algorithm can be extended to create computational models for automatic detection of epileptic discharges. Subasi (2006) used a new approach based on neural network and fuzzy logic technologies for detection of epileptic seizures. EEG signals were decomposed into the frequency sub-bands using discrete wavelet transform (DWT) and db4 wavelet. Then these sub-band frequencies were used as an input to a DFNN with two discrete outputs: normal and epileptic. He concluded that the accuracy rate of DFNN model were higher than that of neural network model. Recently epileptic seizure detection research has employed wavelet based methods with some success in detecting epileptic seizures. Subasi (2007) extracted the features using the wavelet Transform (WT) and classified using ANFIS. The classification results for specificity were 93.7% and sensitivity was 94.3%, respectively. Patnaik et al. (2008) used Wavelet transform for feature extraction and obtained the statistical parameters from the decomposed wavelet coefficients. A feed-forward back propagating artificial neural network (ANN) is used for classification and the average specificity and sensitivity was shown to be 99.12% and 91.29% respectively. Hasan Ocak (2009) used approximate entropy (ApEn) and discrete wavelet transform (DWT) for the analysis of EEG signals and was able to detect seizures with over 96% accuracy. Without DWT as pre processing step, it is shown that the detection rate was reduced to 73%. The literature shows that researchers used Daubechies 2 to Daubechies 8 for seizure detection. There are a lot of constraints in wavelet-based analysis of the EEG signal. The problem is to how to select a suitable wavelet function to decompose an EEG. The arbitrary choice of the wavelet function is not desirable. It is possible that the mother wavelets suggested by the various investigators, may not be

13 optimum (Clement et al., 2003). Hence selection of significant mother wavelet and features are carried out in this study using PCA. 2.4 PRINCIPAL COMPONENT ANALYSIS Principal component analysis (PCA) is used to make a classifier system more effective, having less computational complexity, and less time consumption. For this aim, before classification, PCA method is used for dimensionality reduction of EEG signals features vector. PCA is based on the assumption that most information about the classes is contained in the directions along, which the variations are the largest. PCA is a statistical method used to transform the input space into a new lower dimensional space and has been used to remove the artefacts from EEG (Lager Lund et al., 1997). The first principal component accounts for much of the variability in the data and each succeeding component accounts for the remaining variability. The uncorrelated variables are linear combinations of the original variables and the last of these variables can be removed with minimum loss of real data in order to identify new meaningful underlying variables (Aguado et al., 2008). PCA of high-dimensional data is an ingredient of many signal processing applications and strives to extract the principal directions in the data space where the variance of the data is maximal, thus paving the way for dimension reduction and data compression (Burstyn 2004). This method is especially suited for high-dimensional data, since the computation of the large covariance matrix can be avoided, and for the tracking of Nonstationary data. The principal components correspond to the directions in which the projected observations have the largest variance which are the eigenvectors of the empirical covariance matrix (Tharrault et al., 2008).

14 PCA technique has been investigated before by researchers for signal and image processing (Kavitha et al., 2009, Salaffi et al., 2000, Marek et al., 2003 and Arnaz et al., 2004). PCA is also used to explore overall rank orders for treatment, and relationships between outcomes with classes of asthma medication (Jenkins et al., 2005). Herbert Witte et al. (2003) put forward as to how can one optimally select the set of parameters that are most relevant to seizure detection. PCA is applied for data reduction in EEG applications like classification of mental task, classification of alcoholic groups, determining drowsiness of driving persons and feature enhancement for seizure detection (Nazari et al., 2009, Pari Jahankani et al., 2008, Mu Li et al., 2008, Samanwoy et al., 2008). Because of its ability to discriminate directions with the largest variance in a data set, the suitability of PCA for identifying the most representative features as inputs to a classification scheme and for selecting the significant mother wavelet is investigated in this work. 2.5 ARTIFICIAL NEURAL NETWORK CLASSIFIER Artificial Neural Networks (ANN) are claimed to be systems that enable the execution of a particular task without the need for a prior knowledge of it. A large variety of tracings have been studied using ANN. These include Electro-cardiogram (ECG), Electro-Myogram (EMG), Electro-Encephalogram (EEG), arterial pulse waveforms and evoked potentials. These methods not only detect a seizure, but also monitor and classify its evolution. Recent advances in the field of neural networks have made them attractive for analyzing signals. The application of neural networks has opened a new area for solving problems not resolvable by other signal processing techniques (Basheer and Hajmeer 2000, Guler and Ubeyli 2005, Sujatha et al., 2008). Gholam et al. (2006) have compared different feed forward neural network architectures for ECG signal diagnosis. The authors have considered a two stage feed forward neural network for

15 classifying six different heart conditions. The training of the selected architecture was obtained from a data base and the authors prove that the selected architecture was 93% accurate. Benardos and Vosniakos (2007) have optimized feed forward neural network for different engineering applications. The authors have proposed a methodology for determining the best architecture for both training and generalization problems. Spectral analysis of the EEG signals produces information about the brain activities. However, artificial neural networks (ANNs) may offer a potentially superior method of EEG signal analysis to the spectral analysis methods. In contrast to the conventional spectral analysis methods, ANNs not only model the signal, but also make a decision as to the class of signal (Subasi 2005, Subasi and Ercelebi 2005, Subasi 2007, Sujatha et al., 2008). The major disadvantages of Neural Network are its relatively slow convergence rate (Zweiri et al., 2003) and solutions being trapped at local minima (Kuok et al., 2010). Basically, Neural Network with backpropagation learning is a hill climbing technique which is like running the risk of being trapped in local minima, where every small change in synaptic weight increases the cost function (Kuok et al., 2010). Therefore, the Neural Network will get trapped where there is another set of synaptic weights for which the cost function is smaller than the local minimum in weight space. This made termination of the learning process at local minima by backpropagation is undesirable (Kuok et al., 2010). 2.6 RBFNN CLASSIFIER Radial basis function (RBF) neural networks are based on supervised learning. RBF networks are good at modelling nonlinear data and can be trained in one stage rather than using an iterative process as in Multilayer Layer Perceptron and also learn the given application quickly. A radial basis function network, a highly versatile and easily implementable classifier was chosen to facilitate the selection of decisive features. Enrico

16 (2003) surveyed the different interpretations of radial basis function neural networks in order to emphasize their relevant properties and concluded that medical applications usually used radial basis function neural networks as an ANN. Recently, there is a growing interest in the use of RBFNN for its short training time and being guaranteed to reach the global minimum of error surface during training (Liu et al., 2008). It has superiorities in function approximation and learning speed (Qu et al., 2007). Sujatha et al. (2008) has used radial basis network for Classification of spirometer data. Samanwoy et al. (2008) has used RBFNN for classification of epileptic seizures. The main differences between MLP and RBF networks are that the connection between the input and the hidden layers are not weighted and transfer function of the hidden layer nodes are radially symmetric in RBFNN. The advantage of this network is that the learning process can be faster than the back propagation networks, although the accuracy of the solution is highly dependent on the range and quality of data. Additionally, this network is inherently well suited for classification, because it naturally uses unsupervised learning to cluster the input data (Maqsood and Abraham, 2007). 2.7 PSONN CLASSIFIER Particle swarm optimization has been used to solve many optimization problems since it was proposed by Kennedy and Eberhart in 1995. Eberhart and Xiaohui (1999) present methods for the analysis of human tremor using particle swarm optimization. Two forms of human tremor are addressedμ essential tremor and Parkinson s disease. Particle swarm optimization is used to evolve a neural network that distinguishes between normal subjects and those with tremor. It is reported that PSO converges faster than the back-propagation learning algorithm in ANN s (Gudise et al., 2003). It revealed that PSO is a promising method to train the ANN. It is faster and gets better results in many cases. Very few literatures

17 are found for classification of epileptic seizures using Particle swarm optimization neural network. Hiram Firpi et al. (2007) used PSONN for construction optimum feature of High frequency epileptiform oscillations and detecting seizure without training process. In (Mostafa and Hamid, 2008) a complex clinical problem is described that has been addressed using PSO data mining. A large group of temporal lobe epilepsy patients are studied to find the best surgery candidates. The PSONN is chosen to overcome the problem of termination of learning process at local minima. PSONN improves the convergence rate of Neural Network and avoid solutions being trapped at local minima (Kuok et al., 2010). The PSO is made up of particles, where each particle has a position and a velocity. The idea of PSO in NN is to get the best set of weight (or particle position) where several particles (problem solution) are trying to move to the best solution and this will avoid the solution trap at local minima (Van den Bergh and Engelbrecht 1999). This will improve the performance of the network. 2.7 ANFIS CLASSIFIER Fuzzy set theory plays an important role in dealing with uncertainty when making decisions in medical applications. First introduced by Zadeh (1965) fuzzy logic and fuzzy set theory are employed to describe human thinking and reasoning in a mathematical framework. These intelligent computational methods offer real advantages over conventional modelling, especially when the underlying physical relationships are not fully understood. In recent years, the integration of neural networks and fuzzy logic has given birth to new research into neuro-fuzzy systems. Neuro-fuzzy systems have the potential to capture the benefits of both these fields in a

18 single framework. Neuro-fuzzy systems eliminate the basic problem in fuzzy system design (obtaining a set of fuzzy if then rules) by effectively using the learning capability of an ANN for automatic fuzzy if then rule generation and parameter optimization. As a result, those systems can utilize linguistic information from the human expert as well as measured data during modelling. Such applications have been developed for signal processing, automatic control, information retrieval, database management, computer vision and data classification (Subasi 2006, 2007). A specific approach in neuro-fuzzy development is the adaptive neuro-fuzzy inference system (ANFIS), which has shown significant results in modelling nonlinear functions. In ANFIS, the membership function parameters are extracted from a data set that describes the system behaviour. The ANFIS learns features in the data set and adjusts the system parameters according to a given error criterion (Subasi 2007). Successful implementations of ANFIS in biomedical engineering have been reported, for classification, modelling and controlling real systems (Ubeyli and Guler 2005) and data analysis (Ubeyli and Guler 2005). One disadvantage of the ANFIS method is that; the complexity of the algorithm is high when there are more than a number of inputs fed into the system. However, when the system reaches an optimal configuration of membership functions, it can be used efficiently against large datasets. Highly efficient neuro-fuzzy systems such as ANFIS have the following characteristics such as fast learning, on-line adaptability, self-adjusting with the aim of obtaining the small global error possible and small computational complexity. This will help in improving the accuracy of the classifier.

19 In this work, an attempt has been made to analyse the diagnostic relevance of seizure investigations using Wavelet transformation, Principal Component Analysis and Intelligent Classification BPA, RBFNN, ANFIS and PSONN.