Weighted majority voting based ensemble of classifiers using different machine learning techniques for classification of EEG signal to detect epileptic seizure.
Povzetek: Sistem s pomocjo strojnega ucenja iz EEG signalov zazna epilepticni napad.
Keywords: EEG signal, epilepsy, classification, machine learning
Epilepsy is a persistent disorder of mental ability that has an abnormal EEG signal flow , which manifests in the disoriented human behaviour. In this world, around 40 to 50 million people are mostly affected by this disease , Many people also call it as fits that causes loss of memory and interruption in consciousness, strange sensations, and significant alteration in emotions and behaviour. Research related to the epilepsy disease is basically used for differentiating between letal (seizure period) and Interictal (period between seizures) EEG signals. Hence, the transition from preictal to ictal state for an epileptic seizure contains a gradual change from a chaotic to ordered wave forms. Moreover, the amplitude of the spikes does not necessarily signify the harshness of seizures ,
The difference between the seizure and the common artifact is quite easy to recognize where generally the seizures within EEG measurement  have a prominent spiky, repetitive, transient, or noise-like pattern. Hence, unlike other general signals, for an untrained observer, the EEG signal is quite difficult for understanding and analysis. The recording of these signals is mostly done by the help of a set of electrodes placed on the scalp using 10 to 20 electrode placement systems. The system incorporates the electrodes, which are placed with a specific name based on specific parts of the brain, e.g., Frontal Lobe (F), Temporal Lobe (T), etc. These naming and placement schemes have been discussed in more details in .
For the facilitation and effective diagnosis of the epilepsy, several neuro-imaging techniques such as functional magnetic resonance imaging (fMRI) and position emission tomography (PET) are used. An epileptic seizure can be characterized by paroxysmal occurrence of synchronous oscillation. This impersonation can be separated into two categories depending on the extent of involvement in different brain regions such as focal or partial and generalized seizures . Focal seizure also known as epileptic foci are generated at specific sphere in the brain. In contrast to this, generalized seizures occur in most parts of the brain.
A careful analysis and diagnosis of EEG signals for detecting epileptic seizure in the human brain usually contribute to a substantial insight and support to the medical science. Thus, EEG is quite a beneficial as well as a cost effective way for the study of epilepsy disease. For generalized seizure, the duration of seizure can be easily detected by naked eyes whereas it is very difficult to recognize intervals during focal epilepsy.
Classification is the most useful and functional technique  for properly detecting the epileptic seizures in EEG signals. Classification being a data mining technique is generally used for pattern recognition . Other than that, it is used to predict a group membership for unknown data instances. Hence, by designing a classifier model using different machine learning approaches, we can identify epileptic seizures in EEG brain signal. Pre-processing is considered as one of the necessary task to get it into a proper feature set format, even before considering the classification of raw EEG signal. Generally, the data sample of EEG is not linearly separable. Thus, to obtain non-linear discriminating function for classification, we are using machine learning techniques. Moreover, the case of limiting our focus on machine learning approaches is because of their capability and efficiency for smooth approximation and pattern recognition. However, there are learners who are performing very poorly; hence this work makes an extension over individual learners by ensembling classifier models based on weighted majority voting.
In this analytical study, a publicly available EEG dataset have been considered that is related to epilepsy for all experimental evaluations. Based on this there are mainly two phases of the epileptic seizure detection process that are carried out. The first phase is to analyse the EEG signal and convert it into a set of samples with set of features. The second phase is to classify the already processed data into different classes such as epilepsy or normal.
The rest of the subdivisions of this paper are organized as follows. Section 2 describes the recording and pre-processing of EEG signals through discrete wavelet transform. Some classification methods based on machine learning techniques are described in Section 3. Section 4 discusses the ensemble of classifiers. In Section 5, the detail of empirical work and analysis of results obtained by different machine learning models and ensemble of classifiers. Section 6 draws the conclusions and suggests possibilities for future work.
2 Methods for dataset preparation
In the present work we have collected data from  which is a publicly available database  related to diagnosis of epilepsy. This resource provides five sets of EEG signals. Each set contains reading of 100 single channel EEG segments of 23.6 seconds duration each. These five sets are described as follows. Datasets A and B are considered from five healthy subjects using a standardized electrode placement system. Set A contains signals from subjects in a slowed down state with eyes open. Set B also contains signal same as A but ones with the eyes closed. The data sets C, D and E are recorded from epileptic subjects through intracranial electrodes for interictal and ictal epileptic activities. Set D contains segments recorded from within the epileptogenic zone during seizure free interval. Set C also contains segments recorded during a seizure free interval from the hippocampal formation of the opposite hemisphere of the brain. The set E only contains segments that are recorded during seizure activity. All signals are recorded through the 128 channel amplifier system. Each set contains 100 single channel EEG data. In all there are 500 different single channel EEG data. In Subsection 2.1, we illustrate how to crack these signals using discrete wavelet transform  and prepare several statistical features to form a proper sample feature dataset.
2.1 Wavelet transform
This is a modern signal analysis technique which overcomes the limitations of other transformation techniques. Other transformation methods may include Fast Fourier Transform (FFT), Short Time Fourier Transform (STFT), etc. The major restrictions of these techniques are the analysis limits to stationary signals. These are not effective for analysis of transient signals such as EEG signal. Transient in the sense the frequency is changing rapidly with respect to time. Then, with the help of wavelet coefficients  we can analyse transient signals easily and also efficiently. Wavelet transform can be of two types: Continuous Wavelet Transform (CWT)  and Discrete Wavelet Transform (DWT) [12, 13],
2.1.1 Continuous wavelet transform
It is defined as:
CWT(a, b) = [[integral].sup.[infinity]].sub.-[infinity]] x(t) x [[phi].sup.*.sub.a,b](t)dt, (1)
where, x(t) represents the original signal, a and b represents the scaling factor and translation along the time axis, respectively. The * symbol denotes the complex conjugation and [[phi].sup.*.sub.a,b] is computed by scaling the wavelet at time band scale a.
[[phi].sup.*.sub.a,b](t) = 1/[square root of [absolute value of a]] [phi] (t - b/a), (2)
where, [[phi].sup.*.sub.a,b](t) stands for the mother wavelet. In CWT it is presumed that the scaling and translation parameters a and b changes continuously. But the main disadvantage of CWT is the calculation of wavelet coefficients for every possible scale can result in a large amount of data. It can surmount with the help of DWT.
2.1.2 Discrete wavelet transform
It is almost same as CWT except that the value of a and b does not change continuously. It can be defined as:
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (3)
where a and b of CWT are replaced in DWT by [2.sup.p]&[2.sup.q]
It is a transformation technique that provides a new data representation which can spread to multiple scales. Therefore, the analysis of transforming signal can be performed at a multiple resolution scale. DWT is performed by successively passing the signal through a series of high pass and low pass filters producing a set of detail and approximation coefficients. This generates a decomposing tree known as Mallat's decomposition tree. In this analytical work, the raw EEG signals that have been picked up from web resources is decomposed using DWT  available as a toolbox in MATLAB. This signal is decomposed using the Daubechis Wavelet function of order 2 up to 4 levels . Thus, it produces a series of wavelet coefficient like four detailed coefficients (Dl, D2, D3, and D4) and an approximation signal (A4). Figures 1, 2, and 3 provides a snapshot of this decomposition of a single channel EEG recording from set A, D, and E respectively.
Later, on this decomposition some of the statistical features of many have been extracted from the signals such as Minimum (MIN), Maximum (MAX), MEAN, and Standard Deviation (SD). Figure 4 is a sample output of the MATLAB toolbox showing different features of a single channel EEG recording from set A and set E. The same procedure can be followed for all other EEG recordings to make a perfect set. So, after this level, we are ready with a sample feature dataset of order 500 by 20 matrixes as shown in Table 1. Then it can be further used for classification tasks.
In addition to DWT, there are other feature extraction techniques  that can also be used successfully to extract features from the raw EEG signal. These techniques may include Wavelet Packet Decomposition (WPD), Principal Component Analysis (PCA), Lyapunov Exponent, ANNOVA test, etc.
3 Machine learning classifiers
Machine learning (ML) is a set of computerized techniques, which focus to automatically learn to recognize complex patterns and make intelligent decisions based on data. ML has proven its ability to uncover hidden information present in large complex dataseis. Using ML, it is possible to cluster similar data, classify, or to find association among various features [14, 15]. In the context of EEG signal analysis, ML is the application of algorithms for extracting patterns from EEG signals . However, there are other steps also carried out e.g., data cleaning &preprocessing, data reduction & projection, incorporation of prior knowledge, proper validation and interpretation of results while analysing EEG signals. EEG analysis has number of challenges which make it suitable for machine learning techniques .
* EEG comes in large databases.
* EEG recordings are very noisy.
* EEG signals have large temporal variance. Some popular machine learning approaches are neural networks, evolutionary algorithms, fuzzy theory, and probabilistic learning. In this analytical work, our focus is restricted with neural networks, its variants and support vector machines for classification EEG signals.
3.1 Multilayer perceptron neural network (MLPNN)
Artificial neural network simulates the operation of a neural network of the human brain and solves a problem. Generally, single layer Perceptron neural networks are sufficient for solving linear problems, but nowadays the most commonly employed technique for solving nonlinear problems is Multilayer Perceptron Neural Network (MLPNN) . It can hold various layers such as one input and one output layer along with at least one hidden layer. There are connections between different layers for data transmission. The connections are generally weighted edges to add some extra information's to the data and it can be propagated through different activation functions.
The heart of designing an MLPNN is the training of network for learning the behaviour of input-output patterns. In this work, we have designed an MLPNN with the help of a Java Encog framework. This network is trained with the help of three popular training algorithms such as Back- propagation (BP) , Resilient Propagation (RPROP) , and Manhattan Update Rule (MUR).
Back-propagation training algorithm [5, 19, and 20] is different from other algorithms in terms of the weight updating strategies. In back propagation [21, 22, 23], generally weight is updated by the equation (4) [24, 25, 26],
[w.sub.ij](k + 1) = [w.sub.ij](k) + [DELTA][w.sub.ij](k), (4)
where in regular gradient decent
[DELTA][w.sub.ij](k) = -[eta][partial derivative]E/[partial derivative] [w.sub.ij](k) (5)
with a momentum term
[DELTA][w.sub.ij](k) = -[eta][partial derivative]E/[partial derivative] [w.sub.ij](k) + [mu][DELTA] [w.sub.ij](k - 1) (6)
Resilient propagation  is a supervised training algorithm for feed forward neural network. Instead of magnitude, it takes into account only the sign of the partial derivative, or gradient decent and acts independently on each weight. The advantage of RPROP algorithm is that it needs no setting of parameters before applying it. The weight updating is done according to the equation (7). Equation 4 is same for the RPROP for weight update.
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (7)
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]. (8)
where, [S.sub.ij] = [partial derivative]E/[partial derivative] [w.sub.ij](k - 1) x [partial derivative]E/[partial derivative] [w.sub.ij](k) and [[eta].sub.+] = 1.2 and [[eta].sub.-] = 0.5.
Manhattan update rule also works similar to RPROP and only uses the sign of the gradient and magnitude is discarded. If the magnitude is zero, then no change is made to the weight or threshold value. If the sign is positive, then the weight or threshold value is increased by a specific amount defined by a constant. If the sign is negative, then the weight or the threshold value decreases by a specific amount defined by a constant. This constant must be provided to the training algorithm as a parameter.
3.2 Variants of neural network
In addition to MLPNN, many different types of neural networks have been developed over the year for solving problems with varying complexities of pattern classification. Some of these includes: Recurrent Neural Network (RNN) , Probabilistic Neural Network (PNN) , and Radial Basis Function Neural Network (RBFNN) ,
3.2.1 Recurrent neural network
RNN  is a special type of artificial neural network having a fundamental feature is that the network contains at least one feedback connection , so that activation can flow round in a loop. This feature enables the network to do temporal processing and learn the patterns. The most important common features shared by all types of RNN [46, 47] are, they incorporate some form of Multilayer Perceptron as sub-system. They implement the non-linear capability of MLPNN [48, 49] with some form of memory. In this research work the ANN architecture, we have implemented for modelling and classifying is the Elman Recurrent Neural Network (ERNN). It was originally developed by Jeffrey Elman in 1990. The Back-Propagation through time (BPTT) learning algorithm is used for training [50, 51], which is an extension of Back-propagation that performs gradient decent on a complete unfolded network.
If a network training sequence starts on time [t.sub.0] and ends at time [t.sub.1]; the total cost function can be calculated as:
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII], (9)
and the gradient decent weight update can be calculated as:
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]. (10)
3.2.2 Probabilistic neural network
PNN was first proposed by Specht in 1990. It is a classifier that maps input patterns in a number of class levels. It can be forced into a more general function approximator. This network is organized into a multilayer feed forward network with input layer, pattern layer, summation layer, and the output layer. PNN  is an implementation of a statistical algorithm called kernel discriminant analysis. The advantages of PNN are like; it has a faster training process as compared to Back-Propagation. Also, there are no local minima issues. It has a guaranteed coverage to an optimal classifier as the size of the training set increases. But it has few disadvantages like slow execution of the network because of several layers and heavy memory requirements, etc.
In PNN  a Probability Distribution Function (PDF) is computed for each population. An unknown sample s belongs to a class p if,
PD[F.sub.p](s)> PD[F.sub.q](s)[for all]p [not equal to] q, (11)
where, PD[F.sub.k](s) is the PDF for class k.
Other parameters used are Prior Probability - h, Misclassification Cost - c, so the classification decision becomes,
[h.sub.p][c.sub.p]PD[F.sub.p](s) > [h.sub.q][c.sub.q]PD[F.sub.q](s)[for all]p [not equal to] q (12)
PDF for a single sample can be calculated by using the formula,
PD[F.sub.k](s) = 1/[sigma]W (s-[s.sub.k]/[sigma]), (13)
where s - Input (unknown), [s.sub.k] - [k.sup.t]h sample, W-weighting function, [sigma] - smoothing parameter. PDF for a single population can be calculated by taking the average of PDF of n samples.
PD[F.sup.n.sub.k](s)= 1/n[sigma][[summation].sup.n.sub.1]W (s - [s.sub.k]/[sigma]) (14)
From the result table, it is experimentally proved that for epilepsy identification in EEG signal, PNN gives the most accurate result by taking minimum amount of time.
3.2.3 Radial basis function neural network
RBF networks are also a type of feed-forward network, trained by using a supervised training algorithm. The main advantage of RBF network is, it has only one hidden layer. The RBF network, usually trains much faster than back-propagation networks. This kind of network is less susceptible to problems with non-stationary inputs because of the behaviour of radial basis function hidden units. The general formula for the output of RBF network  can be represented as follows, if we consider the Gaussian function as the basis function.
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (15)
where x, y (x), [c.sub.i], [sigma], and M denotes input, output, center, width, and number of basis function centered at [c.sub.i], similarly [w.sub.i] denotes weights.
For this work, we have constructed a Radial Basis Function Network by taking into consideration of the Gaussian function as the basis function with a prefixing of randomized centres and widths.
3.3 Support vector machine (SVM)
SVM is the most widely used machine learning technique based pattern classification technique nowadays. It is based on statistical learning theory and was developed by Vapnik in the year 1995. The primary aim of this technique is to project nonlinear separable samples onto another higher dimensional space by using different types of kernel functions. In late years, kernel methods have received major attention, especially due to the increased popularity of Support Vector Machines . Kernel functions play a significant role in SVM [28, 29] to bridge from linearity to nonlinearity. Least square SVM  is also an important SVM technique that can be applied for classification task . Extreme learning Machine and Fuzzy SVM [32, 33, 34] and Genetic algorithm tuned expert model  can also be applied for the purpose of classification.
In this analytical work, we have evaluated three different types of kernel functions , i.e., Linear, Polynomial, and RBF kernel , Linear kernel is the simplest kernel function available. Kernel algorithm using a linear kernel is often equivalent to their non-kernel counterparts , From the result table it can be clearly understood that for a classification problem consisting of only sets A & E or D & E, is providing 100% accuracy. But it is not able to classify properly by considering sets A + D & E.
Polynomial kernel is a non-stationary kernel. This kernel function can be represented as given in equation:
K(x,y) = [([alpha][x.sup.T]y+c).sup.d], (16)
where [alpha], c and d denotes slope, any constant, and degree of polynomial, respectively.
Somehow this kernel function [38, 39] is better as compared to linear kernel function. However, the RBF kernel function  has been proven as the best kernel function used for this application, which can classify different groups with 100% accuracy with a minimum time interval.
4 Ensemble of machine learning classifiers
From the empirical analysis we conclude that there some classifier models e.g., SVM and PNN outperforms all other techniques such as MLPNN, RNN, and RBFNN. Hence, to boost the poorly performer as compared to SVM and PNN, we have proposed a model for an ensemble based classifier that combines the above three techniques and improves the accuracy of classification for epileptic seizure detection. This proposed ensemble technique uses a weighted majority vote for classification. The main goal of an ensemble method is to combine the efficiency of several basic classifier models and build a learning algorithm to improve the robustness over a single classification technique. Here we have combined the classification results of MLPNN, RNN, and RBFNN and constructed an ensemble classifier. This classifier uses a weighted majority based vote for classification. Generally in majority based vote the class label of a sample is decided by the class label that is classified by maximum number of classifiers. Let there are two classes (1 and 2) and three classifiers (clfl, clf2, and clf3). Let for a sample clfl and clf2 classifies to class 2 whereas clf3 classifies to class 1 then the ensemble of classifiers classifies the sample to class 2.
Figure 5 describes the architecture of ensemble of classifiers to detect epileptic seizure. It combines the output of three different classifiers such as classifier 1 (MLPNN), classifier 2 (RNN), and classifier 3 (RBFNN) based on a weighted majority voting mechanism. Weight parameter has been added to give different weightage to different classifiers based on their performance. For this we have collected the predicted class probabilities for each classifier and multiplied it with classifier weight and the average is taken. Based on this weighted average probabilities, the class label has been assigned. Here we have taken simple weighted majority technique where same weight is assigned to each class label which is 1/k, where k is the number of class labels.
5 Empirical study
This section gives an empirical study on different classification techniques based on machine learning approach for detection of epilepsy in EEG brain signal. Various experiments are done to validate this empirical study. The Machine Learning based classifiers are proved as the most efficient way for pattern recognition. It aids to design models that can learn from some previous experience (known as training) and further it can be able to recognize appropriate patterns for unknown samples (known as testing).
All experiments for this research work are performed using a powerful Java Framework known as Encog  developed by Jeff Heaton and his team. Currently we are using Encog 3.2 Java framework for all experimental result evaluation. This is the latest version and it supports almost all the features of machine learning techniques. Along with this framework, there are a lot of packages, classes and methods that have been defined to support the experimental evaluations. Java is the most potent and efficient language nowadays. The Tightness of the experimental works can be verified easily using this language. There are almost nine different machine learning algorithms that have been implemented for EEG signal classification for epileptic seizure detection.
5.1 Environment and parameter setup
The Encog Java framework provides a vast circle of library classes, interfaces, and methods that can be utilized for designing different machine learning based classifier models. There are lists of parameters (as shown in Table 2) required to be set for smooth and accurate execution of models.
5.2 Performance measures and validation techniques
Here, we have hashed out about the performance of all machine learning based classifiers for classifying EEG signal. The different measures used for performance estimation are: Specificity (SPE), Sensitivity (SEN), Accuracy (ACC), and Time elapsed for execution of models. From the evaluation result given in Table 3, it is clear that MLPNN with resilient propagation is the most efficient training algorithm both in considerations of accuracy as well as the amount of time needed to execute the programs in all different setting such as A&E, D&E, and A+D & E. This MLPNN technique can be compared with other machine learning techniques. In this work all the experimental evaluations are validated using k- fold cross validation where value of k is taken as 10. So the total dataset has been divided into 10 folds. Each fold is constructed with samples having almost same number from each class labels. In each iteration one fold is considered for testing the classifier and rest of the folds are taken for training the classifier. This is a very efficient validation technique as it rules out all possibilities of misclassification and gives an accurate efficiency measure.
Table 4 shows a comparison of different kernel types used for classification using Support Vector Machine (SVM). It is the most powerful and efficient machine learning tool for designing classifier model. This table clearly shows a very good result for SVM with RBF kernel.
Table 5 defines a list of experiments led by studying different forms of Neural Network, such as Radial Basis Function Neural Network, Probabilistic Neural Network, and Recurrent Neural Network. It suggests that the effectiveness of using PNN for classification of EEG signal for detecting epileptic seizures is promising.
5.3 Comparative analysis
Table 6 gives a detail empirical analysis of the performance of different classification techniques based on machine learning approaches. As discussed above in this experimental evaluation we have used 10-fold cross validation to validate the results of classification.
Table 7 gives the result of experimental evaluation for the proposed ensemble technique and results for individual classification techniques. Figure 6 gives a graphical representation of comparison of different individual machine learning techniques with ensemble based classification technique. These experimental result shows there is a remarkable increase in the accuracy for case 3 (A+D & E) along with other two cases.
6 Conclusions and future study
By classifying the EEG signals collected from different patients in different situations, detection of the epileptic seizure in EEG signal can be performed. Thus classification can be accomplished by using different machine learning techniques. In this work, the efficiency and functioning pattern of different machine learning techniques like MLPNN, RBFNN, RNN, PNN, and SVM for classification of EEG signal for epilepsy identification have been compared. Further, the tool MLPNN uses three training algorithms like BACKPROP, RPROP, and Manhattan Update Rule. Similarly, three kernels such as Linear, Polynomial, and RBF kernels are used in SVM. Hence, this comparative study clearly shows the differences in the efficiency of different machine algorithms with respect to the task of classification. Moreover, from the experimental study, it can be concluded that SVM is the most efficient and powerful machine learning technique for the purpose of classification of the EEG signal. Also, SVM with RBF kernel provides the utmost accuracy in all settings of the classification task. Besides this, PNN is a good contender for SVM for this specific application. But compared to SVM, PNN requires some extra overhead in setting the parameters. Also our proposed ensemble of classifiers based on weighted majority voting that combines the efforts of three different poorly performer classifiers such as MLPNN, RNN and RBFNN is enhancing the performance in different cases. Our continuous efforts in this area of research (both theoretical and experimentales marching with lots of issues and will go ahead in future by considering the real cases with state-of-the-art meta-heuristic optimization techniques.
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Sandeep Kumar Satapathy
Department of Computer Science and Engineering
Siksha 'O' Anusandhan University, Khandagiri, Bhubaneswar-751030, Odisha, India
Alok Kumar Jagadev
School of Computer Engineering
KIIT University, Bhubaneswar-751024, Odisha, India
Department of Information and Communication Technology
Fakir Mohan University, Vyasa Vihar-756019, Balasore, Odisha, India
Received: January 6, 2017
Caption: Figure 1: Single channel EEG signal decomposition of set A using db-2 up to level 4.
Caption: Figure 2: Single channel EEG signal decomposition of set D using db-2 up to level 4.
Caption: Figure 3: Single channel EEG signal decomposition of set E using db-2 up to level 4.
Caption: Figure 4: Statistical features extraction from signals after decomposition.
Caption: Figure 5: Proposed framework for ensemble based classifier for detection of epileptic seizure.
Caption: Figure 6: Performance comparison of different machine learning techniques with ensemble based classifier for detection of epileptic seizure.
Table 1: Structure and dimension of dataset for EEG signal classification. Seizure Size of Class 0 Class 1 Detection Sample Sets Set 1- (A & E) 200x20 100x20 100x20 Set 2- (D & E) 200x20 100x20 100x20 Set 3- (A + D & E) 300x20 200x20 100x20 Table 2: Lists of parameters for models execution. Classification Required Parameters and Techniques Values MLPNN/BP Activation Function--Sigmoid Learning Rate = 0.7 Momentum Coefficient = 0.8 Input Bias--Yes MLPNN/RPROP Activation Function--Sigmoid Learning Rate = NA Momentum Coefficient = NA Input Bias--Yes MLPNN/MUR Activation Function--Sigmoid Learning Rate = 0.001 Momentum Coefficient = NA Input Bias--Yes SVM/Linear Kernel Type--Linear Penalty Factor = 1.0 SVM/Polynomial Kernel Type--Polynomial Penalty Factor = 1.0 SVM/RBF Kernel Type--Radial Basis Function Penalty Factor = 1.0 PNN Kernel Type--Gaussian Sigma low--0.0001 (Smoothing Parameter) Sigma high--10.0 (Smoothing Parameter) Number of Sigma--10 RNN Pattern Type--Elman Primary Training Type--Resilient Propagation Secondary Training Type--Simulated Annealing Parameters for SA Start Temperature--10.0 Stop Temperature--2.0 Number of Cycles--100 RBFNN Basis Function--Inverse Multiquadric Center& Spread Selection--Random Training Type- SVD (Singular Value Decomposition) Table 3: Experimental evaluation result of MLPNN with different training algorithms. Multi-Layer Perceptron Neural Network with Cases different Propagation Training Algorithms for Seizure Back-Propagation Resilient-Propagation Types SPE SEN ACC TIME SPE SEN ACC TIME Case1 100 90.09 94.5 16.52 99.009 100 99.5 2.846 (A,E) Case2 100 83.33 90 22.22 99.009 100 99.5 2.547 (D,E) Case3 100 86.95 92.5 23.12 95.85 85.98 92.33 14.79 (A+D,E) Multi-Layer Perceptron Neural Network with different Cases Propagation Training Algorithms for Seizure Manhattan-Update Rule Types SPE SEN ACC TIME Case1 97.29 77.77 85 7.541 (A,E) Case2 55.68 78.78 60 7.181 (D,E) Case3 93.78 82.24 89.66 14.85 (A+D,E) Table 4: Experimental evaluation result of SVM with different kernel types. Cases Support Vector Machine with different Kernel Types for Seizure Linear Polynomial Types SPE SEN ACC TIME SPE SEN ACC TIME Case1 100 100 100 2.127 100 100 100 2.101 (A,E) Case2 100 100 100 1.904 100 100 100 1.902 (D,E) Case3 90.67 76.63 85.66 11.61 100 99.009 99.66 7.24 (A+D,E) Cases Support Vector Machine with for different Kernel Types Seizure Types RBF SPE SEN ACC TIME Case1 100 100 100 2.002 (A,E) Case2 100 100 100 2.021 (D,E) Case3 100 100 100 2.511 (A+D,E) Table 5: Experimental evaluation result of RBFNN, RNN, PNN with different training algorithms. Cases Other Types of Neural Network for Seizure RBF Neural Network Probabilistic Neural Network Types SPE SEN ACC TIME SPE SEN ACC TIME Case1 83.076 65.925 71.5 2.051 100 100 100 0.967 (A,E) Case2 100 97.08 98.5 1.828 100 100 100 0.977 (D,E) Case3 92.30 66.41 81 2.928 100 100 100 1.616 (A+D,E) Cases Other Types of Neural Network for Seizure Recurrent Neural Network Types SPE SEN ACC TIME Case1 77.173 73.148 75 10.31 (A,E) Case2 64.705 71.604 67.5 13.29 (D,E) Case3 67.346 66.666 67.333 19.58 (A+D,E) Table 6: Comparative analysis of different machine learning classification techniques. Case-1 (set A & E) Case-2 (set D & E) Machine Learning Classification Overall Approximate Overall Approximate Technique Accuracy Time taken Accuracy Time taken in %age in seconds in %age in seconds MLPNN/BP 94.5 16.527 90 22.226 MLPNN/RP 99.5 2.846 99.5 2.547 MLPNN/MUR 85 7.541 60 7.181 SVM/Linear 100 2.127 100 1.904 SVM/Ploy 100 2.101 100 1.902 SVM/RBF 100 2.002 100 2.021 PNN 100 0.967 100 0.977 RNN 75 10.31 67.5 13.29 RBFNN 71.5 2.051 98.5 1.828 Case-3 (set A + D & E) Machine Learning Classification Overall Approximate Technique Accuracy Time taken in %age in seconds MLPNN/BP 92.5 23.127 MLPNN/RP 92.33 14.798 MLPNN/MUR 89.66 14.85 SVM/Linear 85.66 11.61 SVM/Ploy 99.66 7.24 SVM/RBF 100 2.511 PNN 100 1.616 RNN 67.33 19.58 RBFNN 81 2.928 Table 7: Comparative analysis of different machine learning classification techniques with ensemble based classifier. Case-1 (set A & E) Case-2 (set D & E) Machine Learning Classification Overall Approximate Overall Approximate Technique Accuracy Time taken Accuracy Time taken in %age in seconds in %age in seconds MLPNN/RP 99.5 2.846 99.5 2.547 RNN 75 10.31 67.5 13.29 RBFNN 71.5 2.051 98.5 1.828 ENSEBLE 99.5 3.745 99.5 3.876 CLASSIFIER Case-3 (set A + D & E) Machine Learning Classification Overall Approximate Technique Accuracy Time taken in %age in seconds MLPNN/RP 92.33 14.798 RNN 67.33 19.58 RBFNN 81 2.928 ENSEBLE 98.3 5.475 CLASSIFIER
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|Author:||Satapathy, Sandeep Kumar; Jagadev, Alok Kumar; Dehuri, Satchidananda|
|Date:||Mar 1, 2017|
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