CHAPTER 5 NEURAL NETWORK BASED CLASSIFICATION OF ELECTROGASTROGRAM SIGNALS

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1 113 CHAPTER 5 NEURAL NETWORK BASED CLASSIFICATION OF ELECTROGASTROGRAM SIGNALS 5.1 INTRODUCTION In today s computing world, Neural Networks (NNs) fascinate the attention of the multi-disciplinarians like scientist, researchers and technologist. A neural network is used to learn patterns and relationships in data. NNs are acceptable or more convenient tool for solving problems in the field of biomedical engineering and in particular, to analyze the biological signals. NNs are well suited for real time applications such as pattern recognition or classification of biological signals because of their remarkable ability to derive meaning from complicated or imprecise data that could be used to extract patterns and trends that are too complex to be noticed by either humans or other computer techniques. The collective behavior of NNs is similar to human brain. It demonstrates the ability to learn, recall and generalize from training patterns or data. Neural network derive their computing power through its massively parallel-disturbed structure. They have immense ability to learn complex and non-linear relationships which include noisy or less precise information. They are well suited for solving problems in biomedical engineering and in particular in analysis of biomedical signals. NNs have advantage over conventional technologies in that they can solve complex problem that do not

2 114 have algorithmic solution or for which an algorithmic solution is too complex to be found. NNs are trained by example instead of rules and are automated. When used in medical diagnosis, they are not affected by factors such as human fatigue, emotional states and habituation. They are capable of rapid identification, analysis of condition and diagnosis in real time. In this thesis, neural networks models are developed for the classification of Electrogastrogram (EGG) signals. Investigation of digestive system disorders are carried out with EGG data using unsupervised learning network and supervised learning network as listed below. Adaptive Resonance Theory implemented neural network (ART1NN) Learning Vector Quantization (LVQ) Artificial Neural Network (ANN) with MRAN Algorithm 5.2 LITERATURE REVIEW Ahsan et al (2011) described the process of detecting different predefined hand gestures using Artificial Neural Network (ANN) for complex pattern recognition and classification tasks of EMG signals based on features. The authors used BPN with Levenberg-Marquardt training algorithm for the detection of gesture. Yu-Chien Shiau et al (2011) performed cardiac motion analysis using BPN network analysis for all serial images and also for all series of patient s images. Elsayad (2009) applied Learning Vector Quantization (LVQ) neural networks to classify arrhythmia from the Electrocardiogram (ECG) dataset. The experimental results recommend LVQ algorithm for further research in any biosignal.

3 115 Coyle (2009) illustrated the role of neural network in a prediction based preprocessing framework, referred to as Neural-Time-Series- Prediction-Preprocessing (NTSPP), in an Electroencephalogram (EEG)-based Brain-Computer Interface (BCI). The authors also mentioned that NTSPP can improve the potential for employing existing BCI methods with minimal subject-specific parameter tuning to deploy the BCI autonomously with six different classification approaches. Jingwen Tian et al (2009) presented a Web classification mining method based on Fuzzy Neural Network (FNN) with the Levenberg-Marquart optimizing algorithm to train fuzzy neural network to enhance the convergence rate and the classification accuracy. Jiaoying Huang et al (2008) performed Weak Biosignal Processing using Adaptive Wavelet Probabilistic Neural Network (AWPNN) by extracting the features from original signal, and then the Probabilistic Neural Network (PNN) is used to analyze the meaningful features and perform discrimination tasks. Abdulhamit Subasi (2005) tried a few of the modified BPNN algorithms like Resilient backpropagation, Levenberg-Marquardt backpropagation, Scaled conjugate gradient backpropagation for training the ANN because of slow momentum by the conventional backpropagation algorithm in the analysis of EEG signals. The author observed that the best performance was obtained for the training set, validation test set and separate test set with ANN architecture configuration Ramanathan et al (2004) classified lung sound using the neural classification with wavelet coefficients with ANN architecture configuration The authors reported that ANN architecture provided the best performance with an optimum of 40 neurons in the hidden layer of the architecture. Zhiyue Lin et al (1997) developed Multilayer FeedForward Neural Networks (MFNN) for the classification for digestive disorders with one hidden layer with maximum of five hidden nodes using Scaled Conjugate

4 116 Gradient backpropagation training algorithms and the performance is evaluated by computing the percentage of correct classification, sensitivity and specificity. With the architecture, 85% correct classification was obtained with 82 % sensitivity and 89 % specificity. The authors also mentioned that the MFNN can be further improved through better feature extraction of the EGG and best selection of feature combinations for the increase in correct classification of digestive system disorders. Zhiyue Lin et al (1997) proposed that network with 6 or 7 hidden neurons achieve the best result for the classification of EGG signals. It was found that slow convergence rate and critical user dependent parameters are the obstacles for the differentiation of normal and abnormal signal. Zhiyue Lin et al (1995) performed a comparison between gradient descent and conjugate gradient learning algorithms for classification of Electrogastrogram. It was concluded that scaled conjugate gradient (SCG) algorithm is a robust algorithm for the classification of the normal and abnormal EGG. It has moderate computational complexity and shows a super linear convergence rate. Zhiyue Lin et al (1994) proposed the classification of normal and abnormal EGG signals using back propagation network. It has been concluded that optimal BPN has been developed for the ARMA parameters which requires 22 input nodes, 15 hidden nodes and 2 output nodes. The main problem includes the difficulty in interpreting EGG data and extracting the useful information from EGG. Saibal Dutta et al (2011) used Learning Vector Quantization medical diagnostic tool for heart beat categorization. The main objective is to achieve an accurate, timely detection of cardiac arrhythmia for providing appropriate medical attention to a patient. The proposed scheme employs a feature extractor coupled with an Artificial Neural Network (ANN) classifier.

5 117 The feature extractor is based on cross-correlation approach, utilizing the cross-spectral density information in frequency domain. Curilem et al (2010) described a feature selection process applied to EGG processing. The data set is formed by 42 EGG records from functional dyspeptic (FD) patients and 22 from healthy controls. A wrapper configuration classifier was implemented to discriminate between both classes. The aim of this work is to compare artificial neural networks (ANN) and support vector machines (SVM) when acting as fitness functions of a genetic algorithm (GA) that performs a feature selection process over some features extracted from the EGG signals. De Gaetano A. et al (2009) designed a novel supervised neural network-based algorithm to reliably distinguish in electrocardiographic (ECG) records between normal and ischemic beats of the same patient. The basic idea behind this paper is to consider an ECG digital recording of two consecutive R-wave segments (RRR interval) as a noisy sample of an underlying function to be approximated by a fixed number of Radial Basis Functions (RBF). 5.3 NEURAL NETWORK MODEL Artificial Neural Networks (ANN) is systems that are deliberately constructed to make use of some organizational principle resembling those of the human brain. They represent the promising new generation of information processing systems. Neural Networks are good at task such as pattern matching and classification, optimization and data clustering. They have a large number of highly interconnected processing elements called neurons, which usually operate in parallel and are configures in regular architectures. The collective behavior of a NN, like a human brain, demonstrates the ability

6 118 to learn recall and generalize from training patterns or data. NNs are characterized by i) Patter of interconnection between neurons ii) Learning algorithm iii) Activation function. In a NN, each neuron is connected to the other neuron by means of directed communication link and with an associated weight. Each has an internal stare called as its activity level. Based on the signal flow direction they are classified as feed forward networks and feedback networks. The block diagram of a neuron is shown following Figure 5.1. Figure 5.1 Model of a Neuron

7 119 The three basic elements of the neuronal model are: i) A set of synapses or connecting links, each of which is characterized by a weight or strength of its own. A signal x j at the input of synapses j connected to the neuron k is multiplied by weight w kj. ii) An adder for summing the input signals. iii) An activation function for limiting the amplitude of the neuron. (5.2). Neuron k is described mathematically as in Equations (5.1) and k m u W y j 1 u b k k k kj X j (5.1) (5.2) where, X 1, X 2. X m : input W 1, W k,.w m : weights of neuron k b k : bias u k : output of the adder (.) : activation function y k : output of the neuron The weight used on the connections between layers has much significance in the working of the neural network. Weight assignments on connections between neurons not only indicate the strength of the signal that is being fed for aggregation but also the type of interaction between the two neurons. Initializing the network structure is a part of what is called the encoding phase of a network. It is possible to start with randomly chosen

8 120 values for weights and the weights are adjusted appropriately as the network is run through iterations. The net signal is further processed by an activation function or transfer (.). A sigmoidal transfer function given by Equation (5.3) is normally used. 2 u 1 1 exp( u ) (5.3) where, σ = slope parameter shown in Figure 5.2 The sigmoidal activation function used in the BPNN algorithm is Figure 5.2 Sigmoidal Activation Function

9 ADAPTIVE RESONANCE THEORY (ART1) NETWORK The first NN architecture considered for classification is the ART1NN which is designed for clustering binary vectors using unsupervised learning. This network is designed to control the degree of similarity of patterns placed on the same cluster (Raphel Feraud et al 2001). The network produces the clusters by itself if such clusters are identified in the input data, and stores the clustering information about patterns or features without prior information about the possible number and type of clusters. Essentially the network follows the leader after it generates the first cluster with the first input pattern it received. It then creates second cluster if the distance of the second cluster exceeds a certain threshold, otherwise the pattern is clustered with the first cluster and the same procedure is repeated for all sets of data Architecture of ART1 The architecture of an ART1 net (Sivanandam and Deepa, 2007) shown in Figure 5.3 consists of two fields of units the F 1 units and F 2 (cluster) units together with a reset unit to control the degree of similarity of patterns placed on the same cluster unit. The F 1 and F 2 layers are connected by two sets of weighted pathways. It is assumed that the ART1 net is being operated in a fast learning mode, in which the weights reach equilibrium during each learning trial. The architecture of ART1 consists of computational units and supplemental units.

10 122 Figure 5.3 Architecture of ART1 network Computational Units The architecture of the computational units for ART1 consists of F 1 units (input and interface units), F 2 units (cluster units), and a reset unit that implements user control over the degree of similarity of patterns placed on the same cluster. Each unit in the F 1 (a) layer is connected to the corresponding unit in the F 1 (b) layer. Each unit in the F 1 (a) and F 1 (b) is connected to the reset unit, which in turn is connected to every F 2 unit. Each unit in the F 1 (b) layer is connected to each unit in the F 2 (cluster) layer by two weighted pathways. The F 1 (b) unit X i is connected to the F 2 unit Y j by bottom-up weights b ij. Similarly, unit Y j is connected to unit X i by top-down weights t ji. The F 2 layer is the competitive layer in which only the uninhibited node with the largest net input has a nonzero activation.

11 ART1 Training Algorithm The following parameters are used in the ART1 algorithms L : Learning Trail ρ : Vigilance parameter n : Number of components in the input vector b : bottom-up weights ij t : top-down weights ji S : Binary input vector X : Activation vector for interface X : Norm of vector X (sum of the components X i ) The training algorithm of ART 1 network is as follows. Step 1 : Initialize parameters. L 1, 0 1 Initialize weights, 0 b ij L ( L 1 n) t ij ( 0) 1 Step 2 : When stopping condition is false, execute steps 3 to 14. Step 3 : For each training input, do steps 4 to 13. Step 4 : Set activation of all F 2 units to zero. Set activation of F 1 (a) units to input vector S.

12 124 Step 5 : Compute the norm of S using Equation (5.4). S S i (5.4) i Step 6 : Send input signal from F 1 (a) to F 1 (b) layer. Xi S i Step 7 : For each F2 node that is not inhibited, If Y J 1 then computey j using Equation (5.5). Y b j ijxi (5.5) i Step 8 : When reset is true, execute steps 9 to 12. Step 9 : Find J such that J j Y Y for all nodes j. If Y j 1, then all nodes are inhibited and this pattern cannot be clustered. Step 10 : Recompute activations, X of F 1 (b) using the Equation (5.6). X S t (5.6) i i Ji Step 11 : Compute the norm of vector, X using the Equation (5.7). Step 12 : Test for reset X X i (5.7) i If X / S ρ, then Y J 1and execute step 7.

13 125 If X / S ρ then proceed to step 13, Step 13 : Update weights for node J using the Equations (5.8) and (5.9). b ij new LX / L 1 X (5.8) i t Ji(new) X i (5.9) Step 14 : Test for stopping condition. The stopping condition corresponds to no units being reset. In the ART1 architecture, the typical value of learning trail (L) is fixed as 2 and for the different value of vigilance parameter ( 0 1), the classification of normal subjects and abnormal subjects is performed. Table 5.1 EGG Classification for Different in ART1 Network Classification Accuracy (%) EGG Types =0.2 =0.4 =0.6 =0.8 =1.0 Normal Abnormal As shown in Table 5.1, for the ρ = 0.6, the classification percentage is found to be maximum as 68% for normal and 71 % for the abnormal subjects. From the dataset of 1000 samples, five groups of 200 samples each is selected as shown in Table 5.2 and applied to the ART1 network for classification.

14 126 Table 5.2 EGG Classification using ART1 Network Group Normal Abnormal Classification Accuracy (%) Average As a result, ART1 network correctly classified 696 samples out of 1000 samples with a classification accuracy of 69.6 % which include normal subjects and abnormal subjects. To be consistent with the previous studies for comparison confusion matrix is formed and the performance measures are computed for data set of 500 samples Confusion Matrix for ART1NN Confusion matrix formed for the signals acquired in the laboratory setup with different composition as in Table 3.5 are tabulated in Table 5.3 and Table 5.4 for different sample sets.

15 Table. 5.3 Confusion matrix generated using ART1NN for 200 and 300 Samples 200 samples 300 samples Actual classes Actual classes Predicted Classes

16 Table. 5.4 Confusion matrix generated using ART1NN for 400 and 500 Samples 400 samples 500 samples Actual classes Actual classes Predicted Classes

17 129 Table 5.5 Performance Measures for ART1NN ART1NN S. No. Samples Precision Sensitivity Specificity F- measure Time (sec) Classification Accuracy % Precision, Sensitivity, Specificity, F-measure, Time and Classification Accuracy is listed in Table 5.5. For 500 sample set an average of 71% Sensitivity, 95% Specificity and 69.5% Classification Accuracy is observed using ART1 Neural Network. 5.5 LEARNING VECTOR QUANTIZATION (LVQ) NETWORK The second architecture that is used for classification is the LVQ network. One of the common applications of competitive learning is Adaptive Vector Quantization for data compression such as biosignal, speech and image data. In this approach, a given set of EGG data is grouped into n groups or templates so that later one may use an enclosed version of the corresponding template of any input vector to represent the vector as opposed to using the vector itself. Vector Quantization is a technique whereby the input space is divided into a number of distinct regions and for each region a code vector is defined. LVQ is defined for adaptive pattern classification. The class information is used to fine tune the code vector so as to improve the quality of the classifier decision regions. It is assumed that a set of training pattern with known classifications is provided along with an initial distribution of reference vectors.

18 130 Learning Vector Quantization (LVQ) is a method for training competitive layers in a supervised manner. A competitive layer automatically learns to classify input vectors. However, the classes that the competitive layer finds are dependent only on the distance between input vectors. If two input vectors are very similar, the competitive layer places them in the same class. There is no mechanism to determine whether or not any two input vectors are in the same class or different classes. LVQ networks learn to classify input vectors into target classes chosen by the user Architecture of LVQ Figure 5.4 depicts the architecture of LVQ network (Laurene Fausett 2004). The architecture of an LVQ net is similar to that of a Kohonen self-organizing map without a topological structure. In addition, each output unit is assigned to a known class. W 11 X 1 X 1 Y 1 W 12 W 21 W 22 X 2 X 2 Y 2 W 31 W 32 W 23 W 13 X 3 X 3 W 33 Y 3 Figure 5.4 Architecture of LVQ Network

19 131 LVQ network has a first competitive layer and a second linear layer. The competitive layer learns to classify input vectors. The linear layer transforms the competitive layer's classes into target classifications defined by the user. The classes learned by the competitive layer are referred as subclasses and the classes of the linear layer as target classes. Both the competitive and linear layers have one neuron per (sub or target) class. To be consistent with the previous studies for comparison confusion matrix is formed and the performance measures are computed for data set of 500 samples LVQ Training Algorithm The LVQ training algorithm is as follows, Step1 : Assign first m input vectors as reference input vectors. Initialize learning rate Step2 : While stopping condition is false, do steps 3 to 7. Step3 : For each training input vector X do steps 4 and 5. Step4 : Find the winner so that X W is minimum. j Step5 : Update W [if the winner represents the current class]. j If D = C j, then update using the Equation (5.10). W (new ) W (old) α X W (old) j j j (5.10) If D C j, then update using the Equation (5.11).

20 132 W (new ) W (old) α X W (old) j j j (5.11) Step6 : Reduce learning rate, as shown in Equation (5.12). α(new) α(old)/(1 α(old)) (5.12) Step7 : Test stopping condition value of The stopping condition is the learning rate (α) reaching a small Table 5.6 Comparison of LVQ network performance for varying α % of Training Sample % of Testing Sample Classification Accuracy (%) α = α = α = α = Time (sec) α = 0.9 α = 0.6 α = 0.3 α =

21 133 LVQ network is performed with case IV of 500 samples of EGG data from the acquired EGG database. It includes normal subjects and different type of abnormal subjects. The network is trained with different percentage of training vectors and the same is tested with different set of test vectors for varying learning rate of α=0.9, α=0.6, α=0.3 and α=0.1. Table 5.6 lists the percentage of correct classification along with the execution time for different percentage of training and testing data for different values of learning rate α. From Table 5.6, it is found that for the training vector of 60% and above, the efficiency is around 92 %. It is observed that the classification efficiency is independent of learning rate and the execution time increases as the percentage of training vector increases. So for the best performance of LVQ network, it is trained with 60% and above with α= Confusion Matrix for LVQ network The confusion matrix formed for the signals acquired in the laboratory setup with different composition as in Table 3.5 are tabulated in Table 5.7 and Table 5.8 for different sample sets.

22 Table. 5.7 Confusion matrix generated using LVQ network for 200 and 300 Samples 200 samples 300 samples Actual classes Actual classes Predicted Classes

23 Table. 5.8 Confusion matrix generated using LVQ network for 400 and 500 Samples 400 samples 500 samples Actual classes Actual classes Predicted Classes

24 136 Table 5.9 Performance Measures for LVQ network S. No. Samples LVQ network F- Precision Sensitivity Specificity measure Time (sec) Classification Accuracy % Precision, Sensitivity, Specificity, F-measure, Time and Classification Accuracy is listed in Table 5.9. For 500 sample set an average of 91% Sensitivity, 98.4% Specificity and 92 % Classification Accuracy is observed using LVQ network. 5.6 BPNN WITH MINIMAL RESOURCE ALLOCATION NETWORK ALGORITHM The determination of various parameters associated with NN is not straight forward and finding optimal configuration is very much time and memory consuming process. Therefore instead of selecting the architecture randomly or by trial and error method, the MRAN algorithm is used to find the minimum number of neurons to be fixed in the hidden layer for maximum efficiency in BPNN. The most widely used architecture of an NN is that of a Multilayer Perception (MLP) trained using Backpropagation (BP) algorithm. It is a gradient descent algorithm that tries to minimize the average squared error of the network.

25 Minimal Resource Allocation Network MRAN is a sequential learning radial basis function neural network which combines the growth criterion of the Resource Allocating Network (RAN) of Platt with a pruning strategy based on the relative contribution of each hidden unit to the overall network output. The resulting network leads toward a minimal topology for the RAN. Radial Basis Function Neural Networks (RBFNN) is found to be well suited for function approximation and pattern recognition due to its topological structure and its ability to reveal the proceeding of learning in an explicit manner (Tao 1993 and Musavi et al 1992). In the radial basis function (RBF) network implementation, the basis functions are usually chosen as gaussian and the number of hidden units (that is centres and widths of the gaussian functions) is fixed based on the properties of input data. The weight connecting the hidden and output units are estimated by a linear least squares method. The disadvantage with this approach is that it is not suitable for seqential learning and usually results in too many hidden units. A significant contribution that overcomes these drawbacks was made by Platt through the development of an algorithm that adds hidden units to the network based on the novelty of the new data. The algorithm is suitable for sequential learning and is based on the idea that the number of hidden units should correspond to the complexity of the underlying function as reflected in the observed data. The resulting network is called a Resource Allocating Network (RAN) and it starts with no hidden units and grows by allocating new hidden units based on the novality in the observations that arrive sequentially. If an observation has no novelty, then the existing parameters of the network are adjusted by an Least Mean Square (LMS) algorithm to fit that observation.

26 Minimal Resource Allocation Network (MRAN) Architecture The structure of RAN is the same as that of RBF network (Lu Yingwei, 1998) and is shown in Figure 5.5. Each hidden unit in the network has two parameters called a center (µ), and a width (σ) associated with it. w 11 µ 1, σ 1 v 11 Bias v 0 X 1 Y 1 X i Y k µ i, σ j X n Y m W np µ p, σ p V pm INPUT LAYER HIDDEN LAYER OUTPUT LAYER Figure 5.5 Architecture of MRAN Network The activation function of the hidden units is radially symmetric in the input space and the output of each hidden unit depends only on the radial distance between the input vector and the center parameter µ for that hidden unit. The weight between input and hidden layer is w 11 to w np and the weight between hidden and output layer is v 11 to v pm. The response of each hidden unit is scaled by its connecting weights to the output units and then summed to produce the overall network output. The output of neuron is given by Equations (5.13) and (5.14).

27 139 m p 0 v jkφk (X) k 1 j 1 f (x) v (5.13) and 1 2 φ k (X) exp X μ (5.14) 2 j σ j where, φ k (X) : response of the k th hidden unit v : connecting weight to the output unit jk μ : center of the j th hidden neuron j σ : width of the j th hidden neuron j v 0 : bias term. The learning process of RAN involves allocation of new hidden units as well as adaptation of network parameters. The network begins with no hidden units. As input output (x n,y n ) data are received during training, some of them are used for generating new hidden units. The decision as to whether an input output data should give rise to a new hidden unit depends on the data which is decided using the two conditions given by Equations (5.15) and (5.16). X n μ e (5.15) nr n e n y f ( x e (5.16) n n ) min

28 140 where, X n : Input data y n : Output data μ nr : Center which is nearest to x n e n : Thresholds to be selected appropriately e min : Thresholds to be selected appropriately If the above two conditions are satisfied then the data is deemed to have novelty and a new hidden unit is added. The first condition says that the input must be far away from all the centers and the second condition says that the error between the network output and the target output must be significant. The value of e min represents the desired approximation accuracy of the network output and the distance e n represents the scale of resolution in the input space. The algorithm begins with e n = e max. e max is chosen as the largest scale of interest in the input space, typically the entire input space of nonzero probability. The distance is e n decayed exponentially as e n =max{e max, γ n,e min } where 0< γ < 1 is a decay constant. The value for e n is decayed until it reaches e min. The exponential decaying of the distance criterion allows fewer basis functions with large widths (smoother basis functions) initially and with increasing number of observations more basic functions with smaller widths are allocated to fine tune the approximation.

29 141 Figure 5.6 Flow Chart of MRAN Algorithm The parameters associated with the new hidden unit are given by Equations (5.17) to (5.19). v 1 (5.17) k e n μ x k 1 n (5.18) σ k 1 k x n μ nr (5.19)

30 142 where k is an overlap factor that determines the amount of overlap of the responses of the hidden units in the input space. The value of e max and e min are 0.4 and 0.2 respectively. The flowchart of MRAN algorithm is shown in Figure 5.6. It gives the detail about the computation of network output values by comparing it with the actual value so that the error value can be obtained. If the error value satisfies the criterion for adding new neurons, then the new hidden neuron is added else the value of weight, center and width of the existing neuron is adjusted accordingly to satisfy the condition. When the criteria are satisfied by the hidden neurons for pruning then the training is done to prune the hidden neurons, if not the training ends Training of Minimal Resource Allocation Network (MRAN) The MRAN network is trained with the EGG database. The target is assigned as 1 for correct position and 0 is assigned for the wrong position of classification. MRAN algorithm determines the minimum number of Hidden Layer Neurons (HLN) required for the maximum efficiency. HLN=3 HLN=6 HLN=9 HLN=12 HLN=15 HLN=18 Classification Accuracy (%) Decay Constant(γ) Figure 5.7 Classification using MRAN Algorithm for varying Decay Constant

31 143 Figure 5.7 represents the consistency of MRAN network in the classification of disorders for different values of decay constant with respect to hidden layer neurons. It is observed that, when MRAN is trained with less than 9 HLN, the percentage of classification is found to be poor. When HLN is 15 and above, the classification is found to be saturated at 90 % for decay constant of 0.9. It is also found that the classification linearly increases with different value of decay constant for 15 number of HLN. The HLN number of is varied from 9 to 18 for fixing the HLN in the BPNN architecture to obtain maximum percentage of classification. Table 5.10 tabulates the different trials at which MRAN gives different number of neurons and its corresponding efficiency. From the MRAN algorithm, the required number of neurons in the hidden layer is determined for maximum efficiency. The network is tested with 350 samples. The table shows that network with 15 hidden neurons achieves maximum efficiency. It also shows the number of samples classified correctly under each disease. Table 5.10 Fixation of Number of HLN using MRAN Algorithm Trials No of Neurons in Hidden Layer Normal Number of Test EGG Classified ( 50 Each) Bradygastria Dyspepsia Abnormal Nausea Tachygastria Ulcer Vomiting Total Correct Classification (%)

32 144 Thus, the configuration of network is fixed as using MRAN algorithm. This architecture is then used for classification of digestive system disorders. Different training algorithms namely trainrp, trainoss, traingdx, trainlm,trainscg,trainbfg, traincgb,traincgp and traincgf defined in Appendix 4 are applied for classification. The performances of the training algorithm are compared with respect to percentage of correct classification Performance Comparison of Training Algorithms in BP-MRAN Network The performance comparison of different training algorithm for normal subjects, bradygastria subjects, dyspepsia subjects, nausea subjects, tachygastria subjects, ulcer subjects and vomiting subjects is shown in Figures 5.8 to 5.14 respectively. The graph is plotted between error goal and epochs. From all plots, it is observed that in the training algorithms trainrp, traingdx and traincgp, the epoch values increases for error goal of 0.1, 0.01 and Figure 5.8 Error vs Number of Epochs for Normal subjects

33 145 Figure 5.9 Error vs Number of Epochs for Bradygastria subjects Number of epochs Error=0.1 Error=0.01 Error=0.001 Error goal OSS RP GDX LM SCG BFG CGB CGP Figure 5.10 Error vs Number of Epochs for Dyspepsia Subjects 50 OSS Number of epochs Error=0.1 Error=0.01 Error=0.001 Error goal RP GDX LM SCG BFG CGB CGP Figure 5.11 Error vs Number of Epochs for Nausea Subjects

34 Number of epochs OSS 50 RP 40 GDX 30 LM 20 SCG 10 BFG 0 Error=0.1 Error=0.01 Error=0.001 Error goal CGB CGP Figure 5.12 Error vs Number of Epochs for Tachygastria Subjects Number of epochs Figure 5.13 Error vs Number of Epochs for Ulcer Subjects 50 OSS 40 RP 30 GDX LM 20 SCG 10 BFG 0 Error=0.1 Error=0.01 Error goal Error=0.001 CGB CGP Figure 5.14 Error vs Number of Epochs for Vomiting Subjects

35 147 Using this BP-MRAN archictecure, classification of EGG subject is performed with different set of learning rate and momentum factor value for different training algorithm. Maximum efficiency of each algorithm for varying learning rate and momentum factor is shown in Table Table 5.11 Classification Accuracy of EGG for Different Training Algorithms for varying α and β LR (α) MF (β) Correct Classification of EGG (%) OSS RP GDX LM * SCG BFG * CGB CGP CGF Learning Rate, -Momentum Factor,*-MF is not applicable From the Table 5.11, it is observed that the classification of EGG subjects with trainrp is 97 % for LR and MF of 0.6. The trainrp exhibits better performance when compared with other training algorithms. It is also observed that a maximum classification of 61 % with LR of 0.2 and MF of 0.8 is observed for Trainoss. The training algorithm traingdx achieved

36 148 maximum classification of 94 % for LR and MF of 0.4, where as the training algorithm trainlm achieved a maximum of 56 % classification for LR of 0.4 and MF of 0.6. The trainscg algorithm achieved maximum classification of 65 % with LR=0.4 and MF= 0.8. The training algorithm trainbfg showed maximum classification of 63 % for LR of 0.6 and MF of 0.8, the training algorithm traincgb achieved maximum classification of 62 % for LR and MF of 0.6, the training algorithm traincgp achieved maximum classification of 93 % for LR of 0.8 and MF of 0.4 and the training algorithm traincgf achieved maximum classification of 60 % for LR of 0.6 and MF of 0.8. Table 5.12 shows the efficiency of each training algorithm in BPNN during test phase. The targets are used as 0 s and 1 s. The total samples used for classification is 350. The table shows the number of samples classified correctly under each disorder when using BP-MRAN network. Table 5.12 Performance Comparison of Training Algorithm for BP- MRAN Network Training Algorithm Normal Number of Test EGG Classified ( 50 Each) Bradygastria Dyspepsia Abnormal Nausea Tachygastria Ulcer Vomiting Total Correct Classification (%) OSS RP GDX LM SCG BFG CGB CGP CGF

37 149 From Table 5.12, it is concluded that the training algorithms trainrp, traingdx and traincgp gives maximum efficiency compared to other algorithms. MRAN used in combination with the BPNN, decides the number of HLNs to achieve the maximum efficiency of classification. To be consistent with the previous studies for comparison confusion matrix is formed and the performance measures are computed for data set of 500 samples Confusion Matrix for BP-MRAN network Confusion matrix formed for the signals acquired in the laboratory setup with different composition as in Table 3.5 are tabulated in Table 5.13 and Table 5.14 for different sample sets. 5.7 COMPARISON OF NEURAL NETWORK ARCHITECTURES The performance for the three architectures used in this thesis to detect the abnormalities in the EGG signals is discussed here. The percentages of sensitivity, percentage of specificity and classification accuracy of three architectures are tabulated in Table The sensitivity and specificity confirms the classification accuracy of all architecture is plotted in Figure 5.15 and Figure 5.16 respectively. The classification accuracy for the three architectures is compared as shown in Figure For ART1NN, LVQNN and BP-MRAN network the classification accuracy obtained is 69.5%, 92.0% and 96% respectively.

38 Table Confusion matrix generated using BP-MRAN network for 200 and 300 Samples 200 samples 300 samples Actual classes Actual classes Predicted Classes

39 Table Confusion matrix generated using BP-MRAN network for 400 and 500 Samples 400 samples 500 samples Actual classes Actual classes Predicted Classes

40 152 Table 5.15 Performance Measures for BP-MRAN network S. Samples No. BP-MRAN network Precision Sensitivity Specificity F- Time Classification measure (sec) Accuracy % Precision, Sensitivity, Specificity, F-measure, Time and Classification Accuracy are listed in Table For 500 sample set an average of 94% Sensitivity, 98.5% Specificity and 96 % Classification Accuracy is observed using BP-MRAN network.

41 Table 5.16 Comparison of Neural Network Architectures 500 Samples S. No. Architecture Sensitivity % Specificity % Classification Accuracy % 1. ART1 NN LVQ NN BP-MRAN NN

42 154 % of Sensitivity ART1 NN LVQ NN BP-MRAN NN Architectures Figure 5.15 Sensitivity of different Architectures % Specificity ART1 NN LVQ NN BP-MRAN NN Architecures Figure 5.16 Specificity of different Architectures

43 155 % of Classification Accuracy ART1 NN LVQ NN BP-MRAN NN Architecures Figure 5.17 Classification Accuracy of different Architectures 5.8 CONCLUSION In this chapter, three architectures of ANN were trained and tested to classify EGG signals. ART1NN, an unsupervised network is used to classify the EGG as normal or abnormal. The LVQ network, a supervised method using competitive layers to improve the classifier decision is investigated. BPNN using supervised learning was implemented. To maximize the efficiency and minimize the computation time, the MRAN algorithm is applied to fix architecture. Nine training algorithms are used to train the BP- MRAN and their performance is compared. It is observed that trainrp, traingdx and traincgp give maximum classification of 96.28%, 94% and 92.57% respectively. For comparison with other methods, Sensitivity, Specificity analysis is also done and the maximum classification accuracy of 96 % is obtained for BP-MRAN trained with Resilient Backpropagation algorithm. 14% and 10% improvement is observed using BP-MRAN with trainrp when compared Chacon et al (2009) who used BPANN with trainrp and Curilem et al (2010) who used SVM with GA.