Classification Of Epilepsy Risk Levels Research Articles

Wavelet Neural Networks, Elman Backpropagation and Multilayer Perceptrons for Epilepsy Classification from EEG Signals

The aim of this study is to provide the performance analysis of wavelet networks for the classification of epilepsy from Electroencephalography (EEG) signals. The frequency behaviors of different waveform components of EEG waveforms such as Alpha, Beta, Theta, Delta and Gamma are undertaken. The features are extracted at Wavelet Node and it is given for the neural network classifier inputs. Similarly the code converter features are also extracted and it is given for the neural network classifier inputs. The Multilayer Perceptron Network (MLP) and the Elman Back Propagation Network are the two used networks to identify the patient state for the classification of epilepsy risk levels. The Neural network is trained by Levenberg-Marquardt (LM) algorithm. The Performance Index and the Quality Values are the two important benchmark parameters used for the performance analysis of wavelet networks for classification of epilepsy risk levels from EEG signals.

ASSESSMENT OF EPILEPSY CLASSIFICATION USING TECHNIQUES SUCH AS SINGULAR VALUE DECOMPOSITION, APPROXIMATE ENTROPY, AND WEIGHTED K-NEAREST NEIGHBORS MEASURES

ABSTRACTObjective: The main aim of this research is to reduce the dimension of the epileptic Electroencephalography (EEG) signals and then classify it usingvarious post classifiers. For the evaluation and easy treatment of neurological diseases, EEG signals are used. The reflection of the electrical activitiesof the human brain is obtained by the measurement of potentials in EEG. To study and explore the brain functions in an exhaustive manner, EEG is usedby both physicians and scientists. The study of the electrical activity of the brain which is done through EEG recording is a vital tool for the diagnosis ofmany neurological diseases which include epilepsy, sleep disorders, injuries in head, dementia etc. One of the most commonly occurring and prevalentneurological disorders is epilepsy and it is easily characterized by recurrent seizures.Methods: This paper employs the concept of dimensionality reduction concepts like Fuzzy Mutual Information (FMI), Independent ComponentAnalysis (ICA), Linear Graph Embedding (LGE), Linear Discriminant Analysis (LDA) and finally Variational Bayesian Matrix Factorization (VBMF).The epilepsy risk levels are also classified using post classifiers like Singular Value Decomposition (SVD), Approximate Entropy (ApEn) and WeightedKNN (W-KNN) classifiers.Results: The highest accuracy is obtained when LDA is combined with Weighted KNN (W-KNN) Classifiers and it is of 97.18%. Conclusion: Thus the EEG signals not only represent the brain function but also the status of the whole body. The best result obtained was whenLDA is engaged as a dimensionality reduction technique followed by the usage of the W-KNN as post classifier for the classification of epilepsy risklevels from EEG signals. Future work may incorporate the possible usage of different dimensionality reduction techniques with various other types ofclassifiers for the perfect classification of epilepsy risk levels from EEG signals.Keywords: FMI, ICA, LGE, LDA, W-KNN, EEG

Variational Bayesian Matrix Factorization and Certain Post Classifiers for Classification of Epilepsy from EEG Signals

The main aim of this paper is to employ Variational Bayesian Matrix Factorization (VBMF) as a dimensionality reduction technique followed by the Gaussian Mixture Model (GMM), Genetic Algorithm (GA) and Naive Bayes Classifier (NBC) as post classifiers for the classification of epilepsy risk levels from Electroencephalography (EEG) Signals. Since epilepsy is one of the serious disorders of the brain which is characterized by frequent and recurrent seizures, the detection and classification of it seems to be very important. Using the EEG signals, the epileptic seizures can be analyzed because it aids in the recording, diagnosing and for treating other neurological disorders. In this paper, the results are analyzed and compared in terms of sensitivity, specificity, time delay, quality values, performance index and accuracy.

Code Converters with City Block Distance Measures for Classifying Epilepsy from EEG Signals

This paper aims to compare the performance of code-converter as a feature extraction technique followed by various distance measures such as City Block Distance (CBD) measure and Euclidean Distance (ED) measure for the perfect classification of epilepsy risk levels from Electroencephalography (EEG) Signals. From the extracted parameters such as sharp and spike waves, energy, peaks, duration, variance, events and covariance from the EEG Signals of an epileptic patient, the risk level of epilepsy is classified. The City Block Distance Measure and Euclidean Distance Measure is then applied to the Code Converter's risk level classification output in order to optimize the risk levels for the characterization of the patient. In this study, a group of 10 patients with known epilepsy findings are utilized here. The Performance metrics is computed with the help of parameters like Performance Index (PI) and Quality Values (QV).

A real time experimental setup for classification of epilepsy risk levels

The objective of this paper is to analyze the performance of singular value decomposition, expectation maximization, and Elman Neural Networks in optimization of code converter outputs in the classification of epilepsy risk levels from EEG (electroencephalogram) signals. The signal parameters such as the total number of positive and negative peaks, spikes and sharp waves, their duration etc., were extracted using morphological operators and wavelet transforms. Code converters were considered as a level one classifier. Code converters were found to have a performance index and quality value of 33.26 and 12.74, respectively, which is low. Consequently, for the EEG signals of 20 patients, the post classifiers were applied across 3 epochs of 16 channels. After having made a comparative study of different architectures, SVD was found to be the best post classifier as it marked a performance index of 89.48 and a quality value of 20.62. Elman neural network also exhibits good performance metrics than SVD in the morphological operator based feature extraction method.

Performance analysis of wavelet transforms and morphological operator-based classification of epilepsy risk levels

The objective of this paper is to compare the performance of singular value decomposition (SVD), expectation maximization (EM), and modified expectation maximization (MEM) as the postclassifiers for classifications of the epilepsy risk levels obtained from extracted features through wavelet transforms and morphological filters from electroencephalogram (EEG) signals. The code converter acts as a level one classifier. The seven features such as energy, variance, positive and negative peaks, spike and sharp waves, events, average duration, and covariance are extracted from EEG signals. Out of which four parameters like positive and negative peaksand spike and sharp waves, events and average duration are extracted using Haar, dB2, dB4, and Sym 8 wavelet transforms with hard and soft thresholding methods. The above said four features are also extracted through morphological filters. Then, the performance of the code converter and classifiers are compared based on the parameters such as performance index (PI) and quality value (QV).The performance index and quality value of code converters are at low value of 33.26% and 12.74, respectively. The highest PI of 98.03% and QV of 23.82 are attained at dB2 wavelet with hard thresholding method for SVD classifier. All the postclassifiers are settled at PI value of more than 90% at QV of 20.

Wavelets and Morphological Operators Based Classification of Epilepsy Risk Levels

The objective of this paper is to compare the performance of Singular Value Decomposition (SVD), Expectation Maximization (EM), and Modified Expectation Maximization (MEM) as the postclassifiers for classifications of the epilepsy risk levels obtained from extracted features through wavelet transforms and morphological filters from EEG signals. The code converter acts as a level one classifier. The seven features such as energy, variance, positive and negative peaks, spike and sharp waves, events, average duration, and covariance are extracted from EEG signals, out of which four parameters like positive and negative peaks, spike and sharp waves, events, and average duration are extracted using Haar, dB2, dB4, and Sym8 wavelet transforms with hard and soft thresholding methods. The above said four features are also extracted through morphological filters. The performance of the code converter and classifiers are compared based on the parameters such as Performance Index (PI) and Quality Value (QV). The Performance Index and Quality Value of code converters are at low value of 33.26% and 12.74, respectively. The highest PI of 98.03% and QV of 23.82 are attained at dB2 wavelet with hard thresholding method for SVD classifier. All the postclassifiers are settled at PI value of more than 90% at QV of 20.

Performance Analysis of Patient Specific Elman-Chaotic Optimization Model for Fuzzy Based Epilepsy Risk Level Classification from Eeg Signals

Abstract This paper aims to analyze the optimization of Epilepsy risk levels from EEG signals using Fuzzy based Elman-Chaotic Optimization. The EEG (Electroencephalogram) signals of twenty patients are collected from Sri Ramakrishna Hospitals at Coimbatore. The raw EEG signals are sampled and various parameters like energy, energy, variance, peaks, sharp and spike waves, duration, events and covariance. The fuzzy techniques are applied as a first level classifier to classify the risk levels of epilepsy by converting the EEG signal parameters in to code patterns by fuzzy systems. Elman-Chaotic optimization is identified as post classifiers on the classified data to obtain the optimized risk level that characterizes the patient’s epilepsy risk level. This classification provides a better way of treating the epileptic patients. This project aims to safeguard a patient’s life when critical situation occurs. Future scope is to design an embedded system which collects the raw EEG signals from the brain and directly gives the level of epilepsy. It will make the neural surgeons to give appropriate remedial measures.

Analysis of Fuzzy Techniques and Neural Networks (RBF&MLP) in Classification of Epilepsy Risk Levels from EEG Signals

Most research to date using hybrid systems (Fuzzy-Neuro) focused on the Multi-Layer Perceptron (MLP). Alternative neural network approaches such as the Radial Basis Function (RBF) network, and their representations appear to have received relatively little attention. Here we focus on RBF network as an optimizer for classification of epilepsy risk level obtained from the fuzzy techniques using the EEG signals parameters. The obtained risk level patterns from fuzzy techniques are found to have low values of Performance Index (PI) and Quality Value (QV). These neural networks are trained and tested with 480 patterns extracted from three epochs of sixteen channel EEG signals of ten known epilepsy patients. Different architectures of MLP and RBF networks are compared based on the minimum Mean Square Error (MSE), the better networks in MLP (16-16-1) and RBF (1-16-1) are selected. RBF out performs the MLP network and fuzzy techniques with the high Quality Value of 23.98 when compared to the Quality Values of 16.92 and 6.25.

Fuzzy Techniques with Aggregation Operators for Classification and Optimization of Epilepsy Risk Level from EEG Signals

The purpose of this paper is to develop a fuzzy classification model for epilepsy risk level analysis from EEG signals. The parametric values such as energy, variance, and duration, covariance, positive and negative peaks, sharp and spike waves and events are derived in each epoch of two second duration in the EEG signal channels. Fuzzy techniques are used to classify the risk level in each epoch for all the channels, the risk level patterns obtained are found to have low values of sensitivity, specificity, performance index and quality value. In order to increase the classification rate, an optimization technique based on aggregation operators is used and a quality value of 23.78 is achieved when compared to the value 6.25 achieved in the previous case. A comparison of fuzzy techniques without and with optimization is studied. The focal epilepsy problem in normal fuzzy classification is solved using this new approach. A group of ten patients with known epilepsy findings are used in this study. Further research work can be carried out in the classification of epilepsy risk level of a long-term EEG signals about 3 minute's durations. The number of samples may be increased to improve the classification rate.