A Unique Approach to Epilepsy Classification from EEG Signals Using Dimensionality Reduction and Neural Networks

Harikumar Rajaguru,Sunil Kumar Prabhakar

doi:10.4236/cs.2016.78127

Abstract

Characterized by recurrent and rapid seizures, epilepsy is a great threat to the livelihood of the human beings. Abnormal transient behaviour of neurons in the cortical regions of the brain leads to a seizure which characterizes epilepsy. The physical and mental activities of the patient are totally dampened with this epileptic seizure. A significant clinical tool for the study, analysis and diagnosis of the epilepsy is electroencephalogram (EEG). To detect such seizures, EEG signals aids greatly to the clinical experts and it is used as an important tool for the analysis of brain disorders, especially epilepsy. In this paper, the high dimensional EEG data are reduced to a low dimension by incorporating techniques such as Fuzzy Mutual Information (FMI), Independent Component Analysis (ICA), Linear Graph Embedding (LGE), Linear Discriminant Analysis (LDA) and Variational Bayesian Matrix Factorization (VBMF). After employing them as dimensionality reduction techniques, the Neural Networks (NN) such as Cascaded Feed Forward Neural Network (CFFNN), Time Delay Neural Network (TDNN) and Generalized Regression Neural Network (GRNN) are used as Post Classifiers for the Classification of Epilepsy Risk Levels from EEG signals. The bench mark parameters used here are Performance Index (PI), Quality Values (QV), Time Delay, Accuracy, Specificity and Sensitivity.

Highlights

For a majority of the biomedical scientists, medical practitioners and biomedical engineers, a lot of research is inHow to cite this paper: Rajaguru, H. and Prabhakar, S.K. (2016) A Unique Approach to Epilepsy Classification from EEG Signals Using Dimensionality Reduction and Neural Networks
For the performance assessment of the epilepsy risk levels using the Fuzzy Mutual Information (FMI), Independent Component Analysis (ICA), Linear Graph Embedding (LGE), Linear Discriminant Analysis (LDA) and Variational Bayesian Matrix Factorization (VBMF) as Dimensionality Reduction technique followed by Neural Networks (NN) as Post Classifiers, the raw EEG data of 20 epileptic patients who were under treatment in the Neurology Department of Sri Ramakrishna Hospital, Coimbatore in European Data Format (EDF) are taken for study
For FMI, ICA, LGE, LDA and VBMF as dimensionality reduction techniques and Neural Networks as Post Classifiers, based on the Performance Index, Quality values, Time Delay and Accuracy the simulated result values are plotted in Tables 1-3 respectively

Summary

Introduction

For a majority of the biomedical scientists, medical practitioners and biomedical engineers, a lot of research is inHow to cite this paper: Rajaguru, H. and Prabhakar, S.K. (2016) A Unique Approach to Epilepsy Classification from EEG Signals Using Dimensionality Reduction and Neural Networks. How to cite this paper: Rajaguru, H. and Prabhakar, S.K. K. Prabhakar progress about the functioning of the human brain [1]. The brain is a very complex and important organ of a human body where the interconnection of the neurons happens with both the remote and local ones. Since epilepsy greatly affects the quality of life of humans on a day to day basis, tremendous attention is drawn towards this particular disorder. The local or the remote interactions of the neurons in the brain are projected as the spatio-temporal electromagnetic field of the brain and EEG recordings are made [2]. To measure the activities of the brain, the only direct way is processing EEG and in the area of biomedical research it holds a paramount importance

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