TITLE:
A Unique Approach to Epilepsy Classification from EEG Signals Using Dimensionality Reduction and Neural Networks
AUTHORS:
Harikumar Rajaguru, Sunil Kumar Prabhakar
KEYWORDS:
LDA, FMI, ICA, LGE, VBMF, NN
JOURNAL NAME:
Circuits and Systems,
Vol.7 No.8,
June
13,
2016
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.