Assessment of Epilepsy Classification Using Techniques Such as Singular Value Decomposition, Approximate Entropy, and Weighted K-nearest Neighbors Measures

Objective: The main aim of this research is to reduce the dimension of the epileptic Electroencephalography (EEG) signals and then classify it using various post classifiers. For the evaluation and easy treatment of neurological diseases, EEG signals are used. The reflection of the electrical activities of 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 used by 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 of many neurological diseases which include epilepsy, sleep disorders, injuries in head, dementia etc. One of the most commonly occurring and prevalent neurological 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 Component Analysis (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 Weighted KNN (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 when LDA is engaged as a dimensionality reduction technique followed by the usage of the W-KNN as post classifier for the classification of epilepsy risk levels from EEG signals. Future work may incorporate the possible usage of different dimensionality reduction techniques with various other types of classifiers for the perfect classification of epilepsy risk levels from EEG signals.