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Comparison of Isomap and matrix factorization with mahalanobis based sparse representation classifier for epilepsy classification from EEG signals

Brain is quite an amazing but complex organ in the human body comprising of billions of nerve cells. Epilepsy is a medical condition associated with brain where the recurrent seizures occur spontaneously. The normal tasks of the brain are inhibited during the seizure period and so people experience a lot of abnormal activities in the muscle sensation, movement and behaviour. The nerve cells of the brain named neurons utilize chemical reaction to generate electrical signals and so the brain can be called as an electro-chemical machine. The measurement of these electrical signals and the changes in them in terms of fluctuations of voltage can be measured by Electroencephalography (EEG) signals. When multiple electrodes are placed on the scalp of the epileptic patient, the recordings can be analyzed easily. Due to the long nature of the recordings of the EEG, processing so much data is very difficult. In this paper, Matrix Factorization and Isomap are utilized as two prominent dimensionality reduction techniques to reduce the dimensions of the original EEG signals. Then with the help of Mahalanobis based Sparse Representation Classifier (M-SRC), the dimensionally mitigated signals are classified for epilepsy. The results show than an average classification accuracy of 98.02% along with an average quality value of 22.01 is obtained when Isomap is classified with M-SRC. Similarly when Matrix Factorization is classified with M-SRC, an average classification accuracy of 97.77% along with an average quality value of 22.96 is obtained.