Application of morphological filtering with modifications in linear discriminant analysis classifier for epilepsy classification from EEG signals

Abstract

One of the most prominent neurological disorders posing a huge peril to the human community is epilepsy. Because of certain electrical disturbances happening in the function of brain, epilepsy occurs and it is characterized by recurrent seizures. Because of these epileptic seizures, both the physical and mental condition of the patient deteriorates thereby the patient is prone to more physical attacks and injury. Only if the seizures are detected and classified properly, then a good health care can be provided to the patients. For detection of the seizure activities, Electroencephalograph (EEG) signals are used. In this paper, morphological filtering concept is applied to the code converters which is obtained from processing EEG signals and it is employed as a preclassifier, and later it is post classified with Linear Discriminant Analysis (LDA), Log LDA (L-LDA) and Kernel LDA (K-LDA) classifiers. Results show that when LDA is used as a post classifier, an average classification accuracy of 97.39% along with an average quality value of 21.3 is obtained. Similarly if L-LDA is used as a post classifier, then an average classification accuracy of 96.87% along with an average quality value of 21.4 is obtained and when K-LDA is used as a post classifier, then an average classification accuracy of 96.45% along with an average quality value of 20.7 is obtained.