2D-DOST for seizure identification from brain MRI during pregnancy using KRVFL

A paradigm shift is observed in medical domain when it comes to automated identification of several human ailments. Many a research activities are still being carried out in this context towards accuracy and robustness. Brain related disease identification has always been a challenge in this direction. Although handful of benchmark schemes exist related to brain ailment (mainly seizure) identification, however, the specific case of seizure identification for pregnant women has been little explored. In this paper, such a scheme has been proposed that identifies seizure based on the magnetic resonance (MR) digital images especially of pregnant women. The proposed scheme first transforms the MR image through efficient two dimensional discrete orthonormal Stockwell transform (2D-DOST) to obtain meaningful vectors. Further, for generating the final feature vector, the regularized discriminant analysis (RDA) is used. This is followed by the task of identification through classification. There are two classes in this case namely, seizure and no-seizure. This identification is achieved through the utilization of random vector functional link network (RVFL). Along with the typical kernel extension it is dubbed KRVFL. Suitable experimental analysis is conducted that reveals satisfactory results in support of the proposed work with an overall rate of accuracy being 94.5%, 93%, and 92% for the specific samples (seizure during pregnancy), and two other sample sets respectively. The performance measure is done through a k-fold cross validation calculation. Performance comparison with other competent schemes shows that the proposed scheme is marginally efficient.