Detecting Epileptic Activities from resting fMRI Time-course by using PCA algorithm

Abstract

A delay principal component analysis (PCA) novel method is presented for detecting interictal epileptic activities from resting functional magnetic resonance time-course dataset. The proposed method consists of three steps: (1) calculating the covariance matrix with time delay and then conducting SVD on the matrix; (2) calculating the weighted correlation coefficient and applying T-test to every voxel; (3) only those voxel whose weighted correlation coefficient T-value are larger than the threshold (p<0.02) and whose mean and standard deviation belong to each special scope are considered as the epileptic foci. In contrast with traditional PCA methods, our algorithm further considers time delay and makes T-test for weighted correlation coefficient rather than voxel value. Computer simulation and vivo epilepsy fMRI data analysis demonstrate the potential of this technique to localize epilepsy foci