A Comparative Study of Feature Selection Methods for the Discriminative Analysis of Temporal Lobe Epilepsy

It's very crucial to differentiate the temporal lobe epilepsy (TLE) patients from healthy population and localize the aberrant brain regions of the TLE patients. The cortical features and changes can reveal the unique anatomical patterns of brain regions from the structural MR images. In this study, structural MR images from 41 left TLE (LTLE), and 34 right TLE (RTLE), 58 normal controls (NC) were acquired, and four cortical features, namely cortical thickness (CTh), cortical surface area (CSA), gray matter volume (GMV), and mean curvature (MCu), were explored for discriminative analysis. Three feature selection methods including the independent sample t-test filtering, the sparse-constrained dimensionality reduction model (SCDRM), and the support vector machine-recursive feature elimination (SVM-RFE), were investigated to extract dominant features among the compared groups for classification using the SVM classifier. The results showed that the SVM-REF achieved the highest performance (most classifications with more than 84% accuracy), followed by the SCDRM, and the t-test. Especially, the surface area and gray volume matter exhibited prominent discriminative ability, and the performance of the SVM was improved significantly when the four cortical measures were combined. Additionally, the dominant regions with higher classification weights were mainly located in the temporal and the frontal lobe, including the entorhinal cortex, rostral middle frontal, parahippocampal cortex, superior frontal and insula and cuneus. It was demonstrated that the cortical features provided effective information for the recognition of abnormal anatomical pattern and the proposed method had the potential to improve the clinical diagnosis of the TLE.