Detection of temporal lobe epilepsy using support vector machines in multi-parametric quantitative MR imaging

• Measuring regional asymmetry is fundamental for optimal classification results.
• DTI derived measures seem to be more informative than T2 maps for classification of TLE patients.
• Best classification accuracy for left TLE was 100% and for right TLE was 88.9%.
• Most of the discriminative features belong to the temporal lobes.
• The right TLE group is difficult to distinguish from controls. Possible factors are pathology heterogeneity and a limited sample size.

The detection of MRI abnormalities that can be associated to seizures in the study of temporal lobe epilepsy (TLE) is a challenging task. In many cases, patients with a record of epileptic activity do not present any discernible MRI findings. In this domain, we propose a method that combines quantitative relaxometry and diffusion tensor imaging (DTI) with support vector machines (SVM) aiming to improve TLE detection. The main contribution of this work is two-fold: on one hand, the feature selection process, principal component analysis (PCA) transformations of the feature space, and SVM parameterization are analyzed as factors constituting a classification model and influencing its quality. On the other hand, several of these classification models are studied to determine the optimal strategy for the identification of TLE patients using data collected from multi-parametric quantitative MRI.A total of 17 TLE patients and 19 control volunteers were analyzed. Four images were considered for each subject (T1 map, T2 map, fractional anisotropy, and mean diffusivity) generating 936 regions of interest per subject, then 8 different classification models were studied, each one comprised by a distinct set of factors. Subjects were correctly classified with an accuracy of 88.9%. Further analysis revealed that the heterogeneous nature of the disease impeded an optimal outcome. After dividing patients into cohesive groups (9 left-sided seizure onset, 8 right-sided seizure onset) perfect classification for the left group was achieved (100% accuracy) whereas the accuracy for the right group remained the same (88.9%).We conclude that a linear SVM combined with an ANOVA-based feature selection + PCA method is a good alternative in scenarios like ours where feature spaces are high dimensional, and the sample size is limited. The good accuracy results and the localization of the respective features in the temporal lobe suggest that a multi-parametric quantitative MRI, ROI-based, SVM classification could be used for the identification of TLE patients. This method has the potential to improve the diagnostic assessment, especially for patients who do not have any obvious lesions in standard radiological examinations.