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.
epilepsy support vector machines dti tle mri despot fa md quantitative imaging feature selection mrmr anova machine learning svm roi pca