A robust classification model based on iBSA and GCA biomarkers for diagnosis of epilepsy

Presence of neurobiological disorder, like epilepsy, causes abnormalities in brain functionality. This is why connectivity and activity patterns of brain regions in epileptic patients are very different as compared with healthy subjects. These asymmetries can be used to distinguish epileptic patients from healthy subjects. Robust features are extracted to capture asymmetries in connectivity patterns. However, these features do not give any information about the brain activity in a particular local region. So in order to get better classification accuracy, we need robust features to capture asymmetries in regional activities. In this paper, we propose a novel feature set that captures abnormalities in activities at local regional level by finding inter subject blood oxygen level dependent (BOLD) signal asymmetries (iBSA). By combining this iBSA with asymmetries in global connectivity patterns, we are able to capture all asymmetries at global network as well as local regions in only 225 features. Employing these 225 features and support vector machines for classification, an overall accuracy of 86.6% was obtained on resting state fMRI (rfMRI) data of 180 subjects without any feature selection. The results presented in this paper are better than any other reported results in current literature.