A Non-parametric Approach to Detect Epileptogenic Lesions using Restricted Boltzmann Machines
Yijun Zhao*, Tufts University; Bilal Ahmed, Tufts; Carla Brodley, Northeastern University; Jennifer Dy, NEU
Abstract
Visual detection of lesional areas on a cortical surface is critical in rendering a successful surgical operation for Treatment Resistant Epilepsy (TRE) patients. Unfortunately, 45% of Focal Cortical Dysplasia (FCD, the most common kind of TRE) patients have no visual abnormalities in their brains’ 3D-MRI images. We collaborate with doctors from NYU Langone’s Comprehensive Epilepsy Center and apply ma-chine learning methodologies to identify the resective zones for these MRI-negative FCD patients. Our task is particularly challenging because MRI images can only provide a limited number of features. Furthermore, data from different patients often exhibit inter-patient variabilities due to age, gender, left/right handedness, etc. In this paper, we introduce a new approach which combines the restricted Boltzmann machines and a Bayesian non-parametric mixture model to address these issues. We demonstrate the efficacy of our model by applying it to a retrospective dataset of MRI-negative FCD patients who are seizure free after surgery.
Filed under: Classification