Deep brain stimulation of the Anterior Nucleus of the Thalamus (ANT-DBS) is an effective therapy in epilepsy. Poorer surgical outcomes are related to deviations of the lead from the ANT-target. The target identification relies on the visualization of anatomical structures by medical imaging, which presents some disadvantages. This study aims to research whether ANT-LFPs recorded with the PerceptTM PC neurostimulator can be an asset in the identification of the DBS-target. For this purpose, 17 features were extracted from LFPs recorded from a single patient, who stayed at an Epilepsy Monitoring Unit for a 5-day period. Features were then integrated into two machine learning (ML)-based methodologies, according to different LFP bipolar montages: Pass1 (nonadjacent channels) and Pass2 (adjacent channels). We obtained an accuracy of 76.6% for the Pass1-classifier and 83.33% for the Pass2-classifier in distinguishing locations completely inserted in the target and completely outside. Then, both classifiers were used to predict the target percentage of all combinations, and we found that contacts 3 (left hemisphere) and 2 and 3 (right hemisphere) presented higher signatures of the ANT-target, which agreed with the medical images. This result opens a new window of opportunity for the use of LFPs in the guidance of DBS target identification.
Keywords: ANT-DBS; LFPs; closed-loop stimulation; epilepsy; target localization.