An intra-operative feature-based classification of microelectrode recordings to support the subthalamic nucleus functional identification during deep brain stimulation surgery.

Objective. The subthalamic nucleus (STN) is the most selected target for the placement of the Deep Brain Stimulation (DBS) electrode to treat Parkinson's disease. Its identification is a delicate and challenging task which is based on the interpretation of the STN functional activity acquired through microelectrode recordings (MERs). Aim of this work is to explore the potentiality of a set of 25 features to build a classification model for the discrimination of MER signals belonging to the STN.Approach.We explored the use of different sets of spike-dependent and spike-independent features in combination with an ensemble trees classification algorithm on a dataset composed of 13 patients receiving bilateral DBS. We compared results from six subsets of features and two dataset conditions (with and without standardization) using performance metrics on a leave-one-patient-out validation schema.Main results.We obtained statistically better results (i.e. higher accuracyp-value = 0.003) on the RAW dataset than on the standardized one, where the selection of seven features using a minimum redundancy maximum relevance algorithm provided a mean accuracy of 94.1%, comparable with the use of the full set of features. In the same conditions, the spike-dependent features provided the lowest accuracy (86.8%), while a power density-based index was shown to be a good indicator of STN activity (92.3%).Significance.Results suggest that a small and simple set of features can be used for an efficient classification of MERs to implement an intraoperative support for clinical decision during DBS surgery.

Characterization of Microelectrode Recordings for the Subthalamic Nucleus identification in Parkinson's disease.

Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is an effective treatment for Parkinson's disease, when the pharmacological approach has no more effect. DBS efficacy strongly depends on the accurate localization of the STN and the adequate positioning of the stimulation electrode during DBS stereotactic surgery. During this procedure, the analysis of microelectrode recordings (MER) is fundamental to assess the correct localization. Therefore, in this work, we explore different signal feature types for the characterization of the MER signals associated to STN from NON-STN structures. We extracted a set of spike-dependent (action potential domain) and spike-independent features in the time and frequency domain to evaluate their usefulness in distinguishing the STN from other structures. We discuss the results from a physiological and methodological point of view, showing the superiority of features having a direct electrophysiological interpretation.Clinical Relevance- The identification of a simple, clinically interpretable, and powerful set of features for the STN localization would support the clinical positioning of the DBS electrode, improving the treatment outcome.