Neural cell type recognition between globus pallidus externus and globus pallidus internus by Gaussian mixture modeling

This paper proposes a new application of Gaussian mixture modeling (GMM) for neural cell type classification between globus pallidus externus (GPe) and globus pallidus internus (GPi). Our work is motivated by the results of previous research in which different neural cell types could be identified by their discharge patterns. It is critical for surgeons to distinguish between these two cell types to identify brain nuclei during the procedure known as a pallidotomy, a treatment for Parkinson's disease. Currently, skilled surgeons rely on discharge patterns converted to sound. In this study, performance evaluations are conducted based on a labeled database recorded during previous neural surgeries. The GMM achieves better than 92% correct recognition using 10-second segments, which demonstrates the best performing classifier to date.