Detection of SNr recordings basing upon spike shape classes and signal’s background

Abstract

During deep brain stimulation (DBS) surgery for Parkinson disease, the target is the subthalamic nucleus (STN). STN is small, (9 × 7 × 4mm) and typically localized by a series of parallel microelectrodes. As those electrodes are in steps advanced towards and through the STN, they record the neurobiological activity of the surrounding tissues. The electrodes are advanced until they pass through the STN and/or they reach the Substantia Nigra pars reticulata (SNr). There is no necessity of going further as the SNr lies ventral to the STN. There are good classification methods for detection weather given recording comes from the STN or not, they still do sometimes falsely identify SNr recordings as STN ones. This paper focuses on method devised for SNr detection, specifically on detection if given recording bears characteristics typical for SNr. Presented method relies on spike sorting and assessing characteristics of the obtained spike shape classes together with the enhanced analysis of the signal’s background computed by the STN classification methods described in [8–12].