Hidden semi-Markov models in the computerized decoding of microelectrode recording data for deep brain stimulator placement

摘要

Objective: To describe an approach to the analysis of deep brain stimulation (DBS) of the subthalamic nucleus (STN) using a hidden semi-Markov model (HsMM) and early results of the analysis of microelectrode recordings for STN DBS. Methods: The author simulated the anatomy and electrophysiology of STN DBS and built a seven-state model to compare Hidden Markov model (HMM) and HsMM approaches. Results: Accuracy of these competing models was similar for correctly identifying brain nuclei; however, HsMMs showed superior specificity in detecting microelectrode passes traversing the STN. Conclusions: Further clinical work must be done; however, based on these data, HsMMs may be best suited to computer-assisted anatomic delineation for DBS.