title: Hidden semi-Markov models in the computerized decoding of microelectrode recording data for deep brain stimulator placement.
abstract: 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.
authors.affiliation: Department of Neurological Surgery, Keck School of Medicine, University of Southern California, Los Angeles, California, USA. taghva@usc.edu
authors.forename: Alexander
authors.initials: A
authors.lastname: Taghva
mesh_terms: D001921:Brain / Q000502:physiology; D046690:Deep Brain Stimulation / Q000379:methods* / Q000706:statistics & numerical data*; D004567:Electrodes, Implanted; D055724:Electrophysiological Phenomena; D006801:Humans; D008390:Markov Chains; D008839:Microelectrodes; D015233:Models, Statistical; D013378:Substantia Nigra / Q000502:physiology; D020530:Subthalamus / Q000502:physiology; D013788:Thalamus / Q000502:physiology