Hidden semi-Markov models in the computerized decoding of

World Neurosurg · May 2011

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

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. ⋯ Further clinical work must be done; however, based on these data, HsMMs may be best suited to computer-assisted anatomic delineation for DBS.

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- Alexander Taghva.
- Department of Neurological Surgery, Keck School of Medicine, University of Southern California, Los Angeles, California, USA. taghva@usc.edu
- World Neurosurg. 2011 May 1;75(5-6):758-763.e4.
ObjectiveTo 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.MethodsThe author simulated the anatomy and electrophysiology of STN DBS and built a seven-state model to compare Hidden Markov model (HMM) and HsMM approaches.ResultsAccuracy of these competing models was similar for correctly identifying brain nuclei; however, HsMMs showed superior specificity in detecting microelectrode passes traversing the STN.ConclusionsFurther clinical work must be done; however, based on these data, HsMMs may be best suited to computer-assisted anatomic delineation for DBS.Copyright © 2011 Elsevier Inc. All rights reserved.

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

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