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S top! border ahead: A utomatic detection of subthalamic exit during deep brain stimulation surgery
Author(s) -
Valsky Dan,
MarmorLevin Odeya,
Deffains Marc,
Eitan Renana,
Blackwell Kim T.,
Bergman Hagai,
Israel Zvi
Publication year - 2017
Publication title -
movement disorders
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.352
H-Index - 198
eISSN - 1531-8257
pISSN - 0885-3185
DOI - 10.1002/mds.26806
Subject(s) - subthalamic nucleus , deep brain stimulation , parkinson's disease , substantia nigra , microelectrode , neuroscience , artificial intelligence , computer science , hidden markov model , pattern recognition (psychology) , medicine , psychology , physics , pathology , disease , electrode , quantum mechanics
Background Microelectrode recordings along preplanned trajectories are often used for accurate definition of the subthalamic nucleus (STN) borders during deep brain stimulation (DBS) surgery for Parkinson's disease. Usually, the demarcation of the STN borders is performed manually by a neurophysiologist. The exact detection of the borders is difficult, especially detecting the transition between the STN and the substantia nigra pars reticulata. Consequently, demarcation may be inaccurate, leading to suboptimal location of the DBS lead and inadequate clinical outcomes. Methods We present machine‐learning classification procedures that use microelectrode recording power spectra and allow for real‐time, high‐accuracy discrimination between the STN and substantia nigra pars reticulata. Results A support vector machine procedure was tested on microelectrode recordings from 58 trajectories that included both STN and substantia nigra pars reticulata that achieved a 97.6% consistency with human expert classification (evaluated by 10‐fold cross‐validation). We used the same data set as a training set to find the optimal parameters for a hidden Markov model using both microelectrode recording features and trajectory history to enable real‐time classification of the ventral STN border (STN exit). Seventy‐three additional trajectories were used to test the reliability of the learned statistical model in identifying the exit from the STN. The hidden Markov model procedure identified the STN exit with an error of 0.04 ± 0.18 mm and detection reliability (error < 1 mm) of 94%. Conclusions The results indicate that robust, accurate, and automatic real‐time electrophysiological detection of the ventral STN border is feasible. © 2016 International Parkinson and Movement Disorder Society.