Journal article
Stop! Border Ahead: Automatic Detection of Subthalamic Exit During Deep Brain Stimulation Surgery
Movement disorders, Vol.32(1), pp.70-79
01/2017
DOI: 10.1002/mds.26806
PMCID: PMC5478927
PMID: 27709666
Abstract
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 crossvalidation). 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 0.0460.18mm 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. VC 2016 International Parkinson and Movement Disorder Society.
Details
- Title: Subtitle
- Stop! Border Ahead: Automatic Detection of Subthalamic Exit During Deep Brain Stimulation Surgery
- Creators
- Dan Valsky - Hebrew University of JerusalemOdeya Marmor-Levin - Hebrew University of JerusalemMarc Deffains - Hebrew University of JerusalemRenana Eitan - Hadassah Medical CenterKim T. Blackwell - George Mason UniversityHagai Bergman - Hebrew University of JerusalemZvi Israel - Hadassah Medical Center
- Resource Type
- Journal article
- Publication Details
- Movement disorders, Vol.32(1), pp.70-79
- Publisher
- Wiley
- DOI
- 10.1002/mds.26806
- PMID
- 27709666
- PMCID
- PMC5478927
- ISSN
- 0885-3185
- eISSN
- 1531-8257
- Number of pages
- 10
- Grant note
- Magnet program of the Office of the Chief Scientist (OCS) of the Israel Ministry of Economy R01 016022 / NIAAA; United States Department of Health & Human Services; National Institutes of Health (NIH) - USA; NIH National Institute on Alcohol Abuse & Alcoholism (NIAAA) R01DA038890 / NATIONAL INSTITUTE ON DRUG ABUSE; United States Department of Health & Human Services; National Institutes of Health (NIH) - USA; NIH National Institute on Drug Abuse (NIDA); European Commission R01 038890 / NIDA; United States Department of Health & Human Services; National Institutes of Health (NIH) - USA; NIH National Institute on Drug Abuse (NIDA) R01AA016022 / NATIONAL INSTITUTE ON ALCOHOL ABUSE AND ALCOHOLISM; United States Department of Health & Human Services; National Institutes of Health (NIH) - USA; NIH National Institute on Alcohol Abuse & Alcoholism (NIAAA)
- Language
- English
- Date published
- 01/2017
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering
- Record Identifier
- 9984446539002771
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