Journal article
Real-time machine learning classification of pallidal borders during deep brain stimulation surgery
Journal of neural engineering, Vol.17(1), pp.016021-016021
01/06/2020
DOI: 10.1088/1741-2552/ab53ac
PMID: 31675740
Abstract
Objective. Deep brain stimulation (DBS) of the internal segment of the globus pallidus (GPi) in patients with Parkinson's disease and dystonia improves motor symptoms and quality of life. Traditionally, pallidal borders have been demarcated by electrophysiological microelectrode recordings (MERs) during DBS surgery. However, detection of pallidal borders can be challenging due to the variability of the firing characteristics of neurons encountered along the trajectory. MER can also be time-consuming and therefore costly. Here we show the feasibility of real-time machine learning classification of striato-pallidal borders to assist neurosurgeons during DBS surgery. Approach. An electrophysiological dataset from 116 trajectories of 42 patients consisting of 11 774 MER segments of background spiking activity in five classes of disease was used to train the classification algorithm. The five classes included awake Parkinson's disease patients, as well as awake and lightly anesthetized genetic and non-genetic dystonia patients. A machine learning algorithm was designed to provide prediction of the striato-pallidal borders, based on hidden Markov models (HMMs) and the L-1-distance measure in normalized root mean square (NRMS) and power spectra of the MER. We tested its performance prospectively against the judgment of three electrophysiologists in the operating rooms of three hospitals using newly collected data. Main results. The awake and the light anesthesia dystonia classes could be merged. Using MER NRMS and spectra, the machine learning algorithm was on par with the performance of the three electrophysiologists across the striatum-GPe, GPe-GPi, and GPi-exit transitions for all disease classes. Significance. Machine learning algorithms enable real-time GPi navigation systems to potentially shorten the duration of electrophysiological mapping of pallidal borders, while ensuring correct pallidal border detection.
Details
- Title: Subtitle
- Real-time machine learning classification of pallidal borders during deep brain stimulation surgery
- Creators
- Dan Valsky - Hebrew University of JerusalemKim T. Blackwell - George Mason UniversityIdit Tamir - Rabin Medical CenterRenana Eitan - Hebrew University of JerusalemHagai Bergman - Hebrew University of JerusalemZvi Israel - Hadassah Medical Center
- Resource Type
- Journal article
- Publication Details
- Journal of neural engineering, Vol.17(1), pp.016021-016021
- DOI
- 10.1088/1741-2552/ab53ac
- PMID
- 31675740
- NLM abbreviation
- J Neural Eng
- ISSN
- 1741-2560
- eISSN
- 1741-2552
- Publisher
- Iop Publishing Ltd
- Number of pages
- 15
- Grant note
- Magnet program of the Israel Innovation Authority of the Israel Ministry of Economy
- Language
- English
- Date published
- 01/06/2020
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Iowa Neuroscience Institute
- Record Identifier
- 9984446404802771
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