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Automatic feature group combination selection method based on GA for the functional regions clustering in DBS.

Lei Cao1, Jie Li2, Yuanyuan Zhou3

  • 1State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, Liaoning, China; Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, Liaoning, China; University of Chinese Academy of Sciences, Beijing, China.

Computer Methods and Programs in Biomedicine
|October 8, 2019
PubMed
Summary

This study introduces an unsupervised clustering algorithm for deep brain stimulation surgery, improving the identification of functional brain regions. The method enhances neurosurgeon accuracy in targeting optimal electrode placement for better patient outcomes.

Keywords:
Divisive hierarchical clusteringFeature group combination selectionFunctional regions clusteringGenetic algorithmSTN borders identification

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Area of Science:

  • Neurosurgery and Computational Neuroscience
  • Signal Processing and Machine Learning in Medicine

Background:

  • Accurate localization of functional regions during deep brain stimulation (DBS) surgery is crucial for optimal target selection.
  • Current methods rely heavily on neurosurgeon's empirical assessment of microelectrode recording (MER) signals, introducing potential variability.
  • There is a need for objective, automated methods to improve the precision and reliability of target localization in DBS.

Purpose of the Study:

  • To develop and validate an unsupervised clustering algorithm for precise identification of functional regions along the electrode trajectory in DBS surgery.
  • To optimize the clustering process by integrating a genetic algorithm (GA)-based feature group selection (FGS) method.
  • To compare the performance of the proposed algorithm against existing methods for improved target localization.

Main Methods:

  • Utilized microelectrode recording (MER) data from routine bilateral DBS for Parkinson's disease (PD) patients.
  • Extracted and grouped neurophysiological signal features.
  • Employed divisive hierarchical clustering (DHC) with a GA-based FGS method to select optimal feature groups for clustering, and compared with DHC alone and GA-based feature selection (FS).

Main Results:

  • The DHC algorithm combined with GA-based FGS achieved superior clustering results compared to other evaluated methods.
  • Successfully identified the three distinct borders of the subthalamic nucleus (STN), with calculated dorsoventral and dorsal sizes.
  • Determined that multiunit activity, background unit activity, and local field potential features are most representative for identifying STN borders.

Conclusions:

  • The developed unsupervised clustering algorithm demonstrates reliable performance in automatically identifying functional regions for DBS surgery.
  • This automated approach offers significant potential to assist both neurosurgeons and robotic surgical systems in achieving precise targeting.
  • The findings contribute to enhancing the safety and efficacy of deep brain stimulation procedures.