Online identification of functional regions in deep brain stimulation based on an unsupervised random forest with feature selection. (23rd October 2019)

• Record Type: Journal Article
• Authors: Cao, Lei; Li, Jie; Zhou, Yuanyuan; Liu, Yunhui; Zhao, Yiwen; Liu, Hao
• Is Part Of: Journal of neural engineering. Volume 16:Number 6(2019:Dec.)
• Publication Date: 2019-10-23
• Subjects: deep brain stimulation -- microelectrode recording -- unsupervised random forest -- feature selection -- functional region online identification; Neurosciences -- Periodicals; Biomedical engineering -- Periodicals; 612.8
• DOI: 10.1088/1741-2552/ab2eb4
• Languages: English
• ISSNs: 1741-2560

Abstract

Abstract: Objective . The identification of functional regions, in particular the subthalamic nucleus, through microelectrode recording (MER) is the key step in deep brain stimulation (DBS). To eliminate variability in a neurosurgeon's judgment, this study presents an online identification method for identifying functional regions along the electrode trajectory. Approach . Functional regions can be identified through offline clustering and online identification based on the unsupervised random forest (RF) algorithm. We took 106 features from MER and the estimated anatomical distance to target as the dataset to train the RF model. To improve the prediction performance and reduce the computation time, a wrapper feature selection (FS) method was added into the algorithm. The method contains feature ranking based on out-of-bag error or silhouette index and feature subset search based on the roulette selection algorithm. Main results . To evaluate the optimization effect of the FS method on the unsupervised RF algorithm, we compared the results of the algorithm with or without FS on the DBS dataset. In addition, the optimization effect of FS on the computation time is evaluated. The results show that for offline clustering, the accuracy obtained with the selected features is higher than that obtained with all features, and the running time decreased from 259.7 s to 60.8 s in the iteration of the FS. The accuracy in online identification improved from 76.19% to 92.08% through FS. In addition, the functional region online identification time is 41.5 ms, which can meet the requirements of DBS surgery. Significance . In conclusion, using the FS method can improve the accuracy and reduce the computation time of the online identification of functional regions. In addition, the online identification method can provide valuable assistance for both neurosurgeons and stereotactic surgery robots in guiding implantation of the electrode in real time.