SIA OpenIR  > 机器人学研究室
Online identification of functional regions in deep brain stimulation based on an unsupervised random forest with feature selection
Cao L(曹蕾)1,2,3; Li J(李杰)1,4,5; Zhou YY(周圆圆)1,2,3,4; Liu YH(刘云会)6; Zhao YW(赵忆文)1,2,4; Liu H(刘浩)1,2,4
Department机器人学研究室
Source PublicationJOURNAL OF NEURAL ENGINEERING
ISSN1741-2560
2019
Volume16Issue:6Pages:1-13
Indexed BySCI ; EI
EI Accession number20195207894534
WOS IDWOS:000499833000001
Contribution Rank1
Funding OrganizationNational Natural Science Foundation of ChinaNational Natural Science Foundation of China [61873257] ; Open-planned Project from State Key Laboratory of Robotics in China [2017-O10] ; Self-planned Project from State Key Laboratory of Robotics in China [2019-Z05]
Keyworddeep brain stimulation microelectrode recording unsupervised random forest feature selection functional region online identification
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.

Language英语
WOS SubjectEngineering, Biomedical ; Neurosciences
WOS KeywordHUMAN SUBTHALAMIC NUCLEUS ; CLASSIFICATION
WOS Research AreaEngineering ; Neurosciences & Neurology
Funding ProjectNational Natural Science Foundation of China[61873257] ; Open-planned Project from State Key Laboratory of Robotics in China[2017-O10] ; Self-planned Project from State Key Laboratory of Robotics in China[2019-Z05]
Citation statistics
Cited Times:2[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/25975
Collection机器人学研究室
Corresponding AuthorLiu H(刘浩)
Affiliation1.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, Liaoning, China
2.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, Liaoning, China
3.University of Chinese Academy of Sciences, Beijing, China
4.Key Laboratory of Minimally Invasive Surgical Robot, Liaoning Province, Shenyang, Liaoning, China
5.School of Mechanical Engineering, Shenyang Jianzhu University, Shenyang, Liaoning, China
6.Department of Neurosurgery, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China
Recommended Citation
GB/T 7714
Cao L,Li J,Zhou YY,et al. Online identification of functional regions in deep brain stimulation based on an unsupervised random forest with feature selection[J]. JOURNAL OF NEURAL ENGINEERING,2019,16(6):1-13.
APA Cao L,Li J,Zhou YY,Liu YH,Zhao YW,&Liu H.(2019).Online identification of functional regions in deep brain stimulation based on an unsupervised random forest with feature selection.JOURNAL OF NEURAL ENGINEERING,16(6),1-13.
MLA Cao L,et al."Online identification of functional regions in deep brain stimulation based on an unsupervised random forest with feature selection".JOURNAL OF NEURAL ENGINEERING 16.6(2019):1-13.
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