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

CORC > 沈阳自动化研究所 > 中国科学院沈阳自动化研究所 > 机器人学研究室

	Automatic feature group combination selection method based on GA for the functional regions clustering in DBS
	Liu H(刘浩)3,5,6 ; Cao L(曹蕾)4,5,6 ; Li J(李杰)2,3,6; Zhou YY(周圆圆)3,4,5,6 ; Liu YH(刘云辉)1
刊名	Computer Methods and Programs in Biomedicine
	2020
卷号	183 页码:1-10
关键词	Divisive hierarchical clustering Feature group combination selection Functional regions clustering Genetic algorithm STN borders identification
ISSN号	0169-2607
产权排序	1
英文摘要	Background and Objective: The functional regions clustering through microelectrode recording (MER) is a critical step in deep brain stimulation (DBS) surgery. The localization of the optimal target highly relies on the neurosurgeon's empirical assessment of the neurophysiological signal. This work presents an unsupervised clustering algorithm to get the optimal cluster result of the functional regions along the electrode trajectory. Methods: The dataset consists of the MERs obtained from the routine bilateral DBS for PD patients. Several features have been extracted from MER and divided into groups based on the type of neurophysiological signal. We selected single feature groups rather than all features as the input samples of each division of the divisive hierarchical clustering (DHC) algorithm. And the optimal cluster result has been achieved through a feature group combination selection (FGS) method based on genetic algorithm (GA). To measure the performance of this method, we compared the accuracy and validation indexes of three methods, including DHC only, DHC with GA-based FGS and DHC with GA-based feature selection (FS) in other studies, on the universal and DBS datasets. Results: Results show that the DHC with GA-based FGS achieved the optimal cluster result compared with other methods. The three borders of the STN can be identified from the cluster result. The dorsoventral sizes of the STN and dorsal STN are 4.45 mm and 2.02 mm. In addition, the features extracted from the multiunit activity, background unit activity and local field potential are found to be the most representative feature groups to identify the dorsal, D-v and ventral borders of the STN, respectively. Conclusions: Our clustering algorithm showed a reliable performance of the automatic identification of functional regions in DBS. The findings can provide valuable assistance for both neurosurgeons and stereotactic surgical robots in DBS surgery.
语种	英语
资助机构	National Natural Science Foundation of China (No. 61873257) ; Open-planned Project from State Key Laboratory of Robotics in China (No. 2017-O10) ; Self-planned Project from State Key Laboratory of Robotics in China (No. 2019-Z05) ; Liaoning Provincial Union Science Foundation (No. 2019-KF-01-03)
内容类型	期刊论文
源URL	[http://ir.sia.cn/handle/173321/25664]
专题	沈阳自动化研究所_机器人学研究室
通讯作者	Liu H(刘浩); Li J(李杰)
作者单位	1.Department of Neurosurgery, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China 2.School of Mechanical Engineering, Shenyang Jianzhu University, Shenyang, Liaoning, China 3.Key Laboratory of Minimally Invasive Surgical Robot, Liaoning Province, Shenyang, Liaoning, China 4.University of Chinese Academy of Sciences, Beijing, China 5.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, Liaoning, China 6.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, Liaoning, China
推荐引用方式 GB/T 7714	Liu H,Cao L,Li J,et al. Automatic feature group combination selection method based on GA for the functional regions clustering in DBS[J]. Computer Methods and Programs in Biomedicine,2020,183:1-10.
APA	Liu H,Cao L,Li J,Zhou YY,&Liu YH.(2020).Automatic feature group combination selection method based on GA for the functional regions clustering in DBS.Computer Methods and Programs in Biomedicine,183,1-10.
MLA	Liu H,et al."Automatic feature group combination selection method based on GA for the functional regions clustering in DBS".Computer Methods and Programs in Biomedicine 183(2020):1-10.