Amplitude-frequency-aware deep fusion network for optimal contact selection on STN-DBS electrodes  

作　　者：Linxia XIAO Caizi LI Yanjiang WANG Weixin SI Hai LIN Doudou ZHANG Xiaodong CAI Pheng-Ann HENG 

机构地区：[1]College of Control Science and Engineering,China University of Petroleum(East China),Qingdao 266580,China [2]Shenzhen Institute of Advanced Technology,Chinese Academy of Sciences,Shenzhen 518055,China [3]Department of Neurosurgery,Shenzhen Second People’s Hospital,Shenzhen 518035,China [4]School of Medicine,Shenzhen University,Shenzhen 518000,China [5]Department of Computer Science and Engineering,The Chinese University of Hong Kong,Hong Kong 999077,China

出　　处：《Science China(Information Sciences)》2022年第4期44-59,共16页中国科学（信息科学）（英文版）

基　　金：supported in part by Shenzhen Fundamental Research Program(Grant Nos.JCYJ202001091-10208764,JCYJ20200109110420626);in part by National Natural Science Foundation of China(Grant Nos.U1813204,61802385,62072468);Natural Science Foundation of Guangdong(Grant No.2021A1515012604);in part by Guangdong Basic and Applied Basic Research Foundation(Grant No.2019A1515111106)。

摘　　要：Parkinson’s disease(PD)is treated effectively by deep brain stimulation(DBS)of the subthalamic nucleus(STN),using an electrode inserted into the head of a PD patient.The electrode has multiple electrical contacts along its length,so the best may be chosen for selectively stimulating the STN.Neurosurgeons usually determine the optimal stimulated contact via the clinical experience of the neurosurgeon and the motor improvement of PD patients.This is a time-consuming and labor-intensive trial-and-error process.The selection of optimal stimulated contact highly depends on the locations of sweet spots,which are manually identified by the characteristic features of microelectrode recordings(MERs).This paper presents an amplitude-frequency-aware deep fusion network for optimal contact selection on STN-DBS electrodes.The method first obtains the amplitude-frequency fusion features by combining the MERs time sequence features and the amplitude sequence features,and then uses the convolutional neural network(CNN)with convolutional block attention module(CBAM)to identify both the border of the STN and the sweet spots to implant the electrode.The optimal stimulated contact can be selected according to the distribution of the sweet spots.Experimental results indicate that,for successful surgeries,neurosurgeons and the proposed AI solution selected the same optimal contacts.Furthermore,the proposed method outperforms the state-of-the-art methods for STN and sweet spot identification.The proposed method shows great potential for optimal contact selection to improve the efficiency of STN-DBS surgery and reduce the dependence on clinicians’experience.

关 键 词：optimal contact selection sweet spots microelectrode recordings amplitude-frequency feature deep fusion network 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] TN911.7[自动化与计算机技术—控制科学与工程] R742.5[电子电信—通信与信息系统]