Khosravi Mahsa, Atashzar S Farokh, Gilmore Greydon, Jog Mandar S, Patel Rajni V
1Department of Electrical and Computer EngineeringUniversity of Western OntarioLondonONN6A 3K7Canada.
2Canadian Surgical Technologies and Advanced Robotics (CSTAR)Lawson Health Research InstituteLondonONN6A 4V2Canada.
IEEE J Transl Eng Health Med. 2020 Jan 30;8:2500309. doi: 10.1109/JTEHM.2020.2969152. eCollection 2020.
A new approach is presented for localizing the Subthalamic Nucleus (STN) during Deep Brain Stimulation (DBS) surgery based on microelectrode recordings (MERs). DBS is an accepted treatment for individuals living with Parkinson's Disease (PD). This surgery involves implantation of a permanent electrode inside the STN to deliver electrical current. Since the STN is a very small region inside the brain, accurate placement of an electrode is a challenging task for the surgical team. Prior to placement of the permanent electrode, microelectrode recordings of brain activity are used intraoperatively to localize the STN. The placement of the electrode and the success of the therapy depend on this location. In this paper, an objective approach is implemented to help the surgical team in localizing the STN. This is achieved by processing the MER signals and extracting features during the surgery to be used in a Machine Learning (ML) algorithm for defining the neurophysiological borders of the STN. For this purpose, a new classification approach is proposed with the goal of detecting both the dorsal and the ventral borders of the STN during the surgical procedure. Results collected from 100 PD patients in this study, show that by calculating and extracting wavelet transformation features from MER signals and using a data-driven computational deep neural network model, it is possible to detect the borders of the STN with an accuracy of 92%. The proposed method can be implemented in real-time during the surgery to model the neurophysiological nonlinearity along the path of the electrode trajectory during insertion.
本文提出了一种基于微电极记录（MERs）在脑深部电刺激（DBS）手术中定位丘脑底核（STN）的新方法。DBS是帕金森病（PD）患者公认的一种治疗方法。该手术需要在STN内植入永久电极以输送电流。由于STN是脑内一个非常小的区域，电极的精确放置对手术团队来说是一项具有挑战性的任务。在放置永久电极之前，术中使用脑活动的微电极记录来定位STN。电极的放置和治疗的成功取决于这个位置。在本文中，实施了一种客观方法来帮助手术团队定位STN。这是通过在手术过程中处理MER信号并提取特征来实现的，这些特征将用于机器学习（ML）算法中以定义STN的神经生理边界。为此，提出了一种新的分类方法，目标是在手术过程中检测STN的背侧和腹侧边界。本研究从100名PD患者收集的结果表明，通过从MER信号中计算和提取小波变换特征，并使用数据驱动的计算深度神经网络模型，可以以92%的准确率检测STN的边界。所提出的方法可以在手术过程中实时实施，以模拟电极轨迹插入过程中沿路径的神经生理非线性。
