Intraoperative Localization of STN During DBS Surgery Using a Data-Driven Model

Mahsa Khosravi; S Farokh Atashzar; Greydon Gilmore; Mandar S Jog; Rajni V Patel

doi:10.1109/JTEHM.2020.2969152

Intraoperative Localization of STN During DBS Surgery Using a Data-Driven Model

IEEE J Transl Eng Health Med. 2020 Jan 30:8:2500309. doi: 10.1109/JTEHM.2020.2969152. eCollection 2020.

Authors

Mahsa Khosravi^{1

2}, S Farokh Atashzar^{3

4

5}, Greydon Gilmore⁶, Mandar S Jog^{1

7

8}, Rajni V Patel^{1

2

7}

Affiliations

¹ 1Department of Electrical and Computer EngineeringUniversity of Western OntarioLondonONN6A 3K7Canada.
² 2Canadian Surgical Technologies and Advanced Robotics (CSTAR)Lawson Health Research InstituteLondonONN6A 4V2Canada.
³ 3Department of Electrical and Computer EngineeringTandon School of EngineeringNew York UniversityNew YorkNY10003USA.
⁴ 4Department of Mechanical and Aerospace EngineeringTandon School of EngineeringNew York UniversityNew YorkNY10003USA.
⁵ 5NYU WIRELESS, Tandon School of EngineeringNew York UniversityNew YorkNY10003USA.
⁶ 6School of Biomedical EngineeringUniversity of Western OntarioLondonONN6A 3K7Canada.
⁷ 7Department of Clinical NeurosciencesUniversity of Western OntarioLondonONN6A 3K7Canada.
⁸ 8London Health Sciences CentreLondonONN6A 5W9Canada.

Abstract

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.

Keywords: Deep brain stimulation; Parkinson’s disease; deep neural network; intraoperative localization of STN.

Grants and funding

This work was supported in part by the Natural Sciences and Engineering Research Council (NSERC) of Canada under the NSERC Discovery Grant RGPIN 1345, in part by the AGEWELL Network of Centers of Excellence under Grant AW CRP 2015-WP5.3, and in part by the Canada Research Chairs Program.