Electronic Thesis and Dissertation Repository

Degree

Master of Science

Program

Electrical and Computer Engineering

Supervisor

Patel, Rajni V.

Abstract

This thesis presents a novel technique for localizing the Subthalamic Nucleus (STN) during Deep Brain Stimulation (DBS) surgery. 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. The STN is a small grey matter structure within the brain, which makes accurate placement a challenging task for the surgical team. Prior to placement of the permanent electrode, intraoperative microelectrode recordings (MERs) of neural activity are used to localize the STN. The placement of the permanent electrode and the success of the stimulation therapy depend on accurate localization. In this study, an objective approach was 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 algorithm for defining the electrophysiological borders of the STN. A classification approach that can detect the borders of the STN during the operation is proposed. MER signals from 100 PD patients were recorded and used to validate the performance of the proposed method. The results show that by extracting wavelet transformation features from MER signals and using a deep neural network architecture, it is possible to detect the border of the STN with an accuracy of 92%. The proposed method can be implemented in real-time during the surgery to assist the surgical team with the goal of enhancing the accuracy and consistency of electrode placement in the STN.

Available for download on Friday, May 01, 2020

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