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.
Highlights
Parkinson’s disease (PD) is a progressive neurological disease that affects 1% of people over 60 years of age [1], [2]
In this paper, we propose to address the challenge through: (a) collection of a rich and unique dataset to be used for evaluating the possibility of reaching high accuracy intraoperatively; (b) using features that can be calculated intraoperatively with no need of critical postoperative normalization; and (c) relying on the power of the collected dataset and using a state-of-the-art strong machine learning algorithm to model the nonlinear neurophysiology in order to model the borders of the subthalamic nucleus (STN)
The dataset used in this study consisted of microelectrode recordings (MERs) signals from 100 PD patients obtained during Deep Brain Stimulation (DBS) surgery
Summary
Parkinson’s disease (PD) is a progressive neurological disease that affects 1% of people over 60 years of age [1], [2]. Deep Brain Stimulation (DBS) surgery is an effective therapy used for neuropsychiatric disorders especially in those that have advanced PD [3], [4]. During DBS surgery, a permanent electrode is implanted inside the brain to deliver high-frequency electrical pulses to the subthalamic nucleus (STN) [5]. The outcome of DBS surgery is highly dependent on the accurate placement of the electrode inside the STN. Since the STN is a very small (4-7mm) and deep anatomical region, appropriate and accurate implantation of the electrode is a difficult, challenging and time-consuming task that requires a high level of proficiency and expertise.
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