Department of Electrical Engineering, Benha Faculty of Engineering, Benha University, Benha, Egypt.
First Affiliated Hospital of Harbin Medical University, 23 Youzheng Str., Nangang District, Harbin 150001, China.
J Neurosci Methods. 2021 May 15;356:109145. doi: 10.1016/j.jneumeth.2021.109145. Epub 2021 Mar 24.
Deep brain stimulation (DBS) surgery has been extensively conducted for treating advanced Parkinson's disease (PD) patient's symptoms. DBS hinges on the localization of the subthalamic nucleus (STN) in which a permanent electrode should be accurately placed to produce electrical current. Microelectrode recording (MER) signals are routinely recorded in the procedure of DBS surgery to validate the planned trajectories. However, manual MER signals interpretation with the goal of detecting STN borders requires expertise and prone to inter-observer variability. Therefore, a computerized aided system would be beneficial to automatic detection of the dorsal and ventral borders of the STN in MER.
In this study, a new deep learning model based on convolutional neural system for automatic delineation of the neurophysiological borders of the STN along the electrode trajectory was developed.
The proposed model does not involve any conventional standardization, feature extraction or selection steps.
Promising results of 98.67% accuracy, 99.03% sensitivity, 98.11% specificity, 98.79% precision and 98.91% F1-score for subject based testing were achieved using the proposed convolutional neural network (CNN) model.
This is the first study on the analysis of MER signals to detect STN using deep CNN. Traditional machine learning (ML) algorithms are often cumbersome and suffer from subjective evaluation. Though, the developed 10-layered CNN model has the capability of extracting substantial features at the convolution stage. Hence, the proposed model has the potential to deliver high performance on STN region detection which shows perspective in aiding the neurosurgeon intraoperatively.
深部脑刺激（DBS）手术已广泛应用于治疗晚期帕金森病（PD）患者的症状。DBS 依赖于丘脑底核（STN）的定位，其中应准确放置永久电极以产生电流。在 DBS 手术过程中，通常会记录微电极记录（MER）信号，以验证计划的轨迹。然而，手动 MER 信号解释以检测 STN 边界需要专业知识，并且容易受到观察者间的变异性影响。因此，计算机辅助系统有助于自动检测 MER 中 STN 的背侧和腹侧边界。
在这项研究中，开发了一种基于卷积神经网络的新深度学习模型，用于自动沿着电极轨迹描绘 STN 的神经生理学边界。
所提出的模型不涉及任何传统的标准化、特征提取或选择步骤。
使用所提出的卷积神经网络（CNN）模型，对基于受试者的测试，达到了 98.67%的准确率、99.03%的敏感性、98.11%的特异性、98.79%的精度和 98.91%的 F1 分数，这是首次使用深度 CNN 分析 MER 信号以检测 STN 的研究。传统的机器学习（ML）算法通常很繁琐，并且受到主观评价的影响。尽管如此，所开发的 10 层 CNN 模型具有在卷积阶段提取大量特征的能力。因此，所提出的模型具有在 STN 区域检测中提供高性能的潜力，这表明它有可能在手术期间为神经外科医生提供帮助。
