使用深度学习分析微电极记录对帕金森病患者丘脑底核深部脑刺激的临床结果预测。
Clinical outcome prediction from analysis of microelectrode recordings using deep learning in subthalamic deep brain stimulation for Parkinson`s disease.
机构信息
Department of Neurosurgery, Seoul National University Hospital, Seoul, Korea.
Interdisciplinary Program in Bioengineering, Graduate School, Seoul National University, Seoul, Korea.
出版信息
PLoS One. 2021 Jan 26;16(1):e0244133. doi: 10.1371/journal.pone.0244133. eCollection 2021.
BACKGROUND
Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is an effective treatment for improving the motor symptoms of advanced Parkinson's disease (PD). Accurate positioning of the stimulation electrodes is necessary for better clinical outcomes.
OBJECTIVE
We applied deep learning techniques to microelectrode recording (MER) signals to better predict motor function improvement, represented by the UPDRS part III scores, after bilateral STN DBS in patients with advanced PD. If we find the optimal stimulation point with MER by deep learning, we can improve the clinical outcome of STN DBS even under restrictions such as general anesthesia or non-cooperation of the patients.
METHODS
In total, 696 4-second left-side MER segments from 34 patients with advanced PD who underwent bilateral STN DBS surgery under general anesthesia were included. We transformed the original signal into three wavelets of 1-50 Hz, 50-500 Hz, and 500-5,000 Hz. The wavelet-transformed MER was used for input data of the deep learning. The patients were divided into two groups, good response and moderate response groups, according to DBS on to off ratio of UPDRS part III score for the off-medication state, 6 months postoperatively. The ratio were used for output data in deep learning. The Visual Geometry Group (VGG)-16 model with a multitask learning algorithm was used to estimate the bilateral effect of DBS. Different ratios of the loss function in the task-specific layer were applied considering that DBS affects both sides differently.
RESULTS
When we divided the MER signals according to the frequency, the maximal accuracy was higher in the 50-500 Hz group than in the 1-50 Hz and 500-5,000 Hz groups. In addition, when the multitask learning method was applied, the stability of the model was improved in comparison with single task learning. The maximal accuracy (80.21%) occurred when the right-to-left loss ratio was 5:1 or 6:1. The area under the curve (AUC) was 0.88 in the receiver operating characteristic (ROC) curve.
CONCLUSION
Clinical improvements in PD patients who underwent bilateral STN DBS could be predicted based on a multitask deep learning-based MER analysis.
背景
深部脑刺激（DBS）丘脑底核（STN）是一种有效改善晚期帕金森病（PD）运动症状的治疗方法。为了获得更好的临床效果，需要对刺激电极进行准确定位。
目的
我们应用深度学习技术对微电极记录（MER）信号进行分析，以更好地预测双侧 STN-DBS 后晚期 PD 患者运动功能的改善，以 UPDRS 第三部分评分表示。如果我们能通过深度学习找到 MER 的最佳刺激点，即使在全麻或患者不配合等限制下，也可以改善 STN-DBS 的临床效果。
方法
共纳入 34 例在全麻下行双侧 STN-DBS 手术的晚期 PD 患者 696 个 4 秒左侧 MER 段。我们将原始信号转换为 1-50 Hz、50-500 Hz 和 500-5000 Hz 的三个子波。MER 经子波变换后作为深度学习的输入数据。根据术后 6 个月停药状态下 UPDRS 第三部分评分的 DBS 开/关比值，将患者分为良好反应组和中度反应组。在深度学习中，将比值作为输出数据。采用多任务学习算法的 Visual Geometry Group（VGG）-16 模型来估计 DBS 的双侧效应。考虑到 DBS 对两侧的影响不同，在任务特定层中应用了不同的损失函数比值。
结果
当我们根据频率对 MER 信号进行分组时，50-500 Hz 组的准确率最高。此外，与单任务学习相比，应用多任务学习方法可提高模型的稳定性。当右到左的损失比为 5:1 或 6:1 时，准确率最高（80.21%）。在接收器工作特征（ROC）曲线中，曲线下面积（AUC）为 0.88。
结论
基于多任务深度学习 MER 分析，可预测接受双侧 STN-DBS 的 PD 患者的临床改善情况。
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