Greene Patrick, Li Adam, González-Martínez Jorge, Sarma Sridevi V
Neuromedical Control Systems Lab, Institute for Computational Medicine, Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States.
Neurosurgery, Cleveland Clinic, Cleveland, OH, United States.
Front Neurol. 2021 Jan 6;11:605696. doi: 10.3389/fneur.2020.605696. eCollection 2020.
For epileptic patients requiring resective surgery, a modality called stereo-electroencephalography (SEEG) may be used to monitor the patient's brain signals to help identify epileptogenic regions that generate and propagate seizures. SEEG involves the insertion of multiple depth electrodes into the patient's brain, each with 10 or more recording contacts along its length. However, a significant fraction (≈ 30% or more) of the contacts typically reside in white matter or other areas of the brain which can not be epileptogenic themselves. Thus, an important step in the analysis of SEEG recordings is distinguishing between electrode contacts which reside in gray matter vs. those that do not. MRI images overlaid with CT scans are currently used for this task, but they take significant amounts of time to manually annotate, and even then it may be difficult to determine the status of some contacts. In this paper we present a fast, automated method for classifying contacts in gray vs. white matter based only on the recorded signal and relative contact depth. We observe that bipolar referenced contacts in white matter have less power in all frequencies below 150 Hz than contacts in gray matter, which we use in a Bayesian classifier to attain an average area under the receiver operating characteristic curve of 0.85 ± 0.079 (SD) across 29 patients. Because our method gives a probability for each contact rather than a hard labeling, and uses a feature of the recorded signal that has direct clinical relevance, it can be useful to supplement decision-making on difficult to classify contacts or as a rapid, first-pass filter when choosing subsets of contacts from which to save recordings.
对于需要进行切除性手术的癫痫患者，一种称为立体脑电图（SEEG）的方法可用于监测患者的脑信号，以帮助识别产生和传播癫痫发作的致痫区域。SEEG涉及将多个深度电极插入患者大脑，每个电极沿其长度有10个或更多记录触点。然而，相当一部分（约30%或更多）的触点通常位于白质或大脑的其他区域，这些区域本身不会产生癫痫。因此，分析SEEG记录的一个重要步骤是区分位于灰质和非灰质的电极触点。目前，叠加CT扫描的MRI图像用于此任务，但手动标注需要大量时间，即便如此，确定某些触点的状态可能仍很困难。在本文中，我们提出了一种仅基于记录信号和相对触点深度对白质和灰质中的触点进行分类的快速自动化方法。我们观察到，白质中的双极参考触点在150Hz以下的所有频率中的功率都比灰质中的触点小，我们将此用于贝叶斯分类器，在29名患者中获得的接收器操作特征曲线下的平均面积为0.85±0.079（标准差）。由于我们的方法为每个触点给出概率而非硬标签，并且使用具有直接临床相关性的记录信号特征，因此在对难以分类的触点进行决策时，或在从要保存记录的触点子集中进行选择时作为快速的初次筛选，它可能有助于补充决策。
