Rapid Visualization Tool for Intraoperative Dorsal Column Mapping Triggered by Spinal Cord Stimulation in Chronic Pain Patients.
Rapid Visualization Tool for Intraoperative Dorsal Column Mapping Triggered by Spinal Cord Stimulation in Chronic Pain Patients.
复制标题
用于慢性疼痛患者脊髓刺激触发的术中背柱测绘的快速可视化工具。
DOI：
10.1109/embc46164.2021.9630459
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Pilitsis,JulieG
中科院分区：
文献类型：
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作者：
Telkes,Ilknur;Behal,Aditya;Hadanny,Amir;Olmsted,ZacharyT;Chitnis,Girish;McLaughlin,Bryan;Pilitsis,JulieG
脊髓电刺激（SCS）是一种广泛接受的有效治疗慢性疼痛的方法。SCS结局高度依赖于SCS电极在适当脊柱节段的准确放置，以达到预期的疼痛缓解。全身麻醉下的术中神经生理监测（IONM）提供了一个客观的实时映射的背柱，并已被证明是一个安全和有效的工具。IONM以各种强度对多个电极触点施加刺激，并同时监测多个肌肉群中的触发肌电图（EMG）反应。因此，它需要神经外科医生和神经生理学家之间的动态通信以及对反应的连续实时注释，这使得手术复杂且基于经验。在这里，我们描述了一个自动化的数据可视化工具，使用术中收集的信号生成患者特定的活动地图。使用高分辨率（HR）-SCS导联收集反应，该导联具有跨越背柱的8列电极。我们的基于JavaScript/Python的图形用户界面（GUI）通过去噪、特征提取、归一化以及将活动图叠加在选定的颜色图中的身体图像上来提供EMG活动的快速和鲁棒的可视化。与查看一系列EMG信号相比，我们的用户友好型工具可快速、可靠地分析刺激对各种肌肉群的影响，并直接比较受试者和/或刺激设置。未来的工作包括扩展分析能力和手术室实施，作为一种实时处理工具，可与当前的IONM技术结合使用。
Spinal cord stimulation (SCS) is a widely accepted effective treatment for managing chronic pain. SCS outcomes depend highly on accurate placement of SCS electrodes at the appropriate spine level for a desired pain relief. Intraoperative neurophysiological monitoring (IONM) under general anesthesia provides an objective real-time mapping of the dorsal columns, and has been shown to be a safe and effective tool. IONM applies stimulation to multiple electrode contacts at various intensities and monitors the triggered electromyography (EMG) responses in several muscle groups simultaneously. Therefore, it requires dynamic communication between neurosurgeon and neurophysiologist and continuous real-time annotations of the responses, which makes the procedure complex and experience-based. Here, we describe an automated data visualization tool that generates patient specific activity maps using intraoperatively collected signals. Responses were collected using a High-resolution (HR)-SCS lead with 8 columns of electrodes spanning the dorsal columns. Our JavaScript/Python based graphical user interface (GUI) provides a fast and robust visualization of EMG activity via denoising, feature extraction, normalization, and overlaying of the activity maps on body images in selected colormaps. In contrast to reviewing series of EMG signals, our user-friendly tool provides a rapid and robust analysis of stimulation effects on various muscle groups and direct comparison across subjects and/or stimulation settings. Future work includes expanding analytics capabilities and operating room implementation as a real-time processing tool that can be used in conjunction with the current IONM techniques.