Annu Int Conf IEEE Eng Med Biol Soc. 2021 Nov;2021:5760-5763. doi: 10.1109/EMBC46164.2021.9630459.
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.
脊髓刺激 (SCS) 是一种广泛接受的有效治疗慢性疼痛的方法。SCS 的效果高度依赖于将 SCS 电极准确放置在适当的脊柱水平以获得所需的疼痛缓解。全身麻醉下的术中神经生理监测 (IONM) 提供了对背柱的客观实时映射,已被证明是一种安全有效的工具。IONM 以不同的强度刺激多个电极触点,并同时监测多个肌肉群的触发肌电图 (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沟通以及对反应的持续实时注释,这使得该过程复杂且基于经验。在这里,我们描述了一种自动数据可视化工具,该工具使用术中收集的信号生成特定于患者的活动图。使用具有 8 列电极的高分辨率 (HR)-SCS 导联收集反应,这些电极跨越背柱。我们基于 JavaScript/Python 的图形用户界面 (GUI) 通过去噪、特征提取、归一化以及在选定的颜色图上叠加活动图来提供 EMG 活动的快速而稳健的可视化。与查看一系列 EMG 信号相比,我们用户友好的工具提供了对各种肌肉群刺激效果的快速而稳健的分析,并可以直接比较不同的个体和/或刺激设置。未来的工作包括扩展分析功能并在手术室中实现,作为一种实时处理工具,可以与当前的 IONM 技术结合使用。