Surgical Monitor for Detection of Spinal Cord Injury

描述（由申请人提供）：脊柱手术期间的体感诱发电位（SSEP）监测与截瘫发生率降低60%有关。然而，对信号的主观解释并不总是准确的，并且已经证明了一个重要的学习曲线。因此，我们提出了创新的定量信号处理算法的开发和测试，重点是损伤的检测。在第一阶段的工作中，我们将使用ssep的自适应傅立叶分析来评估信号中的参数变化，并将结果与损伤联系起来。我们将进一步开发一种光谱相干分析方法，得出我们称之为线性指数的损伤指数。然后将使用已建立的大鼠脊髓分级损伤模型验证损伤检测算法。电生理测量将由神经电生理学专家进行。然后，我们将严格检查SSEP监测过程中的假阳性和假阴性问题，并研究在不同脊髓压缩比下脊髓损伤的及时检测。如果我们成功了，我们设想第二阶段的重点是设备的临床验证，改进的噪声免疫，以及对人类受试者特定报警阈值标准的调查。我们的长期目标是将该设备作为一种快速、准确的哨兵，在高风险手术中检测脊髓损伤。

DESCRIPTION (provided by applicant): Somatosensory evoked potential (SSEP) monitoring during spinal surgery has been associated with a 60% reduction in paraplegia. However, subjective interpretation of signals is not always accurate, and a significant learning curve has been demonstrated. Therefore, we propose the development and testing of innovative quantitative signal processing algorithms, focused on the detection of injury. In our Phase I effort, we will use adaptive Fourier analysis of SSEPs to evaluate parametric changes in the signal, and correlate the results with injury. We will further develop a spectral coherence analysis approach that results in an index of injury we call the Linearity Index. The injury detection algorithm will then be validated using an established rat model of graded spinal cord injury. Electrophysiological measurements will be performed by an expert neuroelectrophysiologist. We will then critically examine the problems of false-positives and false-negatives during SSEP monitoring, as well as study timely detection of spinal cord injury at various spinal compression ratios. If we are successful, we envision a Phase II focused on clinical validation of the device, improved noise-immunity, and investigation of human-subject specific alarm threshold criteria. It is our long-term goal to see the device used as a rapid, accurate sentry for detection of spinal cord compromise during high-risk surgeries.