Karamehmetoglu Safak Sahir, Ugur Mukden, Arslan Yunus Ziya, Palamar Deniz
Physical Medicine and Rehabilitation Department, Cerrahpasa Medical Faculty, Istanbul University, Cerrahpasa, Istanbul, 34098, Turkey.
Eur Spine J. 2009 Jul;18(7):972-7. doi: 10.1007/s00586-009-0896-x. Epub 2009 Mar 20.
The purpose of this study was to develop a quantitative skin impedance test that could be used to diagnose spinal cord injury (SCI) if any, especially in unconscious and/or non-cooperative SCI patients. To achieve this goal, initially skin impedance of the sensory key points of the dermatomes (between C3 and S1 bilaterally) was measured in 15 traumatic SCI patients (13 paraplegics and 2 tetraplegics) and 15 control subjects. In order to classify impedance values and to observe whether there would be a significant difference between patient and subject impedances, an artificial neural network (ANN) with back-propagation algorithm was employed. Validation results of the ANN showed promising performance. It could classify traumatic SCI patients with a success rate of 73%. By assessing the experimental protocols and the validation results, the proposed method seemed to be a simple, objective, quantitative, non-invasive and non-expensive way of assessing SCI in such patients.
本研究的目的是开发一种定量皮肤阻抗测试方法，用于诊断脊髓损伤（SCI）（如有），特别是针对昏迷和/或不合作的SCI患者。为实现这一目标，最初对15例创伤性SCI患者（13例截瘫患者和2例四肢瘫患者）和15名对照受试者双侧皮节（C3至S1之间）的感觉关键点的皮肤阻抗进行了测量。为了对阻抗值进行分类并观察患者与受试者阻抗之间是否存在显著差异，采用了具有反向传播算法的人工神经网络（ANN）。ANN的验证结果显示出良好的性能。它能够以73%的成功率对创伤性SCI患者进行分类。通过评估实验方案和验证结果，所提出的方法似乎是一种简单、客观、定量、非侵入性且成本低廉的评估此类患者SCI的方法。
