Department of Mechanical Engineering, Faculty of Engineering, Istanbul University, Avcilar, 34320 Istanbul, Turkey.
Comput Math Methods Med. 2012;2012:803980. doi: 10.1155/2012/803980. Epub 2012 Mar 5.
In our previous study, we have demonstrated that analyzing the skin impedances measured along the key points of the dermatomes might be a useful supplementary technique to enhance the diagnosis of spinal cord injury (SCI), especially for unconscious and noncooperative patients. Initially, in order to distinguish between the skin impedances of control group and patients, artificial neural networks (ANNs) were used as the main data classification approach. However, in the present study, we have proposed two more data classification approaches, that is, support vector machine (SVM) and hierarchical cluster tree analysis (HCTA), which improved the classification rate and also the overall performance. A comparison of the performance of these three methods in classifying traumatic SCI patients and controls was presented. The classification results indicated that dendrogram analysis based on HCTA algorithm and SVM achieved higher recognition accuracies compared to ANN. HCTA and SVM algorithms improved the classification rate and also the overall performance of SCI diagnosis.
在我们之前的研究中，已经证明分析沿着皮节关键点测量的皮肤阻抗可能是增强脊髓损伤（SCI）诊断的有用辅助技术，特别是对于无意识和不合作的患者。最初，为了区分对照组和患者的皮肤阻抗，使用人工神经网络（ANNs）作为主要的数据分类方法。然而，在本研究中，我们提出了另外两种数据分类方法，即支持向量机（SVM）和层次聚类树分析（HCTA），这两种方法提高了分类率和整体性能。对这三种方法在分类创伤性 SCI 患者和对照组中的性能进行了比较。分类结果表明，基于 HCTA 算法和 SVM 的聚类树分析得到了比 ANN 更高的识别准确率。HCTA 和 SVM 算法提高了 SCI 诊断的分类率和整体性能。
