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A novel approach to the diagnostic assessment of carpal tunnel syndrome based on the frequency domain of the compound muscle action potential
Biomedical Engineering / Biomedizinische Technik ( IF 1.7 ) Pub Date : 2019-08-04 , DOI: 10.1515/bmt-2018-0077
Veysel Alcan 1 , Hilal Kaya 2 , Murat Zinnuroğlu 3 , Gülçin Kaymak Karataş 3 , Mehmet Rahmi Canal 4
Affiliation  

Conventional electrophysiological (EP) tests may yield ambiguous or false-negative results in some patients with signs and symptoms of carpal tunnel syndrome (CTS). Therefore, researchers tend to investigate new parameters to improve the sensitivity and specificity of EP tests. We aimed to investigate the mean and maximum power of the compound muscle action potential (CMAP) as a novel diagnostic parameter, by evaluating diagnosis and classification performance using the supervised Kohonen self-organizing map (SOM) network models. The CMAPs were analyzed using the fast Fourier transform (FFT). The mean and maximum power parameters were calculated from the power spectrum. A counter-propagation artificial neural network (CPANN), supervised Kohonen network (SKN) and XY-fused network (XYF) were compared to evaluate the classification and diagnostic performance of the parameters using the confusion matrix. The mean and maximum power of the CMAP were significantly lower in patients with CTS than in the normal group (p < 0.05), and the XYF network had the best total performance of classification with 91.4%. This study suggests that the mean and maximum power of the CMAP can be considered as less time-consuming parameters for the diagnosis of CTS without using additional EP tests which can be uncomfortable for the patient due to poor tolerance to electrical stimulation.

中文翻译:

基于复合肌肉动作电位频域的腕管综合征诊断评估新方法

在一些有腕管综合征 (CTS) 体征和症状的患者中,传统的电生理 (EP) 测试可能会产生模棱两可或假阴性的结果。因此,研究人员倾向于研究新的参数以提高 EP 测试的灵敏度和特异性。我们旨在通过使用有监督的 Kohonen 自组织图 (SOM) 网络模型评估诊断和分类性能,研究复合肌肉动作电位 (CMAP) 作为一种新的诊断参数的平均和最大功率。使用快速傅里叶变换 (FFT) 分析 CMAP。从功率谱计算平均和最大功率参数。反向传播人工神经网络(CPANN),比较有监督的 Kohonen 网络 (SKN) 和 XY 融合网络 (XYF),以使用混淆矩阵评估参数的分类和诊断性能。CTS 患者 CMAP 的平均和最大功效显着低于正常组(p < 0.05),其中 XYF 网络的总分类性能最好,为 91.4%。这项研究表明,CMAP 的平均和最大功率可以被认为是诊断 CTS 的耗时参数,而无需使用额外的 EP 测试,由于对电刺激的耐受性差,患者可能会感到不舒服。XYF 网络的分类总性能最好,为 91.4%。这项研究表明,CMAP 的平均和最大功率可以被认为是诊断 CTS 的耗时参数,而无需使用额外的 EP 测试,由于对电刺激的耐受性差,患者可能会感到不舒服。XYF 网络的分类总性能最好,为 91.4%。这项研究表明,CMAP 的平均和最大功率可以被认为是诊断 CTS 的耗时参数,而无需使用额外的 EP 测试,由于对电刺激的耐受性差,患者可能会感到不舒服。
更新日期:2019-08-04
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