Abstract
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.
Acknowledgments
The authors thank the members of the Department of Physical Medicine and Rehabilitation, School of Medicine, Gazi University for collecting data. Moreover, we thank Ankara Yıldırım Beyazıt University for supporting this study with the project number BAP 4043.
Author Statement
Research funding: Authors state no funding involved.
Conflict of interest: Authors state no conflict of interest.
Informed consent: Informed consent is not applicable.
Ethical approval: The conducted research is not related to either human or animal use.
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