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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.

Background: Diagnosis of carpal tunnel syndrome is based on clinical symptoms, examination findings, and electrodiagnostic studies. For carpal tunnel syndrome, the most useful of these are nerve conduction studies. However, nerve conduction studie can result in ambiguous or false-negative results, particularly for mild carpal tunnel syndrome. Increasing the number of nerve conduction studie tests improves accuracy but also increases time, cost, and discomfort. To improve accuracy without additional testing, the terminal latency index and residual latency are additional calculations that can be performed using the minimum number of tests. Recently, the median sensory-ulnar motor latency difference was devised as another way to improve diagnostic accuracy for mild carpal tunnel syndrome., Aims: The median sensory-ulnar motor latency difference, terminal latency index, and residual latency were compared for diagnostic accuracy according to severity of carpal tunnel syndrome., Study Design: Diagnostic accuracy study., Methods: A total of 657 subjects were retrospectively enrolled. The carpal tunnel syndrome group consisted of 546 subjects with carpal tunnel syndrome according to nerve conduction studie (all severities). The control group consisted of 121 subjects with no hand symptoms and normal nerve conduction studie. All statistical analyses were performed using SAS v9.4. Means were compared using one-way ANOVA with the Bonferroni adjustment. Sensitivity, specificity, positive predictive value, and negative predictive value were compared, including receiver operating characteristic curve analysis., Results: For mild carpal tunnel syndrome, the median sensory-ulnar motor latency difference showed higher specificity and positive predictive value rates (0.967 and 0.957, respectively) than terminal latency index (0.603 and 0.769, respectively) and residual latency (0.818 and 0.858, respectively). The area under the receiver operating characteristic was highest for the median sensory-ulnar motor latency difference (0.889), followed by the residual latency (0.829), and lastly the terminal latency index (0.762). Differences were statistically significant (median sensory-ulnar motor latency difference being the most accurate). For moderate carpal tunnel syndrome, sensitivity and specificity rates of residual latency (0.989 and 1.000) and terminal latency index (0.983 and 0.975) were higher than those for median sensory-ulnar motor latency difference (0.866 and 0.958). Differences in area under the receiver operating characteristic curve were not significantly significant, but median sensory-ulnar motor latency difference sensitivity was lower. For severe carpal tunnel syndrome, residual latency yielded 1.000 sensitivity, specificity, positive predictive value, negative predictive value and area beneath the receiver operating characteristic curve. Differences in area under the receiver operating characteristic curve were not significantly different., Conclusion: The median sensory-ulnar motor latency difference is the best calculated parameter for diagnosing mild carpal tunnel syndrome. It requires only a simple calculation and no additional testing. Residual latency and the terminal latency index are also useful in diagnosing mild to moderate carpal tunnel syndrome.