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Recurrent Neural Networks for Diagnosis of Carpal Tunnel Syndrome Using Electrophysiologic Findings

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Abstract

This paper presents the use of recurrent neural networks (RNNs) for diagnosis of carpal tunnel syndrome (CTS) (normal, right CTS, left CTS, bilateral CTS). The RNN is trained with the Levenberg-Marquardt algorithm. The RNN is trained on the features of CTS (right median motor latency, left median motor latency, right median sensory latency, left median sensory latency). The multilayer perceptron neural network (MLPNN) is also implemented for comparison the performance of the classifiers on the same diagnosis problem. The total classification accuracy of the RNN is significantly high (94.80%). The obtained results confirmed the validity of the RNNs to help in clinical decision-making.

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Authors and Affiliations

  1. Departmant of Neurosurgery, Faculty of Medicine, Kocaeli University, Umut Tepe Campus, 31380, Kocaeli, Turkey

    Konuralp Ilbay

  2. Department of Electrical and Electronics Engineering, Faculty of Engineering, TOBB Ekonomi ve Teknoloji Üniversitesi, 06530, Söğütözü, Ankara, Turkey

    Elif Derya Übeyli

  3. Departmant of Physiology, Faculty of Medicine, Kocaeli University, Umut Tepe Campus, 31380, Kocaeli, Turkey

    Gul Ilbay

  4. Departmant of Neurology, Faculty of Medicine, Kocaeli University, Umut Tepe Campus, 31380, Kocaeli, Turkey

    Faik Budak

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  1. Konuralp Ilbay
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  2. Elif Derya Übeyli
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  3. Gul Ilbay
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Correspondence to Elif Derya Übeyli.

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Ilbay, K., Übeyli, E.D., Ilbay, G. et al. Recurrent Neural Networks for Diagnosis of Carpal Tunnel Syndrome Using Electrophysiologic Findings. J Med Syst 34, 643–650 (2010). https://doi.org/10.1007/s10916-009-9277-6

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  • DOI: https://doi.org/10.1007/s10916-009-9277-6

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