Journal of Medical Systems

, Volume 34, Issue 4, pp 643–650 | Cite as

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

  • Konuralp Ilbay
  • Elif Derya Übeyli
  • Gul Ilbay
  • Faik Budak
Original Paper


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.


Carpal tunnel syndrome Median motor latency Median sensory latency Clasification accuracy Recurrent neural network 


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Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Konuralp Ilbay
    • 1
  • Elif Derya Übeyli
    • 2
  • Gul Ilbay
    • 3
  • Faik Budak
    • 4
  1. 1.Departmant of Neurosurgery, Faculty of MedicineKocaeli UniversityKocaeliTurkey
  2. 2.Department of Electrical and Electronics Engineering, Faculty of EngineeringTOBB Ekonomi ve Teknoloji ÜniversitesiAnkaraTurkey
  3. 3.Departmant of Physiology, Faculty of MedicineKocaeli UniversityKocaeliTurkey
  4. 4.Departmant of Neurology, Faculty of MedicineKocaeli UniversityKocaeliTurkey

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