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Machine Learning Approaches to Predict Repetitive Transcranial Magnetic Stimulation Treatment Response in Major Depressive Disorder

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Proceedings of SAI Intelligent Systems Conference (IntelliSys) 2016 (IntelliSys 2016)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 16))

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Abstract

Repetitive transcranial magnetic stimulation (rTMS) is a non-pharmacological treatment that is associated with significant improvements in clinical symptoms of major depressive disorder (MDD). The efficacy of rTMS treatment can be predicted using pre-treatment cordance, a quantitative electroencephalography (QEEG) method extracting information from absolute and relative power of EEG spectra, that will prevent trial-and error treatment sequences, subject suffering and increase in health-care costs. In this study, pre-treatment QEEG data were collected from 6 frontal electrodes (Fp1, Fp2, F3, F4, F7 and F8) in slow bands (delta and theta) for 147 MDD subjects. In order to classify MDD subjects as responder or non-responder, four different machine learning techniques, which are Artificial Neural Networks (ANN), Support Vector Machines (SVM) and Decision Tree (DT), were used and their performances were evaluated. The results show that it is possible to predict rTMS treatment responders with a sensitivity of 95.6%, accuracy of 86.4% and area under Receiver Operating Characteristics (ROC) curve (AUC) value of 0.92 using SVM.

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Acknowledgement

Authors would like to express their thanks to NPIstanbul Hospital for providing the required EEG data.

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Authors and Affiliations

  1. Department of Computer Engineering, Uskudar University, Istanbul, Turkey

    Turker Tekin Erguzel

  2. Department of Psychiatry, NPIstanbul Hospital, Uskudar University, Istanbul, Turkey

    Nevzat Tarhan

Authors
  1. Turker Tekin Erguzel
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  2. Nevzat Tarhan
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Corresponding author

Correspondence to Turker Tekin Erguzel .

Editor information

Editors and Affiliations

  1. Faculty of Computing and Engineering, School of Computing and Mathematics, University of Ulster at Jordanstown, Newtownabbey, United Kingdom

    Yaxin Bi

  2. The Science and Information (SAI) Organization, Bradford, West Yorkshire, United Kingdom

    Supriya Kapoor

  3. The Science and Information (SAI) Organization, Bradford, West Yorkshire, United Kingdom

    Rahul Bhatia

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Erguzel, T.T., Tarhan, N. (2018). Machine Learning Approaches to Predict Repetitive Transcranial Magnetic Stimulation Treatment Response in Major Depressive Disorder. In: Bi, Y., Kapoor, S., Bhatia, R. (eds) Proceedings of SAI Intelligent Systems Conference (IntelliSys) 2016. IntelliSys 2016. Lecture Notes in Networks and Systems, vol 16. Springer, Cham. https://doi.org/10.1007/978-3-319-56991-8_29

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  • DOI: https://doi.org/10.1007/978-3-319-56991-8_29

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-56990-1

  • Online ISBN: 978-3-319-56991-8

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