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A deep transfer learning approach for improved post-traumatic stress disorder diagnosis

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Abstract

Post-traumatic stress disorder (PTSD) is a traumatic-stressor-related disorder developed by exposure to a traumatic or adverse environmental event that caused serious harm or injury. Structured interview is the only widely accepted clinical practice for PTSD diagnosis but suffers from several limitations including the stigma associated with the disease. Diagnosis of PTSD patients by analyzing speech signals has been investigated as an alternative since recent years, where speech signals are processed to extract frequency features and these features are then fed into a classification model for PTSD diagnosis. In this paper, we developed a deep belief network (DBN) model combined with a transfer learning (TL) strategy for PTSD diagnosis. We computed three categories of speech features and utilized the DBN model to fuse these features. The TL strategy was utilized to transfer knowledge learned from a large speech recognition database, TIMIT, for PTSD detection where PTSD patient data are difficult to collect. We evaluated the proposed methods on two PTSD speech databases, each of which consists of audio recordings from 26 patients. We compared the proposed methods with other popular methods and showed that the state-of-the-art support vector machine (SVM) classifier only achieved an accuracy of 57.68%, and TL strategy boosted the performance of the DBN from 61.53 to 74.99%. Altogether, our method provides a pragmatic and promising tool for PTSD diagnosis. Preliminary results of this study were presented in Banerjee (in: 2017 IEEE international conference on data mining (ICDM), IEEE, 2017).

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Acknowledgements

This research is partially supported by DOD under grant W81XWH-15-C-0099. The authors would like to thank UHCMC for providing the Ohio dataset. The support of NVIDIA Corporation for the donation of the TESLA K40 GPU used in this research is gratefully acknowledged.

Author information

Authors and Affiliations

  1. Department of ECE, Old Dominion University, Norfolk, VA, 23508, USA

    Debrup Banerjee, Kazi Islam, Keyi Xue & Jiang Li

  2. Signal Processing and Control, Intelligent Automation, Inc., Rockville, MD, 20855, USA

    Gang Mei, Lemin Xiao, Guangfan Zhang & Roger Xu

  3. School of EECS, Washington State University, Pullman, WA, 99164, USA

    Cai Lei

  4. Panther Creek High School, 6770 McCrimmon Pkwy, Cary, NC, USA

    Keyi Xue

  5. Computer Science and Engineering Department, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, Andhra Pradesh, 522502, India

    Debrup Banerjee

  6. Department of CSE, Texas A&M University College Station, Texas, TX, 77843, USA

    Shuiwang Ji

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  1. Debrup Banerjee
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Appendices

Appendices

1.1 Youtube dataset

Following are Youtube links to the 26 subjects in “Youtube Data” utilized in our study (the last 8 links were not accessible as of November 17, 2018):

  1. (1)

    http://www.youtube.com/user/jay10221979

  2. (2)

    http://video.pbs.org/video/1506939466/

  3. (3)

    http://www.youtube.com/user/veteransPTSD#p/a/76FE97306FBC00C3/1/vPPiFrwCrSI

  4. (4)

    http://www.youtube.com/watch?v=bsFg8wZuI-4

  5. (5)

    http://www.youtube.com/watch?v=1BoKtZ_z-sg&feature=fvw

  6. (6)

    http://www.youtube.com/watch?v=u-W6_0gCVmE

  7. (7)

    https://www.youtube.com/watch?v=US9J2o16bJE

  8. (8)

    http://www.youtube.com/watch?v=SwiONICQe1w

  9. (9)

    https://www.youtube.com/watch?v=KrMY1eqEVPs

  10. (10)

    https://www.youtube.com/watch?v=TKsD-a3XKlY

  11. (11)

    http://www.youtube.com/user/SpecialOpsM4#p/u/17/hlQlUgGy_gs

  12. (12)

    http://www.youtube.com/user/millerusaf#p/search/6/27dEAmlyDVw

  13. (13)

    http://www.youtube.com/user/xXjOmAmMaXx#p/u/10/CxwbQq8B0xw

  14. (14)

    http://www.youtube.com/user/deathcoreairsoft#p/search/11/jCZpcvRZvQk

  15. (15)

    http://www.youtube.com/user/spartan765#p/search/2/82YT5kHmwo8

  16. (16)

    http://www.youtube.com/user/VulcanMarine#p/search/0/WIrXu4q4hCU

  17. (17)

    http://www.youtube.com/user/DuskMarksmen#p/u/22/WEQXBAcMT8U

  18. (18)

    http://www.youtube.com/watch?v=MBN22B3O_J8

  19. (19)

    http://www.kewego.com/video/iLyROoafYsJp.html

  20. (20)

    http://www.youtube.com/watch?v=WP8HEqXbQdo

  21. (21)

    http://video.google.com/videoplay?docid=6545321893646982640#

  22. (22)

    http://video.google.com/videoplay?docid=6545321893646982640#docid365676523829472362

  23. (23)

    http://www.youtube.com/user/TheAirsoftReviewer1#p/search/0/aWxNfIZQj84

  24. (24)

    http://www.youtube.com/user/ResidentEman#p/u/6/w8xos9pCcik

  25. (25)

    http://www.youtube.com/user/SteveBanke#p/a/u/0/Gr1w8XTL8sO

  26. (26)

    http://www.youtube.com/user/vanguardwolf1#p/u/6/AVRzzRezD_E

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Banerjee, D., Islam, K., Xue, K. et al. A deep transfer learning approach for improved post-traumatic stress disorder diagnosis. Knowl Inf Syst 60, 1693–1724 (2019). https://doi.org/10.1007/s10115-019-01337-2

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