Literature DB >> 28450190

Differentiation chronic post traumatic stress disorder patients from healthy subjects using objective and subjective sleep-related parameters.

Masoud Tahmasian1, Hamidreza Jamalabadi2, Mina Abedini3, Mohammad R Ghadami3, Amir A Sepehry4, David C Knight5, Habibolah Khazaie6.   

Abstract

Sleep disturbance is common in chronic post-traumatic stress disorder (PTSD). However, prior work has demonstrated that there are inconsistencies between subjective and objective assessments of sleep disturbance in PTSD. Therefore, we investigated whether subjective or objective sleep assessment has greater clinical utility to differentiate PTSD patients from healthy subjects. Further, we evaluated whether the combination of subjective and objective methods improves the accuracy of classification into patient versus healthy groups, which has important diagnostic implications. We recruited 32 chronic war-induced PTSD patients and 32 age- and gender-matched healthy subjects to participate in this study. Subjective (i.e. from three self-reported sleep questionnaires) and objective sleep-related data (i.e. from actigraphy scores) were collected from each participant. Subjective, objective, and combined (subjective and objective) sleep data were then analyzed using support vector machine classification. The classification accuracy, sensitivity, and specificity for subjective variables were 89.2%, 89.3%, and 89%, respectively. The classification accuracy, sensitivity, and specificity for objective variables were 65%, 62.3%, and 67.8%, respectively. The classification accuracy, sensitivity, and specificity for the aggregate variables (combination of subjective and objective variables) were 91.6%, 93.0%, and 90.3%, respectively. Our findings indicate that classification accuracy using subjective measurements is superior to objective measurements and the combination of both assessments appears to improve the classification accuracy for differentiating PTSD patients from healthy individuals.
Copyright © 2017 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Actigraphy; Classification; Post traumatic stress disorder; Support vector machine

Mesh:

Year:  2017        PMID: 28450190     DOI: 10.1016/j.neulet.2017.04.042

Source DB:  PubMed          Journal:  Neurosci Lett        ISSN: 0304-3940            Impact factor:   3.046


  2 in total

Review 1.  ENIGMA-Sleep: Challenges, opportunities, and the road map.

Authors:  Masoud Tahmasian; André Aleman; Ole A Andreassen; Zahra Arab; Marion Baillet; Francesco Benedetti; Tom Bresser; Joanna Bright; Michael W L Chee; Daphne Chylinski; Wei Cheng; Michele Deantoni; Martin Dresler; Simon B Eickhoff; Claudia R Eickhoff; Torbjørn Elvsåshagen; Jianfeng Feng; Jessica C Foster-Dingley; Habib Ganjgahi; Hans J Grabe; Nynke A Groenewold; Tiffany C Ho; Seung Bong Hong; Josselin Houenou; Benson Irungu; Neda Jahanshad; Habibolah Khazaie; Hosung Kim; Ekaterina Koshmanova; Desi Kocevska; Peter Kochunov; Oti Lakbila-Kamal; Jeanne Leerssen; Meng Li; Annemarie I Luik; Vincenzo Muto; Justinas Narbutas; Gustav Nilsonne; Victoria S O'Callaghan; Alexander Olsen; Ricardo S Osorio; Sara Poletti; Govinda Poudel; Joyce E Reesen; Liesbeth Reneman; Mathilde Reyt; Dieter Riemann; Ivana Rosenzweig; Masoumeh Rostampour; Amin Saberi; Julian Schiel; Christina Schmidt; Anouk Schrantee; Emma Sciberras; Tim J Silk; Kang Sim; Hanne Smevik; Jair C Soares; Kai Spiegelhalder; Dan J Stein; Puneet Talwar; Sandra Tamm; Giana L Teresi; Sofie L Valk; Eus Van Someren; Gilles Vandewalle; Maxime Van Egroo; Henry Völzke; Martin Walter; Rick Wassing; Frederik D Weber; Antoine Weihs; Lars Tjelta Westlye; Margaret J Wright; Mon-Ju Wu; Nathalia Zak; Mojtaba Zarei
Journal:  J Sleep Res       Date:  2021-04-28       Impact factor: 3.981

2.  Changes in sleep architecture in German Armed Forces personnel with posttraumatic stress disorder compared with depressed and healthy control subjects.

Authors:  Laura Haberland; Helge Höllmer; Holger Schulz; Kai Spiegelhalder; Robert Gorzka
Journal:  PLoS One       Date:  2019-04-17       Impact factor: 3.240

  2 in total

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