Objective methods for reliable detection of concealed depression

Solomon, Cynthia and Valstar, Michel F. and Morriss, Richard K. and Crowe, John (2015) Objective methods for reliable detection of concealed depression. Frontiers in ICT, 2 (5). ISSN 2297-198X

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Abstract

Recent research has shown that it is possible to automatically detect clinical depression from audio-visual recordings. Before considering integration in a clinical pathway, a key question that must be asked is whether such systems can be easily fooled. This work explores the potential of acoustic features to detect clinical depression in adults both when acting normally and when asked to conceal their depression. Nine adults diagnosed with mild to moderate depression as per the Beck Depression Inventory (BDI-II) and Patient Health Questionnaire (PHQ-9) were asked a series of questions and to read a excerpt from a novel aloud under two different experimental conditions. In one, participants were asked to act naturally and in the other, to suppress anything that they felt would be indicative of their depression. Acoustic features were then extracted from this data and analysed using paired t-tests to determine any statistically significant differences between healthy and depressed participants. Most features that were found to be significantly different during normal behaviour remained so during concealed behaviour. In leave-one-subject-out automatic classification studies of the 9 depressed subjects and 8 matched healthy controls, an 88% classification accuracy and 89% sensitivity was achieved. Results remained relatively robust during concealed behaviour, with classifiers trained on only non-concealed data achieving 81% detection accuracy and 75% sensitivity when tested on concealed data. These results indicate there is good potential to build deception-proof automatic depression monitoring systems.

Item Type: Article
Additional Information: This Document is Protected by copyright and was first published by Frontiers. All rights reserved. It is reproduced with permission.
Keywords: behaviomedics, depression, affective computing, social signal processing, automatic audio analysis
Schools/Departments: University of Nottingham UK Campus > Faculty of Engineering > Department of Electrical and Electronic Engineering
University of Nottingham UK Campus > Faculty of Medicine and Health Sciences > School of Medicine > Division of Psychiatry
University of Nottingham UK Campus > Faculty of Science > School of Computer Science
Identification Number: https://doi.org/10.3389/fict.2015.00005
Depositing User: Valstar, Michel
Date Deposited: 21 Jan 2016 10:57
Last Modified: 13 Sep 2016 15:01
URI: http://eprints.nottingham.ac.uk/id/eprint/31309

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