Objective methods for reliable detection of concealed depression
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 f...
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| Format: | Article |
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Frontiers
2015
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| Online Access: | https://eprints.nottingham.ac.uk/29458/ |
| _version_ | 1848793789842325504 |
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| author | Solomon, Cynthia Valstar, Michel F. Morriss, Richard K. Crowe, John |
| author_facet | Solomon, Cynthia Valstar, Michel F. Morriss, Richard K. Crowe, John |
| author_sort | Solomon, Cynthia |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | 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, Chang, 2012) 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 analyzed 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 behavior remained so during concealed behavior. 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 behavior, 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. |
| first_indexed | 2025-11-14T19:05:53Z |
| format | Article |
| id | nottingham-29458 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:05:53Z |
| publishDate | 2015 |
| publisher | Frontiers |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-294582020-05-04T17:06:51Z https://eprints.nottingham.ac.uk/29458/ Objective methods for reliable detection of concealed depression Solomon, Cynthia Valstar, Michel F. Morriss, Richard K. Crowe, John 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, Chang, 2012) 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 analyzed 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 behavior remained so during concealed behavior. 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 behavior, 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. Frontiers 2015-04-15 Article PeerReviewed Solomon, Cynthia, Valstar, Michel F., Morriss, Richard K. and Crowe, John (2015) Objective methods for reliable detection of concealed depression. Frontiers in ICT, 2 . 5/1-5/16. ISSN 2297-198X http://journal.frontiersin.org/article/10.3389/fict.2015.00005/abstract doi:10.3389/fict.2015.00005 doi:10.3389/fict.2015.00005 |
| spellingShingle | Solomon, Cynthia Valstar, Michel F. Morriss, Richard K. Crowe, John Objective methods for reliable detection of concealed depression |
| title | Objective methods for reliable detection of concealed depression |
| title_full | Objective methods for reliable detection of concealed depression |
| title_fullStr | Objective methods for reliable detection of concealed depression |
| title_full_unstemmed | Objective methods for reliable detection of concealed depression |
| title_short | Objective methods for reliable detection of concealed depression |
| title_sort | objective methods for reliable detection of concealed depression |
| url | https://eprints.nottingham.ac.uk/29458/ https://eprints.nottingham.ac.uk/29458/ https://eprints.nottingham.ac.uk/29458/ |