| Autor: |
Alice Othmani, Assaad Oussama Zeghina |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
|
| Zdroj: |
Healthcare Analytics, Vol 2, Iss , Pp 100090- (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2772-4425 |
| DOI: |
10.1016/j.health.2022.100090 |
| Popis: |
Major depressive disorder (MDD), also known as depression, is a common and serious mental disorder. It is characterized by a high rate of relapse or recurrence where a person might experience depressive episodes after being depression-free. Although numerous studies are proposed in the literature for depression recognition using video, to the best of our knowledge, only one preliminary study has been proposed in the literature for the automatic identification of signs of depression relapse using audiovisual cues without user intervention. In this paper, we propose a proof of concept of a deep learning-based approach for depression recognition and depression relapse prediction using videos of clinical interviews. We propose a correlation-based anomaly detection framework and a measure of similarity to depression where depression relapse is detected when the deep audiovisual patterns of a depression-free subject become close to the deep audiovisual patterns of depressed subjects. Thus, the correlation between the audiovisual encoding of a test subject and a deep audiovisual representation of depression is computed and is used for monitoring depressed subjects and for predicting relapse after depression. Very promising results are achieved with an accuracy of 80.99% and 82.55% respectively for relapse depression prediction on the DAIC-Woz dataset. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
|
