A Multimodal Approach for Automatic Mania Assessment in Bipolar Disorder
- URL: http://arxiv.org/abs/2112.09467v1
- Date: Fri, 17 Dec 2021 12:09:01 GMT
- Title: A Multimodal Approach for Automatic Mania Assessment in Bipolar Disorder
- Authors: P{\i}nar Baki
- Abstract summary: We create a multimodal decision system based on recordings of the patient in acoustic, linguistic, and visual modalities.
We achieve a 64.8% unweighted average recall score, which improves the state-of-the-art performance achieved on this dataset.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Bipolar disorder is a mental health disorder that causes mood swings that
range from depression to mania. Diagnosis of bipolar disorder is usually done
based on patient interviews, and reports obtained from the caregivers of the
patients. Subsequently, the diagnosis depends on the experience of the expert,
and it is possible to have confusions of the disorder with other mental
disorders. Automated processes in the diagnosis of bipolar disorder can help
providing quantitative indicators, and allow easier observations of the
patients for longer periods. Furthermore, the need for remote treatment and
diagnosis became especially important during the COVID-19 pandemic. In this
thesis, we create a multimodal decision system based on recordings of the
patient in acoustic, linguistic, and visual modalities. The system is trained
on the Bipolar Disorder corpus. Comprehensive analysis of unimodal and
multimodal systems, as well as various fusion techniques are performed. Besides
processing entire patient sessions using unimodal features, a task-level
investigation of the clips is studied. Using acoustic, linguistic, and visual
features in a multimodal fusion system, we achieved a 64.8% unweighted average
recall score, which improves the state-of-the-art performance achieved on this
dataset.
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