Paying attention to uncertainty: A stochastic multimodal transformers for post-traumatic stress disorder detection using video

IF 4.9 2区 医学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
{"title":"Paying attention to uncertainty: A stochastic multimodal transformers for post-traumatic stress disorder detection using video","authors":"","doi":"10.1016/j.cmpb.2024.108439","DOIUrl":null,"url":null,"abstract":"<div><h3>Background and Objectives:</h3><div>Post-traumatic stress disorder is a debilitating psychological condition that can manifest following exposure to traumatic events. It affects individuals from diverse backgrounds and is associated with various symptoms, including intrusive thoughts, nightmares, hyperarousal, and avoidance behaviors.</div></div><div><h3>Methods:</h3><div>To address this challenge this study proposes a decision support system powered by a novel multimodal deep learning approach, based on a stochastic Transformer and video data. This Transformer has the ability to take advantage of its stochastic activation function and layers that allow it to learn sparse representations of the inputs. The method leverages a combination of low-level features extracted using three modalities, including Mel-frequency cepstral coefficients extracted from audio recordings, Facial Action Units captured from facial expressions, and textual data obtained from the audio transcription. By considering these modalities, our proposed model captures a comprehensive range of information related to post-traumatic stress disorder symptoms, including vocal cues, facial expressions, and linguistic content.</div></div><div><h3>Results:</h3><div>The deep learning model was trained and evaluated on the eDAIC dataset, which consists of clinical interviews with individuals with and without post-traumatic disorder. The model achieved state-of-the-art results, demonstrating its effectiveness in accurately detecting PTSD, showing an impressive Root Mean Square Error of 1.98, and a Concordance Correlation Coefficient of 0.722, signifying the model’s superior performance compared to existing approaches.</div></div><div><h3>Conclusion:</h3><div>This work introduces a new method for post-traumatic stress disorder detection from videos by utilizing a multimodal stochastic Transformer model. The model makes use of a variety of modalities, such as text, audio, and visual data, to gather comprehensive and varied information in order to make the detection.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":null,"pages":null},"PeriodicalIF":4.9000,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computer methods and programs in biomedicine","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0169260724004322","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0

Abstract

Background and Objectives:

Post-traumatic stress disorder is a debilitating psychological condition that can manifest following exposure to traumatic events. It affects individuals from diverse backgrounds and is associated with various symptoms, including intrusive thoughts, nightmares, hyperarousal, and avoidance behaviors.

Methods:

To address this challenge this study proposes a decision support system powered by a novel multimodal deep learning approach, based on a stochastic Transformer and video data. This Transformer has the ability to take advantage of its stochastic activation function and layers that allow it to learn sparse representations of the inputs. The method leverages a combination of low-level features extracted using three modalities, including Mel-frequency cepstral coefficients extracted from audio recordings, Facial Action Units captured from facial expressions, and textual data obtained from the audio transcription. By considering these modalities, our proposed model captures a comprehensive range of information related to post-traumatic stress disorder symptoms, including vocal cues, facial expressions, and linguistic content.

Results:

The deep learning model was trained and evaluated on the eDAIC dataset, which consists of clinical interviews with individuals with and without post-traumatic disorder. The model achieved state-of-the-art results, demonstrating its effectiveness in accurately detecting PTSD, showing an impressive Root Mean Square Error of 1.98, and a Concordance Correlation Coefficient of 0.722, signifying the model’s superior performance compared to existing approaches.

Conclusion:

This work introduces a new method for post-traumatic stress disorder detection from videos by utilizing a multimodal stochastic Transformer model. The model makes use of a variety of modalities, such as text, audio, and visual data, to gather comprehensive and varied information in order to make the detection.
关注不确定性:利用视频检测创伤后应激障碍的随机多模态变换器
背景与目标:创伤后应激障碍是一种令人衰弱的心理疾病,可在遭受创伤事件后表现出来。方法:为了应对这一挑战,本研究基于随机变形器和视频数据,提出了一种由新型多模态深度学习方法驱动的决策支持系统。这种变形器能够利用其随机激活函数和层的优势,学习输入的稀疏表示。该方法综合利用了从三种模式中提取的低层次特征,包括从音频记录中提取的梅尔频率倒频谱系数、从面部表情中捕捉到的面部动作单元以及从音频转录中获得的文本数据。结果:深度学习模型在 eDAIC 数据集上进行了训练和评估,该数据集由患有和未患有创伤后应激障碍的临床访谈组成。结果:深度学习模型在 eDAIC 数据集上进行了训练和评估,该数据集由患有和未患有创伤后应激障碍的个人的临床访谈组成。该模型取得了最先进的结果,证明了其在准确检测创伤后应激障碍方面的有效性,显示出令人印象深刻的均方根误差(Root Mean Square Error)为 1.98,协整相关系数(Conordance Correlation Coefficient)为 0.722,表明该模型与现有方法相比具有更优越的性能。该模型利用文本、音频和视觉数据等多种模式来收集全面、多样的信息,从而进行检测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Computer methods and programs in biomedicine
Computer methods and programs in biomedicine 工程技术-工程:生物医学
CiteScore
12.30
自引率
6.60%
发文量
601
审稿时长
135 days
期刊介绍: To encourage the development of formal computing methods, and their application in biomedical research and medical practice, by illustration of fundamental principles in biomedical informatics research; to stimulate basic research into application software design; to report the state of research of biomedical information processing projects; to report new computer methodologies applied in biomedical areas; the eventual distribution of demonstrable software to avoid duplication of effort; to provide a forum for discussion and improvement of existing software; to optimize contact between national organizations and regional user groups by promoting an international exchange of information on formal methods, standards and software in biomedicine. Computer Methods and Programs in Biomedicine covers computing methodology and software systems derived from computing science for implementation in all aspects of biomedical research and medical practice. It is designed to serve: biochemists; biologists; geneticists; immunologists; neuroscientists; pharmacologists; toxicologists; clinicians; epidemiologists; psychiatrists; psychologists; cardiologists; chemists; (radio)physicists; computer scientists; programmers and systems analysts; biomedical, clinical, electrical and other engineers; teachers of medical informatics and users of educational software.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信