E-DANN: An Enhanced Domain Adaptation Network for Audio-EEG Feature Decoupling in Explainable Depression Recognition

IF 5.2 2区 医学 Q2 ENGINEERING, BIOMEDICAL
Qinglin Zhao;Hua Jiang;Zhongqing Wu;Lixin Zhang;Kunbo Cui;Kai Zheng;Jingyu Liu;Ran Cai;Mingqi Zhao;Fuze Tian;Bin Hu
{"title":"E-DANN: An Enhanced Domain Adaptation Network for Audio-EEG Feature Decoupling in Explainable Depression Recognition","authors":"Qinglin Zhao;Hua Jiang;Zhongqing Wu;Lixin Zhang;Kunbo Cui;Kai Zheng;Jingyu Liu;Ran Cai;Mingqi Zhao;Fuze Tian;Bin Hu","doi":"10.1109/TNSRE.2025.3608181","DOIUrl":null,"url":null,"abstract":"Given the significant global health burden caused by depression, numerous studies have utilized artificial intelligence techniques to objectively and automatically detect depression. However, existing research primarily focuses on improving the accuracy of depression recognition while overlooking the explainability of detection models and the evaluation of feature importance. In this paper, we propose a novel framework named Enhanced Domain Adversarial Neural Network (E-DANN) for depression detection. First, we extract joint features that combine audio-specific physical properties and electroencephalogram (EEG) responses to construct a multimodal feature space, which facilitates the understanding of how EEG signals dynamically respond to the physical properties of audio. Next, we employ the feature decoupling framework of E-DANN, which separates the extracted feature space into shared features and private features through adversarial training. The decoupled private features are then utilized for the binary classification of depression. Our experimental results validate the effectiveness of the proposed framework, which achieves accurate classification of normal controls and individuals with depression (accuracy: <inline-formula> <tex-math>$92.83~\\pm ~4.38$ </tex-math></inline-formula>%, specificity: <inline-formula> <tex-math>$93.56~\\pm ~7.25$ </tex-math></inline-formula>%, sensitivity: <inline-formula> <tex-math>$91.61~\\pm ~6.87$ </tex-math></inline-formula>%, and F1 score: <inline-formula> <tex-math>$91.81~\\pm ~4.52$ </tex-math></inline-formula>%). Furthermore, we employ an Explainable Artificial Intelligence (XAI) approach to hierarchically visualize feature importance and elucidate complex feature interaction patterns. In summary, this study provides a theoretical foundation for developing explainable diagnostic tools for depression and contributes to improving the clinical trustworthiness of AI-assisted diagnostic systems.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"3647-3661"},"PeriodicalIF":5.2000,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11156140","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/11156140/","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}
引用次数: 0

Abstract

Given the significant global health burden caused by depression, numerous studies have utilized artificial intelligence techniques to objectively and automatically detect depression. However, existing research primarily focuses on improving the accuracy of depression recognition while overlooking the explainability of detection models and the evaluation of feature importance. In this paper, we propose a novel framework named Enhanced Domain Adversarial Neural Network (E-DANN) for depression detection. First, we extract joint features that combine audio-specific physical properties and electroencephalogram (EEG) responses to construct a multimodal feature space, which facilitates the understanding of how EEG signals dynamically respond to the physical properties of audio. Next, we employ the feature decoupling framework of E-DANN, which separates the extracted feature space into shared features and private features through adversarial training. The decoupled private features are then utilized for the binary classification of depression. Our experimental results validate the effectiveness of the proposed framework, which achieves accurate classification of normal controls and individuals with depression (accuracy: $92.83~\pm ~4.38$ %, specificity: $93.56~\pm ~7.25$ %, sensitivity: $91.61~\pm ~6.87$ %, and F1 score: $91.81~\pm ~4.52$ %). Furthermore, we employ an Explainable Artificial Intelligence (XAI) approach to hierarchically visualize feature importance and elucidate complex feature interaction patterns. In summary, this study provides a theoretical foundation for developing explainable diagnostic tools for depression and contributes to improving the clinical trustworthiness of AI-assisted diagnostic systems.
E-DANN:一种用于可解释抑郁症识别的音频-脑电特征解耦的增强域自适应网络。
鉴于抑郁症造成的巨大全球健康负担,许多研究利用人工智能技术客观、自动地检测抑郁症。然而,现有的研究主要集中在提高抑郁症识别的准确性上,而忽视了检测模型的可解释性和特征重要性的评估。本文提出了一种新的抑郁症检测框架——增强域对抗神经网络(E-DANN)。首先,提取结合音频物理属性和脑电响应的联合特征,构建多模态特征空间,便于理解脑电信号对音频物理属性的动态响应。接下来,我们采用E-DANN的特征解耦框架,通过对抗性训练将提取的特征空间分离为共享特征和私有特征。然后利用解耦的私有特征对抑郁症进行二元分类。实验结果验证了该框架的有效性,实现了正常对照和抑郁症个体的准确分类(准确率:92.28±7.45%,特异性:94.67±7.30%,敏感性:89.11±1.01%,F1评分:90.89±8.60%)。此外,我们采用可解释的人工智能(XAI)方法分层可视化特征重要性并阐明复杂的特征交互模式。总之,本研究为开发可解释的抑郁症诊断工具提供了理论基础,并有助于提高人工智能辅助诊断系统的临床可信度。
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
CiteScore
8.60
自引率
8.20%
发文量
479
审稿时长
6-12 weeks
期刊介绍: Rehabilitative and neural aspects of biomedical engineering, including functional electrical stimulation, acoustic dynamics, human performance measurement and analysis, nerve stimulation, electromyography, motor control and stimulation; and hardware and software applications for rehabilitation engineering and assistive devices.
×
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:604180095
Book学术官方微信