Early detection of depression through facial expression recognition and electroencephalogram-based artificial intelligence-assisted graphical user interface
参考中译:通过面部表情识别和基于脑电波的人工智能辅助图形用户界面早期检测抑郁症


          

刊名：Neural Computing & Applications
作者：Gajendra Kumar（Department of Molecular Biology, Cell Biology and Biochemistry (MCB), Brown University）
Tanaya Das（School of Biomedical Engineering, Faculty of Engineering, The University of Sydney）
Kuldeep Singh（Department of Electronics Technology, Guru Nanak Dev University）
刊号：738E0033
ISSN：0941-0643
出版年：2024
年卷期：2024, vol.36, no.12
页码：6937-6954
总页数：18
分类号：TP18
关键词：Depression；Artificial intelligence；EEG；Emotion recognition；Graphical user interface
参考中译：抑郁症;人工智能;脑电;情绪识别;图形用户界面
语种：eng
文摘：Psychological disorders have increased globally at an alarming rate. Among these disorders, depression stands out as one of the leading and most prevalent conditions that have affected more than 280 million people. However, it remains widely undiagnosed and untreated due to lack of sensitive and reliable diagnostic tools. This underscores the imperative for the development of a sensitive and accurate diagnostic tool facilitating the early diagnosis of depression symptoms to mitigate the impending mental illness epidemic. To address this need, we developed an artificial intelligence (AI)-assisted tool utilizing facial expression-based emotion recognition and electroencephalogram (EEG) analysis for the detection of depression symptoms along with their severity level assessment. Our approach yielded successful detection of depression symptoms with an accuracy of 93.58%, a sensitivity of 92.70%, a specificity of 93.40%, and an f1-score of 93.68% through facial emotion recognition task. Additionally, severity level detection employing EEG biomarkers achieved an accuracy of 99.75%, a sensitivity of 99.75%, a specificity of 99.92%, and an f1-score of 99.75%. Consequently, a graphical user interface (GUI) tool was developed that seamlessly integrated the AI with facial image and EEG data inputs, enabling efficient recognition of depression from both real-time and pre-recorded data. The resulting AI assistant demonstrates high sensitivity, precision, and accuracy in the early detection of depression, establishing its potential as a reliable diagnostic tool. The application of our tool may be extended to clinicians, therapists, and hospitals for the identification of depression at its early stage.
参考中译：心理障碍在全球范围内以惊人的速度增加。在这些疾病中，抑郁症是影响超过2.8亿人的主要和最普遍的疾病之一。然而，由于缺乏敏感和可靠的诊断工具，它仍然普遍没有得到诊断和治疗。这突出表明，必须开发一种敏感和准确的诊断工具，促进抑郁症症状的早期诊断，以缓解即将到来的精神疾病流行。为了满足这一需求，我们开发了一种人工智能(AI)辅助工具，利用基于面部表情的情绪识别和脑电(EEG)分析来检测抑郁症状及其严重程度评估。该方法对抑郁症状的检测准确率为93.58%，敏感度为92.70%，特异度为93.40%，F1-Score为93.68%。此外，使用脑电生物标志物进行严重程度检测的准确率为99.75%，灵敏度为99.75%，特异度为99.92%，F1评分为99.75%。因此，开发了一种图形用户界面(GUI)工具，该工具将人工智能与面部图像和EEG数据输入无缝集成，从而能够从实时和预先记录的数据中高效识别抑郁症。由此产生的人工智能助手在抑郁症的早期检测中表现出高灵敏度、精确度和准确性，确立了其作为可靠诊断工具的潜力。我们的工具的应用可以扩展到临床医生、治疗师和医院，以便在抑郁症的早期阶段进行识别。



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