■要检查语音和面部特征与抑郁之间的关联,焦虑,轻度认知障碍(MCI)的老年人和冷漠。
■通过音频和视频记录软件对319名MCI患者的语音和面部表情进行了数字记录。通过公共卫生问卷评估了三种最常见的神经精神症状(NPS),一般焦虑症,和冷漠评估量表,分别。使用开源数据分析工具包提取语音和面部特征。使用机器学习技术来验证所提取特征的诊断能力。
■不同的语音和面部特征与特定的NPS相关。抑郁症与频谱和时间特征有关,焦虑和冷漠的频率,能源,光谱,和时间特征。此外,抑郁症与面部特征(动作单元,AU)10、12、15、17、25,AU焦虑10、15、17、25、26、45,AU冷漠5、26、45。在男性和女性之间观察到语音和面部特征的显着差异。基于机器学习模型,检测抑郁症的最高准确度,焦虑,冷漠达到95.8%,96.1%,男性占83.3%,87.8%,88.2%,女性占88.6%,分别。
■抑郁症,焦虑,冷漠的特点是语言和面部特征不同。在这项研究中开发的机器学习模型在检测抑郁症方面表现出良好的分类,焦虑,和冷漠。音频和视频的组合可以提供用于精确分类这些症状的客观方法。
To examine the association between speech and facial features with depression, anxiety, and apathy in older adults with mild cognitive impairment (MCI).
Speech and facial expressions of 319 MCI patients were digitally recorded via audio and video recording software. Three of the most common neuropsychiatric symptoms (NPS) were evaluated by the Public Health Questionnaire, General Anxiety Disorder, and Apathy Evaluation Scale, respectively. Speech and facial features were extracted using the open-source data analysis toolkits. Machine learning techniques were used to validate the diagnostic power of extracted features.
Different speech and facial features were associated with specific NPS. Depression was associated with spectral and temporal features, anxiety and apathy with frequency, energy, spectral, and temporal features. Additionally, depression was associated with facial features (action unit, AU) 10, 12, 15, 17, 25, anxiety with AU 10, 15, 17, 25, 26, 45, and apathy with AU 5, 26, 45. Significant differences in speech and facial features were observed between males and females. Based on machine learning models, the highest accuracy for detecting depression, anxiety, and apathy reached 95.8%, 96.1%, and 83.3% for males, and 87.8%, 88.2%, and 88.6% for females, respectively.
Depression, anxiety, and apathy were characterized by distinct speech and facial features. The machine learning model developed in this study demonstrated good classification in detecting depression, anxiety, and apathy. A combination of audio and video may provide objective methods for the precise classification of these symptoms.