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Charles Darwin proposed that facial expressions are an evolutionary adaptation for communication. He argued that these expressions are not influenced by culture but are universal across species. For example, a snarling expression with exposed teeth signals a threat in many animals, including humans. Darwin also suggested that displaying an emotion can intensify the feeling. Smiling, for example, could enhance one's sense of happiness. This idea laid the foundation for understanding the role...
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SFTNet:一种基于微表达的方法用于抑郁症检测.

Xingyun Li1, Xinyu Yi1, Jiayu Ye1

  • 1Faculty of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China.

Computer methods and programs in biomedicine
|November 21, 2023
PubMed
概括

本研究介绍了SFTNet,这是一个新的深度学习模型,用于使用微表达式自动检测抑郁症. 通过面部暗示,SFTNet可以准确地识别抑郁症,帮助临床诊断和早期干预.

关键词:
抑郁的检测检测 抑郁的检测情绪上的刺激.面部表情 面部表情微表达的微表达方式

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科学领域:

  • 计算精神病学是一种计算精神病学.
  • 情感计算是一种情感计算.
  • 机器学习用于心理健康诊断.

背景情况:

  • 抑郁症查对于预防病情恶化至关重要.
  • 微表达式为精神疾病检测提供了潜在的生物标志物.
  • 使用微表达式自动检测抑郁症仍未得到充分探索.

研究的目的:

  • 开发一种基于微表达式的自动抑郁检测方法.
  • 评估拟议的SFTNet模型的有效性.
  • 分析抑郁症患者的面部表情特征.

主要方法:

  • 收集了156名参与者的数据集 (76例抑郁症病例,80例对照).
  • 提出了一个双流模型,SFTNet,集成单时 (STNet) 和全时 (FTNet) 网络.
  • 分析了面部表情出现的平均次数 (ANO) 和平均持续时间 (AD).

主要成果:

  • 与对照组相比,抑郁症患者表现出更少,更不丰富的面部表情.
  • 在情绪刺激数据集上,SFTNet实现了高精度 (0.873),精度 (0.888) 和回忆 (0.846).
  • 在医生与患者对话数据集上,SFTNet表现出强的性能 (准确度:0.829,精度:0.817,回忆:0.837).

结论:

  • 抑郁症患者更容易表现出负面情绪.
  • 在基于微表情的抑郁症检测中,SFTNet的性能优于最先进的模型.
  • 提出的方法可以帮助临床医生诊断抑郁症.