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用机器学习量化精神疾病中的面部影响变化

Jayson Jeganathan¹, Renate Thienel², Michael Breakspear³

¹School of Psychological Sciences, College of Engineering, Science and the Environment, University of Newcastle, Newcastle, NSW, Australia; Hunter Medical Research Institute, Newcastle, NSW, Australia.

Psychiatry research

|October 15, 2025

概括

机器学习揭示了精神病中明显的面部情绪模式. 患有精神病的个体表现出面部肌肉活动的改变,影响了对负面症状的诊断和治疗监测.

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.
情绪的神经科学情绪的神经科学

背景情况:

减少面部表情是精神分裂症等精神疾病中负面症状的标志.
传统的评价依赖于主观的评级尺度.
新的机器学习方法可以客观地评估面部情绪.

研究的目的:

使用机器学习和系统建模来评估精神病患者的面部情绪.
识别动态的面部情感模式及其与症状的关系.

主要方法:

获取了48名患有精神病的参与者和40名控制人观看喜剧的视频数据.
使用OpenFace软件提取了面部动作单位时间序列.
应用连续波波变换和隐藏的马尔科夫模型 (HMM) 来识别8个动态的面部情感模式.

主要成果:

患有精神病的个体表现出积极影响面部肌肉的活化减少和增加负面影响活动.
精神病组在负HMM状态的时间较少,在低活动状态的时间较长.
负面状态的时间与积极症状相关;低活动状态的持久性与负面症状相关.

结论:

精神病中的面部影响变化是复杂的,包括静态和动态的变化.
机器学习对面部变化的量化可能会提高诊断差异化.
客观的面部分析可以帮助监测负面症状的治疗疗效.

关键词:

情绪情绪情绪情绪面部动作单位面部动作单位面部影响影响面部影响面部表情面部表情隐藏的马尔科夫模型负面的症状是负面的症状.精神错乱是一种精神病.精神分裂症是一种精神分裂症.

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