Jian Bo-Lin, Chen Chieh-Li, Chu Wen-Lin, Huang Min-Wei
Department of Aeronautics and Astronautics, National Cheng Kung University, Tainan, 701, Taiwan.
Institute of Biomedical Engineering, National Cheng Kung University, Tainan, 701, Taiwan.
BMC Psychiatry. 2017 Jun 24;17(1):229. doi: 10.1186/s12888-017-1387-y.
Schizophrenia is a neurological disease characterized by alterations to patients' cognitive functions and emotional expressions. Relevant studies often use magnetic resonance imaging (MRI) of the brain to explore structural differences and responsiveness within brain regions. However, as this technique is expensive and commonly induces claustrophobia, it is frequently refused by patients. Thus, this study used non-contact infrared thermal facial images (ITFIs) to analyze facial temperature changes evoked by different emotions in moderately and markedly ill schizophrenia patients.
Schizophrenia is an emotion-related disorder, and images eliciting different types of emotions were selected from the international affective picture system (IAPS) and presented to subjects during ITFI collection. ITFIs were aligned using affine registration, and the changes induced by small irregular head movements were corrected. The average temperatures from the forehead, nose, mouth, left cheek, and right cheek were calculated, and continuous temperature changes were used as features. After performing dimensionality reduction and noise removal using the component analysis method, multivariate analysis of variance and the Support Vector Machine (SVM) classification algorithm were used to identify moderately and markedly ill schizophrenia patients.
Analysis of five facial areas indicated significant temperature changes in the forehead and nose upon exposure to various emotional stimuli and in the right cheek upon evocation of high valence low arousal (HVLA) stimuli. The most significant P-value (lower than 0.001) was obtained in the forehead area upon evocation of disgust. Finally, when the features of forehead temperature changes in response to low valence high arousal (LVHA) were reduced to 9 using dimensionality reduction and noise removal, the identification rate was as high as 94.3%.
Our results show that features obtained in the forehead, nose, and right cheek significantly differed between moderately and markedly ill schizophrenia patients. We then chose the features that most effectively distinguish between moderately and markedly ill schizophrenia patients using the SVM. These results demonstrate that the ITFI analysis protocol proposed in this study can effectively provide reference information regarding the phase of the disease in patients with schizophrenia.
精神分裂症是一种神经疾病，其特征为患者认知功能和情感表达的改变。相关研究常使用脑部磁共振成像（MRI）来探究脑区的结构差异和反应性。然而，由于该技术成本高昂且常诱发幽闭恐惧症，患者常拒绝接受。因此，本研究使用非接触式红外热面部图像（ITFIs）来分析中度和重度精神分裂症患者在不同情绪诱发下的面部温度变化。
精神分裂症是一种与情绪相关的疾病，在收集ITFIs期间，从国际情感图片系统（IAPS）中选择引发不同类型情绪的图像并呈现给受试者。使用仿射配准对ITFIs进行对齐，并校正由小幅度不规则头部运动引起的变化。计算前额、鼻子、嘴巴、左脸颊和右脸颊的平均温度，并将连续的温度变化用作特征。在使用成分分析方法进行降维和去噪后，使用多变量方差分析和支持向量机（SVM）分类算法来识别中度和重度精神分裂症患者。
对五个面部区域的分析表明，在暴露于各种情绪刺激时，前额和鼻子的温度有显著变化，在诱发高愉悦度低唤醒（HVLA）刺激时，右脸颊的温度有显著变化。在诱发厌恶情绪时，前额区域获得的最显著P值（低于0.001）。最后，当将对低愉悦度高唤醒（LVHA）反应的前额温度变化特征通过降维和去噪减少到9个时，识别率高达94.3%。
我们的结果表明，中度和重度精神分裂症患者在前额、鼻子和右脸颊获得的特征存在显著差异。然后，我们使用支持向量机选择了最能有效区分中度和重度精神分裂症患者的特征。这些结果表明，本研究中提出的ITFI分析方案可以有效地为精神分裂症患者的疾病阶段提供参考信息。
