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学者姓名：栗觅
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Abstract ：
Depression is a serious mental disorder, significantly burdens individuals, families, and society. For clinical psychiatrists, assessing the severity of depression is a crucial tool in selecting treatment approaches and evaluating their effectiveness. Although many studies in machine learning have focused on the automatic evaluation of self-rating scales [such as the Beck Depression Inventory-II (BDI-II) and the Patient Health Questionnaire-8 (PHQ-8)], research into the machine learning-based automatic evaluation of the medical clinical assessment scale [such as the Hamilton Depression Scale (HAMD)] has not yet been focused on. In this study, an end-to-end automatic evaluation device for HAMD and Patient Health Questionnaire-9 (PHQ-9) scores was developed. In addition, we constructed a dataset consisting of emotional facial expression videos (eFEVs) signals and emotional pupil-wave (ePW) signals from 65 patients with depression. The dataset has HAMD and PHQ-9 score labels, encompassing two emotional states: sadness and happiness. We built a 3-dimensional convolutional neural network + long short-term memory (3DCNN + LSTM) model framework and a multiscale 1-dimensional convolutional neural network (1DCNN) to learn and extract features from eFEVs and ePW automatically. The results showed that compared with the previous evaluation methods for depression levels, the evaluation precision of HAMD and PHQ-9 has been improved significantly. The results also showed that, in both HAMD and PHQ-9 evaluations, the evaluation precision of eFEVs was superior to ePW, and HAMD is better than PHQ-9. These studies indicated that both emotional facial expressions and ePW can better represent depressive mood in patients with depression, especially emotional facial expressions, and the predictive precision of the medical scale is significantly better than the self-rating scale. This automated assessment method and device can assist doctors in diagnosing depressive symptoms more effectively and serve as an evaluation tool for treatment efficacy.
Keyword ：
Hamilton Depression Scale (HAMD) Hamilton Depression Scale (HAMD) Predictive models Predictive models Deep learning Deep learning Depression Depression Data mining Data mining Pupils Pupils Patient Health Questionnaire-9 (PHQ-9) Patient Health Questionnaire-9 (PHQ-9) Feature extraction Feature extraction symptom severity symptom severity patients with depression patients with depression Convolutional neural networks Convolutional neural networks Videos Videos Visualization Visualization Brain modeling Brain modeling Physiology Physiology
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| GB/T 7714 | Li, Mi , Lu, Zeying , Cao, Qishuang et al. Automatic Assessment Method and Device for Depression Symptom Severity Based on Emotional Facial Expression and Pupil-Wave [J]. | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT , 2024 , 73 . |
| MLA | Li, Mi et al. "Automatic Assessment Method and Device for Depression Symptom Severity Based on Emotional Facial Expression and Pupil-Wave" . | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 73 (2024) . |
| APA | Li, Mi , Lu, Zeying , Cao, Qishuang , Gao, Junlong , Hu, Bin . Automatic Assessment Method and Device for Depression Symptom Severity Based on Emotional Facial Expression and Pupil-Wave . | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT , 2024 , 73 . |
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Abstract ：
本发明公开了一种基于静态和动态表情图像的联合表情编码系统，包括图像预处理模块、动态表情图像生成模块、动态权重图像生成模块和联合表情编码图像生成模块。同时公开了一种基于上述系统的联合表情编码方法，采用上述一种基于静态和动态表情图像的联合表情编码系统及其方法，将静态表情图像和动态表情图像按照编码方法合并到一张图像中，使其能够同时表征静态的和动态的表情信息，提高基于面部表情的情感识别能力。
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| GB/T 7714 | 栗觅 , 陈阳阳 , 王钰琪 et al. 基于静态和动态表情图像的联合表情编码系统及其方法 : CN202211687009.5[P]. | 2022-12-27 . |
| MLA | 栗觅 et al. "基于静态和动态表情图像的联合表情编码系统及其方法" : CN202211687009.5. | 2022-12-27 . |
| APA | 栗觅 , 陈阳阳 , 王钰琪 , 胡斌 . 基于静态和动态表情图像的联合表情编码系统及其方法 : CN202211687009.5. | 2022-12-27 . |
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Abstract ：
1.本外观设计产品的名称：带心理状态评估图形用户界面的显示屏幕面板。2.本外观设计产品的用途：利用虚拟现实眼动技术对抑郁、焦虑和压力风险水平进行评估的屏幕面板，可用于台式电脑、笔记本电脑、平板电脑、手机、手表、智能手表、掌上电脑、抑郁症评估仪、抑郁症治疗仪、心理状态评估仪。3.本外观设计产品的设计要点：在于图形用户界面。4.最能表明设计要点的图片或照片：主视图。5.无设计要点，省略后视图、俯视图、仰视图、左视图、右视图。6.图形用户界面的用途：用于操作设备对抑郁、焦虑和压力风险水平进行评估。7.图形用户界面在产品中的区域：在设备操作面板部分。8.变化状态：图形用户界面的变化状态说明 : (1)打开系统，显示出登录界面，即主视图。(2)在主视图输入账号和密码输入完成后，点击登录按钮，进入界面状态变化图1。(3)点击界面状态变化图1的设置按钮，显示界面状态变化图2，左侧为识别参数，右侧为系统参数，填完点击界面状态变化图2返回按钮，进入主视图。(4)点击主视图的管理按钮，进入界面状态变化图3，此界面为设备使用人员的信息界面，可以针对设备使用人员的信息进行录入、查询、修改和删除，填完点击界面状态变化图3返回按钮，进入主视图。(5)点击主视图资料按钮，进入界面状态变化图4，即信息填写模块，输入测试者的个人信息，填完点击界面状态变化图4的返回按钮，进入主视图。(6)点击主视图的开始按钮，进入界面状态变化图5，VR眼镜中和电脑的屏幕中会播放视频，同时设备对参试者进行数据采集。(7)视频播放结束，采集过程也随之结束，系统会弹出检测完成的窗口，即界面状态变化图6。(8)点击界面状态变化图6的OK按钮，评估按键亮起，点击界面状态变化图6的评估按钮进行评估，进入界面状态变化图7，显示评估完成，点击界面状态变化图7的返回按钮，回到主视图。(9)点击主视图的打印按钮，显示界面状态变化图8，点击界面状态变化图8的打印报告按钮打印报告。(10)点击界面状态变化图8的返回按钮，回到主视图，如需采集下一个人，继续点主视图的资料按钮，重复以上步骤，如果全部采集完成，点击主视图的退出按钮，结束。
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| GB/T 7714 | 栗觅 , 胡斌 , 吕胜富 et al. 带心理状态评估图形用户界面的显示屏幕面板 : CN202230398161.6[P]. | 2022-06-27 . |
| MLA | 栗觅 et al. "带心理状态评估图形用户界面的显示屏幕面板" : CN202230398161.6. | 2022-06-27 . |
| APA | 栗觅 , 胡斌 , 吕胜富 , 康嘉明 . 带心理状态评估图形用户界面的显示屏幕面板 : CN202230398161.6. | 2022-06-27 . |
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Abstract ：
1.本外观设计产品的名称：心理状态评估机。2.本外观设计产品的用途：本外观设计产品利用虚拟现实眼动技术对抑郁、焦虑和压力风险水平进行评估。3.本外观设计产品的设计要点：在于形状。4.最能表明设计要点的图片或照片：组合状态立体图。5.本外观设计产品为组件产品，组件1为主机，组件2为VR眼镜。
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| GB/T 7714 | 栗觅 , 胡斌 , 吕胜富 et al. 心理状态评估机 : CN202230397826.1[P]. | 2022-06-27 . |
| MLA | 栗觅 et al. "心理状态评估机" : CN202230397826.1. | 2022-06-27 . |
| APA | 栗觅 , 胡斌 , 吕胜富 , 康嘉明 . 心理状态评估机 : CN202230397826.1. | 2022-06-27 . |
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Abstract ：
To improve the diagnosis accuracy of non-severe depression (NSD), this article proposes a diagnosis method of NSD based on cognitive behavior of emotional conflict. First, the original classification features are constructed based on the cognitive behavior of emotional conflict and statistical distribution, and a classification normalization method is proposed to preprocess the feature data. Then, the relief algorithm and principal component analysis (PCA) are recruited for feature processing. Finally, four classifiers [k-nearest neighbor (KNN), support vector machine (SVM), kernel extreme learning machine (KELM), and random forest (RF)] are used to classify NSD patients and normal subjects. The test results show that among all the classifiers, RF achieves the highest classification sensitivity and specificity of 92% and 88%, respectively. Compared with the results of other NSD diagnosis methods in recent years, it has a better performance. The diagnostic method for NSD proposed in this article has obvious performance advantages and provides technical support for improving the accuracy of clinical depression diagnosis. Furthermore, it also provides a new idea and method for the diagnosis and screening of depression.
Keyword ：
Depression diagnosis Depression diagnosis emotional conflict emotional conflict Electroencephalography Electroencephalography Sensitivity Sensitivity major depressive disorder (MDD) major depressive disorder (MDD) Depression Depression Functional magnetic resonance imaging Functional magnetic resonance imaging Feature extraction Feature extraction normalization by category normalization by category Task analysis Task analysis Support vector machines Support vector machines non-severe depression (NSD) non-severe depression (NSD)
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| GB/T 7714 | Li, Mi , Zhang, Jinyu , Song, Jie et al. A Clinical-Oriented Non-Severe Depression Diagnosis Method Based on Cognitive Behavior of Emotional Conflict [J]. | IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS , 2022 , 10 (1) : 131-141 . |
| MLA | Li, Mi et al. "A Clinical-Oriented Non-Severe Depression Diagnosis Method Based on Cognitive Behavior of Emotional Conflict" . | IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 10 . 1 (2022) : 131-141 . |
| APA | Li, Mi , Zhang, Jinyu , Song, Jie , Li, Zijian , Lu, Shengfu . A Clinical-Oriented Non-Severe Depression Diagnosis Method Based on Cognitive Behavior of Emotional Conflict . | IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS , 2022 , 10 (1) , 131-141 . |
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Abstract ：
In recent years, the use of convolutional neural network (DNN) for depression recognition has received a lot of research. However, DNN can only be employed for the modelling of video, audio and natural language processing, and is not suitable for learning with few samples and tabular data. In this paper, for tabular data based few shot learning, we propose a multiple parallel graph attention networks (pGAT) architecture. As the first, calculate information of multiple emotional bandwidths (such as information entropy, energy) based on the pupil size, and extract classification features according to their statistical distribution, and then, distance similarity (Euclid, Manhattan, Chebyshev) is used to construct three pGAT, finally, fuse the three streams for classifying depression. The results show that the classification sensitivity and specificity are 84.88% and 83.16%, respectively, which have better recognition performance than the related research recently.
Keyword ：
graph attention network (GAT) graph attention network (GAT) distance similarity distance similarity depression depression parallel graph attention network (pGAT) parallel graph attention network (pGAT)
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| GB/T 7714 | Lu, Shengfu , Jiao, Jinan , Li, Zhengzhen et al. A Depression Diagnosis Method Based on Parallel Graph Attention Network [J]. | PROCEEDINGS OF 2021 3RD INTERNATIONAL CONFERENCE ON INTELLIGENT MEDICINE AND IMAGE PROCESSING (IMIP 2021) , 2021 : 140-145 . |
| MLA | Lu, Shengfu et al. "A Depression Diagnosis Method Based on Parallel Graph Attention Network" . | PROCEEDINGS OF 2021 3RD INTERNATIONAL CONFERENCE ON INTELLIGENT MEDICINE AND IMAGE PROCESSING (IMIP 2021) (2021) : 140-145 . |
| APA | Lu, Shengfu , Jiao, Jinan , Li, Zhengzhen , Li, Mi , Zhang, Wei , Kang, Jiaming . A Depression Diagnosis Method Based on Parallel Graph Attention Network . | PROCEEDINGS OF 2021 3RD INTERNATIONAL CONFERENCE ON INTELLIGENT MEDICINE AND IMAGE PROCESSING (IMIP 2021) , 2021 , 140-145 . |
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Abstract ：
本公开提供了一种抑郁倾向评估方法及装置，该方法包括：获取被测者做出情绪决策的反应时间；对反应时间进行特征提取，得到原始特征参数；对原始特征参数进行降维处理得到评估特征参数；将评估特征参数输入预训练的超限学习机模型得到抑郁倾向指数。该方法通过被测者做出情绪决策的反应时间进行抑郁倾向评估能够增加对情绪和反应速度的关注度。与相关技术中的人工评估相比降低主观因素影响，与相关技术中通过生理信号进行评估相比降低了环境及设备噪声，有利于提高抑郁倾向评估的准确性。
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| GB/T 7714 | 栗觅 , 胡斌 , 吕胜富 et al. 抑郁倾向评估方法及装置 : CN202111179593.9[P]. | 2021-10-11 . |
| MLA | 栗觅 et al. "抑郁倾向评估方法及装置" : CN202111179593.9. | 2021-10-11 . |
| APA | 栗觅 , 胡斌 , 吕胜富 , 康嘉明 . 抑郁倾向评估方法及装置 : CN202111179593.9. | 2021-10-11 . |
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Abstract ：
本公开提供了一种情绪状态展示方法、装置及系统，该方法包括：基于情绪刺激信号获取被测者的面部图像；将面部图像输入第一网络模型得到情绪指数；对面部图像进行特征提取得到表情特征图像；根据情绪指数对表情特征图像中感兴趣的区域进行增强得到目标特征图像；在面部图像上叠加目标特征图像得到用于展示情绪状态的表情模式图像。方该法通过第一网络模型得到情绪指数能够初步确认被测者情绪状态的异常风险，通过对面部图像进行特征提取和部分增强能够进一步获取与情绪状态相关的图像信息，通过在面部图像上叠加目标特征图像得到的表情模式图像能够直观地展示被测者的情绪状态。
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| GB/T 7714 | 栗觅 , 胡斌 , 吕胜富 et al. 情绪状态展示方法、装置及系统 : CN202111178893.5[P]. | 2021-10-11 . |
| MLA | 栗觅 et al. "情绪状态展示方法、装置及系统" : CN202111178893.5. | 2021-10-11 . |
| APA | 栗觅 , 胡斌 , 吕胜富 , 康嘉明 , 杨闯 . 情绪状态展示方法、装置及系统 : CN202111178893.5. | 2021-10-11 . |
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Abstract ：
本公开提供了一种基于瞳孔波计算情绪指标的方法、装置及系统，该方法包括：采集被测试者处于平静状态时的瞳孔波作为平静瞳孔波；获取情绪指标对应的多个情绪；分别采集被测试者处于每个情绪时的瞳孔波作为情绪瞳孔波；根据平静瞳孔波和情绪瞳孔波计算情绪指标的指数值；其中，瞳孔波为瞳孔直径或瞳孔面积随时间的变化曲线。该方法通过采集被测试者的瞳孔波能够得到被测试者瞳孔的动态变化情况，与现有的自评量表和拍照获取被测试者状态图片相比，本公开根据动态的瞳孔的变化情况计算情绪指标能够降低主观因素的影响，进而得到更精准的情绪指标的指数值。
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| GB/T 7714 | 栗觅 , 胡斌 , 吕胜富 et al. 基于瞳孔波计算情绪指标的方法、装置及系统 : CN202111178895.4[P]. | 2021-10-11 . |
| MLA | 栗觅 et al. "基于瞳孔波计算情绪指标的方法、装置及系统" : CN202111178895.4. | 2021-10-11 . |
| APA | 栗觅 , 胡斌 , 吕胜富 , 康嘉明 , 张维 . 基于瞳孔波计算情绪指标的方法、装置及系统 : CN202111178895.4. | 2021-10-11 . |
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Abstract ：
本公开提供了一种基于静息态脑功能图像的情绪状态展示方法、装置及系统，该方法包括：获取被测者的静息态脑功能图像；将静息态脑功能图像输入第一网络模型得到情绪指数；对静息态脑功能图像进行特征提取得到初始特征图像；根据情绪指数对初始特征图像中感兴趣的区域进行增强得到目标特征图像；在静息态脑功能图像上叠加目标特征图像得到用于展示情绪状态的脑模式图像。该方法通过第一网络模型得到情绪指数能够初步确认被测者情绪状态的异常风险，通过对静息态脑功能图像进行特征提取和部分增强能够进一步获取与情绪状态相关的图像信息，通过在静息态脑功能图像上叠加目标特征图像得到的脑模式图像能够直观地展示被测者的情绪状态。
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| GB/T 7714 | 栗觅 , 胡斌 , 吕胜富 et al. 基于静息态脑功能图像的情绪状态展示方法、装置及系统 : CN202111178892.0[P]. | 2021-10-11 . |
| MLA | 栗觅 et al. "基于静息态脑功能图像的情绪状态展示方法、装置及系统" : CN202111178892.0. | 2021-10-11 . |
| APA | 栗觅 , 胡斌 , 吕胜富 , 康嘉明 . 基于静息态脑功能图像的情绪状态展示方法、装置及系统 : CN202111178892.0. | 2021-10-11 . |
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