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学者姓名：栗觅
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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 ：
Semi-supervised learning has always been a hot topic in machine learning. It uses a large number of unlabeled data to improve the performance of the model. This paper combines the co training strategy and random forest to propose a novel semi-supervised regression algorithm: semi supervised random forest regression model based on co-training and grouping with information entropy (E-CoGRF), and applies it to the evaluation of depression symptoms severity. The algorithm inherits the ensemble characteristics of random forest, and combines well with co-training. In order to balance the accuracy and diversity of co-training random forests, the algorithm proposes a grouping strategy to decision trees. Moreover, the information entropy is used to measure the confidence, which avoids unnecessary repeated training and improves the efficiency of the model. In the practical application of evaluation of depression symptoms severity, we collect cognitive behavioral data of emotional conflict based on the depressive affective disorder. And on this basis, feature construction and normalization preprocessing are carried out. Finally, the test is conducted on 35 labeled and 80 unlabeled depression patients. The result shows that the proposed algorithm obtains MAE (Mean Absolute Error) = 3.63 and RMSE (Root Mean Squared Error) = 4.50, which is better than other semi-supervised regression algorithms. The proposed method effectively solves the modeling difficulties caused by insufficient labeled samples, and has important reference value for the diagnosis of depression symptoms severity.
Keyword ：
semi-supervised learning semi-supervised learning emotional conflict emotional conflict E-CoGRF E-CoGRF symptoms severity symptoms severity depression depression
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| GB/T 7714 | Lu, Shengfu , Shi, Xin , Li, Mi et al. Semi-supervised random forest regression model based on co-training and grouping with information entropy for evaluation of depression symptoms severity [J]. | MATHEMATICAL BIOSCIENCES AND ENGINEERING , 2021 , 18 (4) : 4586-4602 . |
| MLA | Lu, Shengfu et al. "Semi-supervised random forest regression model based on co-training and grouping with information entropy for evaluation of depression symptoms severity" . | MATHEMATICAL BIOSCIENCES AND ENGINEERING 18 . 4 (2021) : 4586-4602 . |
| APA | Lu, Shengfu , Shi, Xin , Li, Mi , Jiao, Jinan , Feng, Lei , Wang, Gang . Semi-supervised random forest regression model based on co-training and grouping with information entropy for evaluation of depression symptoms severity . | MATHEMATICAL BIOSCIENCES AND ENGINEERING , 2021 , 18 (4) , 4586-4602 . |
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Abstract ：
Up to now, there is still the absence of research about depression recognition using resting-state functional magnetic resonance imaging (rest_fMRI) and deep learning. Previous studies have shown that regional homogeneity (ReHo) of rest_fMRI (rest_ReHo_fMRI) is a characterization of the functional synchronization of adjacent voxels in brain regions, and the mental and behavioral abnormalities in depression are due to an imbalance of ReHo synchronization in some brain functional areas. Accordingly, this paper presents a method for depression recognition using rest_ReHo_fMRI. First, the rest_ReHo_fMRI is extracted from the preprocessed rest-fMRI by calculation. Then, deep convolutional networks (such as VGG16) pretrained on ImageNet are used to automatically complete extracting the classification features from rest_ReHo_fMRI. Finally, the Kernel Extreme Learning Machine (KELM) was used to classify the depression. The results of the test set show that the proposed method achieves 89.07% in sensitivity and 89.74% in specificity. This study suggests that features of rest_ReHo_fMRI can be used as biomarkers to distinguish depression from normal people.
Keyword ：
Depression Depression resting-state functional magnetic resonance imaging (rest_fMRI) resting-state functional magnetic resonance imaging (rest_fMRI) rest_fMRI with ReHo (rest_ReHo_fMRI) rest_fMRI with ReHo (rest_ReHo_fMRI) kernel extreme learning machine (KELM) kernel extreme learning machine (KELM) regional homogeneity (ReHo) regional homogeneity (ReHo) fewer-shot leaning fewer-shot leaning
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| GB/T 7714 | Li, Mi , Zhang, Jinyu , Zhai, Qian et al. AUTOMATED RECOGNITION OF DEPRESSION FROM FEWER-SHOT LEANING IN RESTING-STATE fMRI WITH ReHo USING DEEP CONVOLUTIONAL NEURAL NETWORK [J]. | JOURNAL OF MECHANICS IN MEDICINE AND BIOLOGY , 2021 , 21 (10) . |
| MLA | Li, Mi et al. "AUTOMATED RECOGNITION OF DEPRESSION FROM FEWER-SHOT LEANING IN RESTING-STATE fMRI WITH ReHo USING DEEP CONVOLUTIONAL NEURAL NETWORK" . | JOURNAL OF MECHANICS IN MEDICINE AND BIOLOGY 21 . 10 (2021) . |
| APA | Li, Mi , Zhang, Jinyu , Zhai, Qian , Kang, Jiaming , Lu, Shengfu , Yang, Jian . AUTOMATED RECOGNITION OF DEPRESSION FROM FEWER-SHOT LEANING IN RESTING-STATE fMRI WITH ReHo USING DEEP CONVOLUTIONAL NEURAL NETWORK . | JOURNAL OF MECHANICS IN MEDICINE AND BIOLOGY , 2021 , 21 (10) . |
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Abstract ：
The research of deep convolutions neural network (DNN) in the depression recognition has become a popular topic. In this paper, we propose a method for depression recognition based on the regional homogeneity (ReHo) in emotional task state functional magnetic resonance imaging (task-fMRI) using DNN. First, the task-fMRI is extracted by processing the fMRI of emotional stimulation tasks. And the task-fMRI with ReHo (ReHo-task-fMRI) is calculated based on task-fMRI. And then, convolutional networks of DNN (such as VGG16, etc.) pre-trained on ImageNet are used to automatically complete extracting the classification features from ReHo-taskfMRI. Finally, the Kernel Extreme Learning Machine (KELM) was used to classify the depression. The results of test set showing that for depression recognition, the sensitivity and specificity of ReHo-task-fMRI were 87.46% and 85.35%, however that of task-fMRI were only 67.69% and 55.44%. This study suggest that compared with emotional task-fMRI, ReHo-task-fMRI can better represent the characteristics of brain dysfunction for patients with depression.
Keyword ：
Deep convolutions neural network (DNN) Deep convolutions neural network (DNN) Regional homogeneity (ReHo) Regional homogeneity (ReHo) Functional magnetic resonance imaging (fMRI) Functional magnetic resonance imaging (fMRI) Major depressive disorder (MDD) Major depressive disorder (MDD) Kernel Extreme Learning Machine (KELM) Kernel Extreme Learning Machine (KELM)
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| GB/T 7714 | Li, Mi , Liu, Minshuai , Kang, Jiaming et al. Depression Recognition Method Based on Regional Homogeneity Features from Emotional Response fMRI Using Deep Convolutional Neural Network [J]. | PROCEEDINGS OF 2021 3RD INTERNATIONAL CONFERENCE ON INTELLIGENT MEDICINE AND IMAGE PROCESSING (IMIP 2021) , 2021 : 45-49 . |
| MLA | Li, Mi et al. "Depression Recognition Method Based on Regional Homogeneity Features from Emotional Response fMRI Using Deep Convolutional Neural Network" . | PROCEEDINGS OF 2021 3RD INTERNATIONAL CONFERENCE ON INTELLIGENT MEDICINE AND IMAGE PROCESSING (IMIP 2021) (2021) : 45-49 . |
| APA | Li, Mi , Liu, Minshuai , Kang, Jiaming , Zhang, Wei , Lu, Shengfu . Depression Recognition Method Based on Regional Homogeneity Features from Emotional Response fMRI Using Deep Convolutional Neural Network . | PROCEEDINGS OF 2021 3RD INTERNATIONAL CONFERENCE ON INTELLIGENT MEDICINE AND IMAGE PROCESSING (IMIP 2021) , 2021 , 45-49 . |
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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 ：
The goal of this paper is to reveal the influence of temperatures on the triaxial strength of asphalt mixtures. The triaxial compression and triaxial tensile tests, as well as plane tensile and compression/axial tensile tests, which were developed by the self-developed triaxial test equipment to carry out the complex stress state under different temperatures (5 degrees C, 10 degrees C, 15 degrees C, 20 degrees C, and 25 degrees C), were performed on the asphalt mixture. The ultimate failure strength of the material shows that the temperature and stress state significantly affect the triaxial strength characteristics of the asphalt mixture, and the three-dimensional strength decreases by the increase in temperature. Under the triaxial compression and triaxial tensile stress state, the resistance of the octahedral shear stress increases with the hydrostatic stress. A three-dimensional strength calculation model is established based on the influence of temperature and characterization by the tensile meridian, compression meridian, and failure strength envelope. It reveals the change of failure strength envelope with increasing temperatures and decreasing hydrostatic stress under complex stress state. It provides experimental and theoretical references to the design of asphalt pavement structure of different temperature conditions according to the three-dimensional stress state.
Keyword ：
Hydrostatic stress Hydrostatic stress Pavement engineering Pavement engineering Triaxial tests Triaxial tests Three-dimensional strength model Three-dimensional strength model
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| GB/T 7714 | Huang, Tuo , Li, Mi , Yang, Yi et al. Unified Strength Models of an Asphalt Mixture under Different Temperatures and Three-Dimensional Stresses [J]. | JOURNAL OF MATERIALS IN CIVIL ENGINEERING , 2020 , 32 (11) . |
| MLA | Huang, Tuo et al. "Unified Strength Models of an Asphalt Mixture under Different Temperatures and Three-Dimensional Stresses" . | JOURNAL OF MATERIALS IN CIVIL ENGINEERING 32 . 11 (2020) . |
| APA | Huang, Tuo , Li, Mi , Yang, Yi , Xie, Jing , Liu, Hongfu , Yao, Hui et al. Unified Strength Models of an Asphalt Mixture under Different Temperatures and Three-Dimensional Stresses . | JOURNAL OF MATERIALS IN CIVIL ENGINEERING , 2020 , 32 (11) . |
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Abstract ：
The paper constructed a depression classification model based on emotionally related eye-movement data and kernel extreme learn machine (ELM). In order to improve the classification ability of the model, we use particle swarm optimization (PSO) to optimize the model parameters (regularization coefficient C and the parameter a in the kernel function). At the same time, in order to avoid to be caught in the local optimum and improve PSO's searching ability, we use improved chaotic PSO optimization algorithm and Gauss mutation strategy to increase PSO's particle diversity. The classification results show that the accuracy, sensitivity and specificity of classification models without parameter optimization and Gauss mutation strategy are 80.23%, 80.31% and 79.43%, respectively, while those results of classification model using improved chaotic projection model and Gauss mutation strategy are improved to 88.55%, 87.71% and 89.42%, respectively. Compared with other classification methods of depression, the proposed classification method has better performance on depression recognition.
Keyword ：
Mutation Mutation Depression Depression Extreme Learning Machine (ELM) Extreme Learning Machine (ELM) Particle Swarm Optimization (PSO) Particle Swarm Optimization (PSO) Chaos Chaos
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| GB/T 7714 | Lu, Shengfu , Liu, Sa , Li, Mi et al. Depression Classification Model Based on Emotionally Related Eye-Movement Data and Kernel Extreme Learning Machine [J]. | JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS , 2020 , 10 (11) : 2668-2674 . |
| MLA | Lu, Shengfu et al. "Depression Classification Model Based on Emotionally Related Eye-Movement Data and Kernel Extreme Learning Machine" . | JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS 10 . 11 (2020) : 2668-2674 . |
| APA | Lu, Shengfu , Liu, Sa , Li, Mi , Shi, Xin , Li, Richeng . Depression Classification Model Based on Emotionally Related Eye-Movement Data and Kernel Extreme Learning Machine . | JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS , 2020 , 10 (11) , 2668-2674 . |
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Abstract ：
The method from DS evidence theory based multi-modal information decision fusion uses the classification structure information which the correct and error classification information provided by the classifiers. These two types of information affect the fusion results of DS evidence theory. This paper proposes a new method(DShW) for correct and error classification information in the balanced classification structure information based on DS evidence theory. That is, a method based on inertia weight normalization is introduced in the confusion matrix. To adjust the specific gravity of correct and error classification in classification structure information by changing the size of the value h, so as to achieve the purpose of balancing correct and error classification information. By comparing with other classifiers, we find that the DShW method effectively improves the accuracy of decision fusion.
Keyword ：
decision-level fusion decision-level fusion multimodal data fusion multimodal data fusion DS evidence theory DS evidence theory inertia weight inertia weight
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| GB/T 7714 | Lu, Shengfu , Li, Peng , Li, Mi . An Improved Multi-modal Data Decision Fusion Method Based on DS Evidence Theory [C] . 2020 : 1684-1690 . |
| MLA | Lu, Shengfu et al. "An Improved Multi-modal Data Decision Fusion Method Based on DS Evidence Theory" . (2020) : 1684-1690 . |
| APA | Lu, Shengfu , Li, Peng , Li, Mi . An Improved Multi-modal Data Decision Fusion Method Based on DS Evidence Theory . (2020) : 1684-1690 . |
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Abstract ：
This paper presents a method of depression recognition based on direct measurement of affective disorder. Firstly, visual emotional stimuli are used to obtain eye movement behavior signals and physiological signals directly related to mood. Then, in order to eliminate noise and redundant information and obtain better classification features, statistical methods (FDR corrected t-test) and principal component analysis (PCA) are used to select features of eye movement behavior and physiological signals. Finally, based on feature extraction, we use kernel extreme learning machine (KELM) to recognize depression based on PCA features. The results show that, on the one hand, the classification performance based on the fusion features of eye movement behavior and physiological signals is better than using a single behavior feature and a single physiological feature; on the other hand, compared with previous methods, the proposed method for depression recognition achieves better classification results. This study is of great value for the establishment of an automatic depression diagnosis system for clinical use.
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| GB/T 7714 | Li, Mi , Cao, Lei , Zhai, Qian et al. Method of Depression Classification Based on Behavioral and Physiological Signals of Eye Movement [J]. | COMPLEXITY , 2020 , 2020 . |
| MLA | Li, Mi et al. "Method of Depression Classification Based on Behavioral and Physiological Signals of Eye Movement" . | COMPLEXITY 2020 (2020) . |
| APA | Li, Mi , Cao, Lei , Zhai, Qian , Li, Peng , Liu, Sa , Li, Richeng et al. Method of Depression Classification Based on Behavioral and Physiological Signals of Eye Movement . | COMPLEXITY , 2020 , 2020 . |
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Abstract ：
The method from DS evidence theory based multi-modal information decision fusion uses the classification structure information which the correct and error classification information provided by the classifiers. These two types of information affect the fusion results of DS evidence theory. This paper proposes a new method(DShW) for correct and error classification information in the balanced classification structure information based on DS evidence theory. That is, a method based on inertia weight normalization is introduced in the confusion matrix. To adjust the specific gravity of correct and error classification in classification structure information by changing the size of the value h, so as to achieve the purpose of balancing correct and error classification information. By comparing with other classifiers, we find that the DShW method effectively improves the accuracy of decision fusion. © 2020 IEEE.
Keyword ：
Classification (of information) Classification (of information) Modal analysis Modal analysis Errors Errors Decision theory Decision theory
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| GB/T 7714 | Lu, Shengfu , Li, Peng , Li, Mi . An Improved Multi-modal Data Decision Fusion Method Based on DS Evidence Theory [C] . 2020 : 1684-1690 . |
| MLA | Lu, Shengfu et al. "An Improved Multi-modal Data Decision Fusion Method Based on DS Evidence Theory" . (2020) : 1684-1690 . |
| APA | Lu, Shengfu , Li, Peng , Li, Mi . An Improved Multi-modal Data Decision Fusion Method Based on DS Evidence Theory . (2020) : 1684-1690 . |
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