计算机科学 ›› 2022, Vol. 49 ›› Issue (5): 206-211.doi: 10.11896/jsjkx.210300049
李鹏祖, 李瑶, Ibegbu Nnamdi JULIAN, 孙超, 郭浩, 陈俊杰
LI Peng-zu, LI Yao, Ibegbu Nnamdi JULIAN, SUN Chao, GUO Hao, CHEN Jun-jie
摘要： 脑功能超网络的研究对脑疾病的准确诊断具有重要作用,目前已经有多种超网络的构建方法被应用于脑疾病的分类研究,但这些方法均未考虑到组间的重叠性问题。研究证明,组间的重叠性可能会对相关超网络模型的构建及构建后的分类应用产生影响,因此若仅使用非重叠组结构会限制其在超网络中的适用性。针对已应用于脑疾病分类研究的超网络构建方法在构建超网络模型时未考虑到分组之间的部分重叠性问题以及特征提取阶段的属性单一性问题,提出将多特征融合分析的重叠组套索方法应用于超网络的构建,并将其应用于抑郁症的诊断。结果表明,无论是在纯聚类系数属性下还是在多特征融合分析下,重叠组套索方法的分类性能较其他已有方法均有提高;在重叠组套索方法下,采用多特征融合分析较仅使用聚类系数属性分析获得了更高的分类准确率,达到了87.87%。
中图分类号:
| [1]ZHOU M N,NIU Y,CAO R,et al.Recognition and Analysis of Epileptic Signal Based on Phase Synchronization[J].Computer Engineering,2019,45(7):291-295,302. [2]SHI L,TONG H,DAIANU M,et al.Visual Analysis of Brain Networks Using Sparse Regression Models[J].ACM Transactions on Knowledge Discovery from Data,2018,12(1):1-30. [3]WU X,ZENG L L,SHEN H,et al.Blind source separation of functional MRI scans of the human brain based on canonical correlation analysis[J].Neurocomputing,2017,269(20):220-225. [4]MENG L.The evolution mechanism and application of hypernetwork[D].Xining:Qinghai Normal University,2020. [5]JIE B,WEE C Y,SHEN D,et al.Hyper-connectivity of functional networks for brain disease diagnosis[J].Medical Image Analysis,2016,32:84-100. [6]GUO H,LI Y,XU Y,et al.Resting-State Brain Functional Hyper-Network Construction Based on Elastic Net and Group Lasso Methods[J/OL].Frontiers in Neuroinformatics,2018,12:25.https://doi.org/10.3389/fninf.2018.00025. [7]ZHOU S,ZHOU J,ZHANG B.Overlapping group lasso forhigh-dimensional generalized linear models[J].Communications in Statistics－Theory and Methods,2019,48(19):4903-4917. [8]SHEN T,JIANG J H,LIN W,et al.Use of Overlapping Group LASSO Sparse Deep Belief Network to Discriminate Parkinson’sDisease and Normal Control[J].Frontiers in Neuroscience,2019,13:396-396. [9]TZOURIO-MAZOVER N,LANDEAU B,PAPATHANAS-SIOU D,et al.Automated Anatomical Labeling of Activations in SPM Using a Macroscopic Anatomical Parcellation of the MNI MRI Single-Subject Brain[J].NeuroImage,2002,15(1):273-289. [10]HARTONO,SITOMPUL O S,TULUS,et al.OptimizationModel of K-Means Clustering Using Artificial Neural Networks to Handle Class Imbalance Problem[C]//Iop Conference.2018. [11]LIU M.Analysis of Hypernetwork Characteristics and Its Application[D].Xining:Qinghai Normal University,2018. [12]CHE K L,MAO N,XIE H Z,et al.Altered spontaneous neural activity in postpartum depression:a resting-state functional magnetic resonance imaging study[J].Chinese Journal of Psychiatry,2020,53(3):243-248. [13]LAI C H,WU Y T,HOU Y M.Functional network-based statistics in depression:Theory of mind subnetwork and importance of parietal region[J].Journal of Affective Disorders,2017,217:132-137. [14]MA X Y,LIU J,LIU T Y,et al.Altered Resting-State Functional Activity in Medication-Naive Patients With First-Episode Major Depression Disorder vs.HealthyControl:A Quantitative Meta-Analysis[J/OL].Frontiers in Behavioral Neuroence,2019,13:89.https://doi.org/10.3389/fnbeh.2019.00089. [15]WANG X L,WANG D L.Hypothalamic Abnormalities and Major Depressive Disorder[J].Progress in Psychological Science,2015,23(10):1763-1774. [16]CHEN X D,HU X P,TAO L X,et al.Changes and significance of resting brain structure network in patients with first-episode depression[J].Journal of Anhui Medical University,2016,51(2):222-226. [17]ZENG J K,TANG D J,LIU H,et al.The characteristic of fractional amplitude of low frequency fluctuation in depression patients with suicidalideation:a resting-state functional magnetic resonance imaging study[J].Chinese Journal of Nervous and Mental Diseases,2017,43(1):19-25. [18]TENG C J,ZHANG N,WANG C,et al.Comparisons of theresting-state fMRI functional connectivity of the habenula in the first-episode current depression and remitted depression[J].Chinese Journal of Behavioral Medicine and Brain Science,2020,29(9):813-819. [19]WANG M L.Resting state functional magnetic resonance imaging of first episode depression patients before and after treatment[D].Kunming:Kunming Medical University,2016. [20]LU L P,SHAO Y L,SHEN Z X.Resting state functional magnetic resonance imaging analysis of brain network in depression[J].Chinese Modern Doctor,2020,58(26):120-123. [21]SACCHET M D,PRASAD G,FOLAND-ROSS L C,et al.Support Vector Machine Classification of Major Depressive Disorder Using Diffusion-Weighted Neuroimaging and Graph Theory[J/OL].Frontiers in Psychiatry,2015,6:21.https://www.frontiersin.org/articles/10.3389/fpsyt.2015.00021/full. [22]ROSA M J,PORTUGAL L,HAHN T,et al.Sparse network-based models for patient classification using fMRI[J].Neuro Image,2015,105:493-506. |
| [1] | 陈志强, 韩萌, 李慕航, 武红鑫, 张喜龙. 数据流概念漂移处理方法研究综述 Survey of Concept Drift Handling Methods in Data Streams 计算机科学, 2022, 49(9): 14-32. https://doi.org/10.11896/jsjkx.210700112 |
| [2] | 周旭, 钱胜胜, 李章明, 方全, 徐常胜. 基于对偶变分多模态注意力网络的不完备社会事件分类方法 Dual Variational Multi-modal Attention Network for Incomplete Social Event Classification 计算机科学, 2022, 49(9): 132-138. https://doi.org/10.11896/jsjkx.220600022 |
| [3] | 李瑶, 李涛, 李埼钒, 梁家瑞, Ibegbu Nnamdi JULIAN, 陈俊杰, 郭浩. 基于多尺度的稀疏脑功能超网络构建及多特征融合分类研究 Construction and Multi-feature Fusion Classification Research Based on Multi-scale Sparse Brain Functional Hyper-network 计算机科学, 2022, 49(8): 257-266. https://doi.org/10.11896/jsjkx.210600094 |
| [4] | 郝志荣, 陈龙, 黄嘉成. 面向文本分类的类别区分式通用对抗攻击方法 Class Discriminative Universal Adversarial Attack for Text Classification 计算机科学, 2022, 49(8): 323-329. https://doi.org/10.11896/jsjkx.220200077 |
| [5] | 武红鑫, 韩萌, 陈志强, 张喜龙, 李慕航. 监督和半监督学习下的多标签分类综述 Survey of Multi-label Classification Based on Supervised and Semi-supervised Learning 计算机科学, 2022, 49(8): 12-25. https://doi.org/10.11896/jsjkx.210700111 |
| [6] | 檀莹莹, 王俊丽, 张超波. 基于图卷积神经网络的文本分类方法研究综述 Review of Text Classification Methods Based on Graph Convolutional Network 计算机科学, 2022, 49(8): 205-216. https://doi.org/10.11896/jsjkx.210800064 |
| [7] | 闫佳丹, 贾彩燕. 基于双图神经网络信息融合的文本分类方法 Text Classification Method Based on Information Fusion of Dual-graph Neural Network 计算机科学, 2022, 49(8): 230-236. https://doi.org/10.11896/jsjkx.210600042 |
| [8] | 高振卓, 王志海, 刘海洋. 嵌入典型时间序列特征的随机Shapelet森林算法 Random Shapelet Forest Algorithm Embedded with Canonical Time Series Features 计算机科学, 2022, 49(7): 40-49. https://doi.org/10.11896/jsjkx.210700226 |
| [9] | 杨炳新, 郭艳蓉, 郝世杰, 洪日昌. 基于数据增广和模型集成策略的图神经网络在抑郁症识别上的应用 Application of Graph Neural Network Based on Data Augmentation and Model Ensemble in Depression Recognition 计算机科学, 2022, 49(7): 57-63. https://doi.org/10.11896/jsjkx.210800070 |
| [10] | 张洪博, 董力嘉, 潘玉彪, 萧宗志, 张惠臻, 杜吉祥. 视频理解中的动作质量评估方法综述 Survey on Action Quality Assessment Methods in Video Understanding 计算机科学, 2022, 49(7): 79-88. https://doi.org/10.11896/jsjkx.210600028 |
| [11] | 邵欣欣. TI-FastText自动商品分类算法 TI-FastText Automatic Goods Classification Algorithm 计算机科学, 2022, 49(6A): 206-210. https://doi.org/10.11896/jsjkx.210500089 |
| [12] | 郁舒昊, 周辉, 叶春杨, 王太正. SDFA:基于多特征融合的船舶轨迹聚类方法研究 SDFA:Study on Ship Trajectory Clustering Method Based on Multi-feature Fusion 计算机科学, 2022, 49(6A): 256-260. https://doi.org/10.11896/jsjkx.211100253 |
| [13] | 陈景年. 一种适于多分类问题的支持向量机加速方法 Acceleration of SVM for Multi-class Classification 计算机科学, 2022, 49(6A): 297-300. https://doi.org/10.11896/jsjkx.210400149 |
| [14] | 杨健楠, 张帆. 一种结合双注意力机制和层次网络结构的细碎农作物分类方法 Classification Method for Small Crops Combining Dual Attention Mechanisms and Hierarchical Network Structure 计算机科学, 2022, 49(6A): 353-357. https://doi.org/10.11896/jsjkx.210200169 |
| [15] | 杨涵, 万游, 蔡洁萱, 方铭宇, 吴卓超, 金扬, 钱伟行. 基于步态分类辅助的虚拟IMU的行人导航方法 Pedestrian Navigation Method Based on Virtual Inertial Measurement Unit Assisted by GaitClassification 计算机科学, 2022, 49(6A): 759-763. https://doi.org/10.11896/jsjkx.211200148 |
|
||
首页