查询结果:   郭浩,刘文钊,刘志芬,曹晓华,陈俊杰.静息态功能脑网络差异指标分析及抑郁症分类应用[J].计算机应用与软件,2014,31(12):85 - 88.
中文标题
静息态功能脑网络差异指标分析及抑郁症分类应用
发表栏目
应用技术与研究
摘要点击数
869
英文标题
DIFFERENCE INDEX ANALYSIS ON RESTING STATE FUNCTIONAL BRAIN NETWORK AND ITS APPLICATION IN MAJOR DEPRESSIVE DISORDER CLASSIFICATION
作 者
郭浩 刘文钊 刘志芬 曹晓华 陈俊杰 Guo Hao Liu Wenzhao Liu Zhifen Cao Xiaohua Chen Junjie
作者单位
太原理工大学计算机科学与技术学院 山西 太原 030024 山西医科大学第一医院精神卫生科 山西 太原 030001    
英文单位
College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024,Shanxi,China Mental Health Division, First Hospital of Shanxi Medical University, Taiyuan 030001,Shanxi,China    
关键词
抑郁症 复杂网络 特征选择 脑功能网络 分类
Keywords
Major depressive disorder Complex network Feature selection Functional brain network Classification
基金项目
国家自然科学基金项目(61070077,61170 136,81171290);山西省自然科学基金项目(2010011020-2,2011011015-4);山西省教育厅高校科技项目(20121003);太原理工大学青年基金项目(2012L014)
作者资料
郭浩,讲师,主研领域:智能信息处理,脑信息学,脑影像学,语义网。刘文钊,硕士生。刘志芬,博士生。曹晓华,博士。陈俊杰,教授。 。
文章摘要
为了构建辅助诊断模型,以提高抑郁症诊断的准确率。在连续的阈值空间(8%~32%)内构建所有被试的功能脑网络并使用复杂网络理论对抑郁症患者的脑网络进行分析。通过设定阈值,根据统计显著性提取不同数量的节点属性与全局属性组合作为分类特征,并选择四种不同的分类算法进行分类研究,以得到构建一个准确率较高的模型。结果是SVM和神经网络算法在阈值P为0.05下,所建的模型的分类模型的准确率较高,分别达82.78%及81.36%,因此利用该方法所构建的诊断模型可以用于抑郁症的辅助临床诊断中。
Abstract
In order to construct a computer-aided diagnosis model to improve the accuracy of depression diagnosis, we construct within continuous threshold space (8%~32%) the functional brain networks which are all in testing and analyse the brain networks of the depressive patients with complicated network theory. By setting the threshold, we extract the node attributes in different numbers depending on the statistical significance and combine them with global attributes to be the classification features, and select four classification algorithms to carry out the classification research so as to build a model with higher accuracy. The result is that the classification model of the models built by SVM and neural network algorithm under the threshold P=0.05 has higher accuracy, reaches 82.78% and 81.36% respectively, so the diagnosis model built by this method can be used in computer-aided clinical diagnosis for depression.[HQ]
下载PDF全文