[1]付常洋,王瑜,肖洪兵,等.基于多尺度功能脑网络融合特征的抑郁症分类算法[J].中国医学物理学杂志,2020,37(4):439-444.[doi:DOI:10.3969/j.issn.1005-202X.2020.04.008]
 FU Changyang,WANG Yu,XIAO Hongbing,et al.Classification of depression using fusion features based on multi-scale functional brain network[J].Chinese Journal of Medical Physics,2020,37(4):439-444.[doi:DOI:10.3969/j.issn.1005-202X.2020.04.008]
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基于多尺度功能脑网络融合特征的抑郁症分类算法()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
37
期数:
2020年第4期
页码:
439-444
栏目:
医学影像物理
出版日期:
2020-04-29

文章信息/Info

Title:
Classification of depression using fusion features based on multi-scale functional brain network
文章编号:
1005-202X(2020)04-0439-06
作者:
付常洋王瑜肖洪兵邢素霞
北京工商大学计算机与信息工程学院食品安全大数据技术北京市重点实验室, 北京 100048
Author(s):
FU Changyang WANG Yu XIAO Hongbing XING Suxia
Beijing Key Laboratory of Big Data Technology for Food Safety, School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China
关键词:
抑郁症功能脑网络多尺度特征融合支持向量机
Keywords:
Keywords: depression functional brain network multi-scale feature fusion support vector machine
分类号:
R318;TP181
DOI:
DOI:10.3969/j.issn.1005-202X.2020.04.008
文献标志码:
A
摘要:
【摘要】提出一种多尺度功能脑网络融合特征的抑郁症分类方法,具体思想包括:首先通过精细化脑区,建立4种不同尺度的脑网络;然后对每种尺度的脑网络分别提取局部特征和全局特征,并将多种尺度脑网络的特征进行有效融合并降维;最后使用支持向量机对患者脑部功能磁共振影像进行分类。试验结果表明,分别提取局部特征和全局特征,并进行有效融合,可以提升识别效果;空间尺度减小会得到更多有效特征,进而能够有效提升分类结果;多尺度特征融合也可以在很大程度上对分类结果起到积极作用。与传统单一大尺度脑网络方法相比,本研究提出的方法获得了更加优秀的试验结果,识别率可达88.67%,充分验证了本研究提出方法的有效性和可行性,并为抑郁症患者的临床诊断与治疗提供生物学依据。
Abstract:
Abstract: A novel method for the classification of depression is proposed based on a multi-scale functional brain network and fusion features. After 4 different scales of brain networks are established by refining the brain region,the local and global features are extracted from each scale of brain network, and the features of multi-scale brain networks are effectively fused and the dimensionality is reduced. Finally, support vector machine is used to classify the functional magnetic resonance images of the brain.The experimental results show that it is effective to improve the recognition effect by the fusion of separately extracted local and global features. More effective features can be obtained by reducing the spatial scale, which can remarkably improve the classification results. Multi-scale feature fusion can also greatly promote the accuracy and recall rate of classification. Compared with the traditional method with single large-scale brain networks, the proposed method achieves better performances. The classification accuracy by the proposed method reaches 88.67%, which fully verifies the effectiveness and feasibility of the proposed method, and also provides a biological basis for the clinical diagnosis and treatment of depression.

相似文献/References:

[1]郭家梁,等.基于振幅-周期二维分布的脑电复杂度分析[J].中国医学物理学杂志,2016,33(6):633.[doi:10.3969/j.issn.1005-202X.2016.06.019]
[2]刘岩,李幼军,陈萌. 基于固有模态分解和深度学习的抑郁症脑电信号分类分析[J].中国医学物理学杂志,2017,34(9):963.[doi:DOI:10.3969/j.issn.1005-202X.2017.09.021]
 [J].Chinese Journal of Medical Physics,2017,34(4):963.[doi:DOI:10.3969/j.issn.1005-202X.2017.09.021]

备注/Memo

备注/Memo:
【收稿日期】2019-12-08 【基金项目】国家自然科学基金(61671028);国家重大科技研发子课题(ZLJC6 03-5-1);北京工商大学校级两科培育基金项目(19008001270) 【作者简介】付常洋,在读研究生,主要从事图像处理、机器学习方面的研究,E-mail: fcy112@outlook.com 【通信作者】王瑜,博士,教授,主要从事图像处理与模式识别的研究,E-mail: wangyu@btbu.edu.cn
更新日期/Last Update: 2020-04-29