Time Series Classification Diagnosis Model based on Partical Swarm Optimization and Support Vector Machine
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    Abstract:

    This paper presents a fMRI timeseries classification diagnosis model based on particle swarm optimizationsupport vector machine (PSOSVM), which achieves more accurate judgments and distinctions between patients and healthy individuals by deeply analyzing multidimensional timeseries data of brain regions. This approach is significantly different from the other existing related research work in four aspects as follows: Constructing the feature representation for multidimensional timeseries data of brain regions based on the autoregressive (AR) model; Constructing the classification scheme for multidimensional timeseries of brain regions based on the support vector machine (SVM) model; Constructing the parameter optimization strategy for the classification learning based on the particle swarm optimization (PSO) algorithm; Constructing the classification diagnosis framework for fMRI timeseries data by integrating the above feature representation, optimized classification and parameter optimization patterns. With the mental depression disorder (MDD) as a specific case of empirical analysis, our classification diagnosis model has obtained very positive numerical computation results.

    Reference
    [1]Banerjee T.P., Das S. Multi-sensor data fusion using support vector machine for motor fault detection [J]. Information Sciences, 2012, 217: 96-107.
    [2]Calhoun, V.D., et al. Temporal lobe and “default” hemodynamic brain modes discriminate between schizophrenia and bipolar disorder. Hum Brain Mapp, 2008, 29(11): 1265-1275.
    [3]Chupin M., Gerardin E., Cuingnet R., et al. Fully automatic hippocampus segmentation and classification in Alzheimer’s disease and mild cognitive impairment applied on data from ADNI [J]. Hippocampus, 2009, 19(6): 579-587.
    [4]Costafreda S.G., Chu C., Ashburaer J., et al. Prognostic and diagnostic potential of the structural neuroanatomy of depression [J]. PLoS One, 2009, 4(7).
    [5]Davatzikos C., et al, Whole-brain morphometric study of schizophrenia revealing a spatially complex set of focal abnormalities. Arch Gen Psychiatry, 2005, 62(11): 1218-1227.
    [6]de Moraes R.M., dos Santos Machado L. Online Assessment in Medical Simulators Based on Virtual Reality Using Fuzzy Gaussian Naive Bayes[J]. Multiple-Valued Logic and Soft Computing, 2012,18(5-6): 479-492.
    [7]Feis D.L., et al. Decoding gender dimorphism of the human brain using multimodal anatomical and diffusion MRI data. Neuroimage, 2013, 70: 250-257.
    [8]Fu C.H., Mourao-Miranda J., Costafreda S.G., et al. Pattern classification of sad facial processing: toward the development of neurobiological markers in depression [J]. Biological psychiatry, 2008, 63(7).
    [9]Gong Q., Wu Q., Scarpazza C., et al. Prognostic prediction of therapeutic response in depression using high-field MR imaging [J]. Neuroimage, 2011, 55(4): 1497-1503.
    [10]Guo H., Cao X.H., Liu Z.F., et al. Machine learning classifier using abnormal brain network topological metrics in major depressive disorder [J]. Neuroreport, 2012, 23(17): 1006-1011.
    [11]Kamitani Y., Tong F. Decoding the visual and subjective contents of the human brain [J]. Nature Neuroscience, 2005, 8: 679-685.
    [12]Kloppel S., et al. Automatic classification of MR scans in Alzheimer's disease. Brain, 2008. 131(3): 681-689.
    [13]Li Y., Wang Y., Wu G.,et al. Discriminant analysis of longitudina1 cortical thickness changes in Ahheimer’S diseaseusing dynamic and network features [J]. Neurobiology of Aging, 2012, 33(2).
    [14]Mueller S.G., et al. A two-level multimodality imaging Bayesian network approach for classification of partial epilepsy: preliminary data. Neuroimage, 2013, 71: 224-232.
    [15]Norman K.A., Polyn S.M., Detre G.J., et al. Beyond mind-reading: Multi-voxel pattern analysis of fMRI data [J]. Trends in Cognitive Sciences. 2006, 10(9): 424-430.
    [16]Park H.S., Cho S.B. Evolutionary attribute ordering in Bayesian networks for predicting the metabolic syndrome [J]. Expert Systems with Applications, 2012, 39(4): 4240-4249.
    [17]Pereira F., Mitchell T., Botvinick M. Machine learning classifiers and fMPJ: a tutorial overview [J]. Neuroimage, 2009, 45(1): 199-209.
    [18]Santos J.E, Li D. Temporal Bayesian Knowledge Bases-Reasoning about uncertainty with temporal constraints [J]. Expert Systems with Applications, 2012, 39(17): 12905-12917.
    [19]Wee C.Y., Yap P.T., Zhang D.Q., et al. Identification of MCI individuals using structural and functional connectivity networks [J]. NeuroImage, 2012, 59(3): 2045-2056.
    [20]Xie S.Y., Guo R., Li N.F., et al. Brain fMRI processing and classification based on combination of PCA and SVM [J]. IJCNN’09, 2009, 3384-3389.
    [21]蒋芸, 李战怀. 基于改进的SVM分类器的医学图像分类新方法 [J]. 计算机应用研究, 2008, 25(1): 53-55.Jiang Yu, Li Zhanhuai. New medical image classify approach based on improved SVM classifier. Application Research of Computers, 2008, 25(1): 53-55.
    [22]李传明, 王健, 桂莉 等. AD和MCI病人前额叶注意功能区fMRI检测 [J]. 青岛大学医学院学报, 2009, 45(4): 331-334.Li Chuanming, Wang Jian, Gui Li, et al. A bold-fMRI study of prefrontal cortex in Alzheimer disease, mild cognitive impairment and normal control subjects. Acta Academiae Medicinae Qingdao Uinversitatis, 2009, 45(4): 331-334.
    [23]吕卓, 谢松云, 赵金 等. 基于SVM及其改进算法的fMRI图像分类性能研究 [J]. 电子设计工程, 2011, 19(16) : 24-27.Lv Zhuo, Xie Songyun, Zhao Jin, et al. Research on performance of fMRI image classification based on SVM and its improved algorithm. Electronic Design Engineering, 2011, 19(16) : 24-27.
    [24]相洁, 陈俊杰. 基于SVM的fMRI数据分类: 一种解码思维的方法 [J]. 计算机研究与发展, 2010, 47(2): 286-291.Xiang Jie, Chen Junjie. SVM based fMRI data classification: an approach to decode mental state. Journal of Computer Research and Development, 2010, 47(2): 286-291.
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ZHANG Tao, ZHANG Minghui, LI Qingwei, ZHANG Yuejie. Time Series Classification Diagnosis Model based on Partical Swarm Optimization and Support Vector Machine[J].同济大学学报(自然科学版),2016,44(9):1450~1457

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History
  • Received:July 12,2015
  • Revised:July 05,2016
  • Adopted:June 20,2016
  • Online: October 10,2016
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