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Identification of Mild Cognitive Impairment Using Extreme Learning Machines Model

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Intelligent Computing Theories and Methodologies (ICIC 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9226))

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Abstract

Alzheimer’s disease (AD) is very common disorder among the older people. Predicting mild cognitive impairment (MCI), an intermediate stage between normal cognition and dementia, in individuals with some symptoms of cognitive decline may have great influence on treatment choice and disease progression. In this work, a novel prediction model based on extreme learning machines algorithm was proposed to identify mild cognitive impairment using the information of some biomarkers, i.e., Position Emission Tomography (PET), Magnetic Resonance Imaging (MRI), and the cerebrospinal fluid (CSF). Firstly, each subject will be represented by three biomarkers using a vector with 201 dimensions after some essential pre-processing. Then, a prediction model based on extreme learning machine algorithm was trained to catch the difference between the patients and healthy persons. Finally, the model had been employed to identify identifying MCI participants from the normal people. Based on a dataset, including 99 MCI patients and 52 health controls, our proposed method achieved 70.86 % prediction accuracy with 67.68 % sensitivity at the precision of 76.92 %. Extensive experiments are performed to compare our method with state-of-the-art techniques, i.e., support vector machine (SVM) and SVM with fusion kernels. Experimental results demonstrate that proposed extreme learning machine is a powerful tool for predicting MCI with excellent performance and less time.

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References

  1. Westman, E., et al.: Combining MRI and CSF measures for classification of Alzheimer’s disease and prediction of mild cognitive impairment conversion. Neuroimage 62, 229–238 (2012)

    Article  Google Scholar 

  2. Abasolo, D., et al.: Approximate entropy and auto mutual information analysis of the electroencephalogram in Alzheimer’s disease patients. Med. Biol. Eng. Comput. 46, 1019–1028 (2008)

    Article  Google Scholar 

  3. Weiner, M.W., et al.: The Alzheimer’s disease neuroimaging initiative: a review of papers published since its inception. Alzheimers Dement. 9, e111–e194 (2013)

    Article  Google Scholar 

  4. Friston, K.J.: Modalities, modes, and models in functional neuroimaging. Science 326, 399–403 (2009)

    Article  Google Scholar 

  5. Orru, G., et al.: Using support vector machine to identify imaging biomarkers of neurological and psychiatric disease: a critical review. Neurosci. Biobehav. Rev. 36, 1140–1152 (2012)

    Article  Google Scholar 

  6. Costafreda, S.G., et al.: Automated hippocampal shape analysis predicts the onset of dementia in mild cognitive impairment. Neuroimage 56, 212–219 (2011)

    Article  Google Scholar 

  7. Misra, C., et al.: Baseline and longitudinal patterns of brain atrophy in MCI patients, and their use in prediction of short-term conversion to AD: results from ADNI. Neuroimage 44, 1415–1422 (2009)

    Article  Google Scholar 

  8. Li, Y., et al.: Discriminant analysis of longitudinal cortical thickness changes in Alzheimer’s disease using dynamic and network features. Neurobiol. Aging 33(2), 427 (2012)

    Article  Google Scholar 

  9. McEvoy, L.K., et al.: Alzheimer disease: quantitative structural neuroimaging for detection and prediction of clinical and structural changes in mild cognitive impairment. Radiology 251, 195–205 (2009)

    Article  Google Scholar 

  10. Cuingnet, R., et al.: Automatic classification of patients with Alzheimer’s disease from structural MRI: a comparison of ten methods using the ADNI database. Neuroimage 56, 766–781 (2010)

    Article  Google Scholar 

  11. Davatzikos, C., et al.: Prediction of MCI to AD conversion, via MRI, CSF biomarkers, and pattern classification. Neurobiol. Aging 32, 2322.e19–2322.e27 (2010)

    Google Scholar 

  12. Shaw, L.M., et al.: Cerebrospinal fluid biomarker signature in Alzheimer’s disease neuroimaging initiative subjects. Ann Neurol. 65, 403–413 (2009)

    Article  Google Scholar 

  13. Mattsson, N., et al.: CSF biomarkers and incipient Alzheimer disease in patients with mild cognitive impairment. JAMA 302, 385–393 (2009)

    Article  Google Scholar 

  14. Minati, L., et al.: Current concepts in Alzheimer’s disease: a multidisciplinary review. Am. J. Alzheimers Dis. Other Demen. 24, 95–121 (2009)

    Article  Google Scholar 

  15. Mattsson, N., et al.: CSF biomarkers and incipient Alzheimer disease in patients with mild cognitive impairment. JAMA-J. Am. Med. Assoc. 302, 385–393 (2009)

    Article  Google Scholar 

  16. Arnone, D., et al.: Magnetic resonance imaging studies in unipolar depression: systematic review and meta-regression analyses. Eur. Neuropsychopharmacol. 22, 1–16 (2011)

    Article  Google Scholar 

  17. Davatzikos, C., Resnick, S.M.: Degenerative age changes in white matter connectivity visualized in vivo using magnetic resonance imaging. Cereb. Cortex 12, 767–771 (2002)

    Article  Google Scholar 

  18. Ellison-Wright, I., Bullmore, E.: Anatomy of bipolar disorder and schizophrenia: a meta-analysis. Schizophr. Res. 117, 1–12 (2010)

    Article  Google Scholar 

  19. Etkin, A., Wager, T.D.: Functional neuroimaging of anxiety: a meta-analysis of emotional processing in PTSD, social anxiety disorder, and specific phobia. Am. J. Psychiatry 164, 1476–1488 (2007)

    Article  Google Scholar 

  20. Smieskova, R., et al.: Neuroimaging predictors of transition to psychosis–a systematic review and meta-analysis. Neurosci. Biobehav. Rev. 34, 1207–1222 (2010)

    Article  Google Scholar 

  21. Zakzanis, K.K., et al.: A meta-analysis of structural and functional brain imaging in dementia of the Alzheimer’s type: a neuroimaging profile. Neuropsychol. Rev. 13, 1–18 (2003)

    Article  Google Scholar 

  22. Cui, Y., et al.: Identification of conversion from mild cognitive impairment to Alzheimer’s disease using multivariate predictors. PLoS ONE 6, e21896 (2011)

    Article  Google Scholar 

  23. Marmarelis, V.Z., et al.: Model-based physiomarkers of cerebral hemodynamics in patients with mild cognitive impairment. Med. Eng. Phys. 36, 628–637 (2014)

    Article  MathSciNet  Google Scholar 

  24. Petersen, R.C., et al.: Alzheimer’s disease neuroimaging initiative (ADNI): clinical characterization. Neurology 74, 201–209 (2010)

    Article  Google Scholar 

  25. Risacher, S.L., et al.: Baseline MRI predictors of conversion from MCI to probable AD in the ADNI cohort. Curr. Alzheimer Res. 6, 347–361 (2009)

    Article  Google Scholar 

  26. Davatzikos, C., et al.: Detection of prodromal Alzheimer’s disease via pattern classification of magnetic resonance imaging. Neurobiol. Aging 29, 514–523 (2008)

    Article  Google Scholar 

  27. Qiu, A., et al.: Regional shape abnormalities in mild cognitive impairment and Alzheimer’s disease. Neuroimage. 45, 656–661 (2009)

    Article  Google Scholar 

  28. Vemuri, P., et al.: Alzheimer’s disease diagnosis in individual subjects using structural MR images: validation studies. Neuroimage 39, 1186–1197 (2008)

    Article  MathSciNet  Google Scholar 

  29. Fan, Y., et al.: Structural and functional biomarkers of prodromal Alzheimer’s disease: a high-dimensional pattern classification study. Neuroimage 41, 277–285 (2008)

    Article  Google Scholar 

  30. Chapman, R.M., et al.: Predicting conversion from mild cognitive impairment to Alzheimer’s disease using neuropsychological tests and multivariate methods. J. Clin. Exp. Neuropsychol. 33, 187–199 (2010)

    Article  Google Scholar 

  31. Perri, R., et al.: Amnestic mild cognitive impairment: difference of memory profile in subjects who converted or did not convert to Alzheimer’s disease. Neuropsychology 21, 549–558 (2007)

    Article  Google Scholar 

  32. Zhang, D., et al.: Multimodal classification of Alzheimer’s disease and mild cognitive impairment. Neuroimage 55, 856–867 (2011)

    Article  Google Scholar 

  33. Ewers, M., et al.: Prediction of conversion from mild cognitive impairment to Alzheimer’s disease dementia based upon biomarkers and neuropsychological test performance. Neurobiol. Aging 33, 1203–1214 (2010)

    Article  Google Scholar 

  34. Huang, G.B.: An insight into extreme learning machines: random neurons, random features and kernels. Cogn. Comput. 6(3), 376–390 (2014)

    Article  Google Scholar 

  35. Shi, J., et al.: SEMG-based hand motion recognition using cumulative residual entropy and extreme learning machine. Med. Biol. Eng. Comput. 51, 417–427 (2013)

    Article  Google Scholar 

  36. Huang, G.B., Chen, L.: Convex incremental extreme learning machine. Neurocomputing 70, 3056–3062 (2007)

    Article  Google Scholar 

  37. Huang, G.B., et al.: Extreme learning machine: theory and applications. Neurocomputing 70, 489–501 (2006)

    Article  Google Scholar 

  38. Huang, G.B., et al.: Extreme learning machine for regression and multiclass classification. IEEE Trans. Syst. Man Cybern. B Cybern. 42, 513–529 (2012)

    Article  Google Scholar 

  39. You, Z.H., et al.: Prediction of protein-protein interactions from amino acid sequences with ensemble extreme learning machines and principal component analysis. BMC Bioinformatics 14(Suppl 8), S10 (2013)

    Article  Google Scholar 

  40. Wang, B., et al.: Radial basis function neural network ensemble for predicting protein-protein interaction sites in heterocomplexes. Protein Pept. Lett. 17, 1111–1116 (2010)

    Article  Google Scholar 

  41. Wang, B., et al.: Predicting protein interaction sites from residue spatial sequence profile and evolution rate. FEBS Lett. 580, 380–384 (2006)

    Article  Google Scholar 

  42. Hidalgo-Munoz, A.R., et al.: EEG study on affective valence elicited by novel and familiar pictures using ERD/ERS and SVM-RFE. Med. Biol. Eng. Comput. 52, 149–158 (2014)

    Article  Google Scholar 

  43. Kim, K.A., et al.: Mortality prediction of rats in acute hemorrhagic shock using machine learning techniques. Med. Biol. Eng. Comput. 51, 1059–1067 (2013)

    Article  Google Scholar 

Download references

Acknowledgement

This work was supported by the National Science Foundation of China (Nos. 61300058, 61472282 and 61374181), and Anhui Provincial Natural Science Foundation (No.1508085MF129).

Author information

Authors and Affiliations

  1. School of Electronics and Information Engineering, Tongji University, Shanghai, 201804, China

    Wen Zhang, Hao Shen, Zhiwei Ji & Bing Wang

  2. The Advanced Research Institute of Intelligent Sensing Network, Tongji University, Shanghai, 201804, China

    Wen Zhang, Hao Shen, Zhiwei Ji & Bing Wang

  3. The Key Laboratory of Embedded System and Service Computing, Tongji University, Shanghai, 201804, China

    Wen Zhang, Hao Shen, Zhiwei Ji & Bing Wang

  4. Department of Clinical Laboratory, Tongde Hospital of Zhejiang Province, 234th Gucui Road, Hangzhou, 310012, China

    Guanmin Meng

Authors
  1. Wen Zhang
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  2. Hao Shen
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  3. Zhiwei Ji
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  4. Guanmin Meng
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  5. Bing Wang
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Corresponding author

Correspondence to Bing Wang .

Editor information

Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. University of Ulsan, Ulsan, Korea (Republic of)

    Kang-Hyun Jo

  3. Liverpool John Moores University, Liverpool, United Kingdom

    Abir Hussain

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© 2015 Springer International Publishing Switzerland

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Zhang, W., Shen, H., Ji, Z., Meng, G., Wang, B. (2015). Identification of Mild Cognitive Impairment Using Extreme Learning Machines Model. In: Huang, DS., Jo, KH., Hussain, A. (eds) Intelligent Computing Theories and Methodologies. ICIC 2015. Lecture Notes in Computer Science(), vol 9226. Springer, Cham. https://doi.org/10.1007/978-3-319-22186-1_59

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  • DOI: https://doi.org/10.1007/978-3-319-22186-1_59

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-22185-4

  • Online ISBN: 978-3-319-22186-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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