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Independent Component Analysis-Support Vector Machine-Based Computer-Aided Diagnosis System for Alzheimer’s with Visual Support by:73 (Source: Crossref)

    Computer-aided diagnosis (CAD) systems constitute a powerful tool for early diagnosis of Alzheimer’s disease (AD), but limitations on interpretability and performance exist. In this work, a fully automatic CAD system based on supervised learning methods is proposed to be applied on segmented brain magnetic resonance imaging (MRI) from Alzheimer’s disease neuroimaging initiative (ADNI) participants for automatic classification. The proposed CAD system possesses two relevant characteristics: optimal performance and visual support for decision making. The CAD is built in two stages: a first feature extraction based on independent component analysis (ICA) on class mean images and, secondly, a support vector machine (SVM) training and classification. The obtained features for classification offer a full graphical representation of the images, giving an understandable logic in the CAD output, that can increase confidence in the CAD support. The proposed method yields classification results up to 89% of accuracy (with 92% of sensitivity and 86% of specificity) for normal controls (NC) and AD patients, 79% of accuracy (with 82% of sensitivity and 76% of specificity) for NC and mild cognitive impairment (MCI), and 85% of accuracy (with 85% of sensitivity and 86% of specificity) for MCI and AD patients.

    Data used in preparation of this article were obtained from the Alzheimer’s disease neuroimaging initiative (ADNI) database ( As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at:


    • 1. H. Adeli, S. Ghosh-Dastidar and N. Dadmehr, Alzheimer’s disease and models of computation: imaging, classification, and neural models, J. Alzheimers Dis. 7(3) (2005) 187–199. Crossref, Medline, ISIGoogle Scholar
    • 2. H. Adeli, S. Ghosh-Dastidar and N. Dadmehr, Alzheimer’s disease: models of computation and analysis of EEGs, Clin. EEG Neurosci. 36(3) (2005b) 131–140. Crossref, Medline, ISIGoogle Scholar
    • 3. H. Adeli, S. Ghosh-Dastidar and N. Dadmehr, A spatio-temporal wavelet-chaos methodology for EEG-based diagnosis of Alzheimers disease, Neurosci. Lett. 444(2) (2008) 190–194. Crossref, Medline, ISIGoogle Scholar
    • 4. H. Adeli and S. Ghosh-Dastidar, Automated EEG-based Diagnosis of Neurological Disorders. Inventing the Future of Neurology (CRC Press, Taylor & Francis, Boca Raton, Florida, 2010). CrossrefGoogle Scholar
    • 5. M. Ahmadlou, H. Adeli and A. Adeli, New diagnostic EEG markers of the Alzheimers disease using visibility graph, J. Neural Transm. 117(9) (2010) 1099–1109. Crossref, Medline, ISIGoogle Scholar
    • 6. A. Ahmadlou, H. Adeli and A. Adeli, Fractality and a wavelet-chao methodology for EEG-based diagnosis of Alzheimers disease, Alzheimer Dis. Assoc. Disord. 25(1) (2011) 85–92. Crossref, Medline, ISIGoogle Scholar
    • 7. M. Ahmadlou, A. Adeli, R. Bajo and H. Adeli, Complexity of functional connectivity networks in mild cognitive impairment patients during a working memory task, Clin. Neurophysiol. 125(4) (2014) 694–702. Crossref, Medline, ISIGoogle Scholar
    • 8. I. Álvarez, J. M. Górriz, J. Ramírez, D. Salas-Gonzalez and M. López, 18f-fdg pet imaging analysis for computer-aided Alzheimer’s diagnosis, Inf. Sci. 184 (2011) 903–916. Google Scholar
    • 9. I. Álvarez, J. M. Górriz, J. Ramírez, D. Salas-Gonzalez, M. López, C. G. Puntonet and F. Segovia, Alzheimer’s diagnosis using eigenbrains and support vector machines, IET Electron. Lett. 45 (2009) 342–343. Crossref, ISIGoogle Scholar
    • 10. I. Alvarez, J. M. Gorriz, J. Ramirez, D. Salas-Gonzalez, M. Lpez, C. G. Puntonet and F. Segovia, Independent component analysis of SPECT images to assist the Alzheimer’s disease diagnosis 2009, The Sixth International Symposium on Neural Networks (ISNN 2009) (Spring-Heidelberg, Berlin, 2009), pp. 411–419 Google Scholar
    • 11. K. J. Friston, C. D. Frith, R. J. Dolan, C. J. Price, S. Zeki, J. T. Ashburner and W. D. Penny, Human Brain Function, 2nd edn. (Academic Press, London, 2003). Google Scholar
    • 12. J. C. Baron, G. Chételat, B. Desgranges, G. Perchey, B. Landeau, V. de la Sayette, and F. Eustache, In vivo mapping of gray matter loss with voxel-based morphometry in mild alzheimers disease. Neuroimage 14 (2001) 298–309. Crossref, Medline, ISIGoogle Scholar
    • 13. J. P. Blass, Alzheimer’s disease and Alzheimer’s dementia: distinct but overlapping entities, Neurobiol. Aging 23 (2002) 1077–1084. Crossref, Medline, ISIGoogle Scholar
    • 14. E. E. Bron, et al., Standardized evaluation of algorithms for computer-aided diagnosis of dementia based on structural MRI: The CADDementia challenge, Neuroimage 111 (2015) 562–579. Crossref, Medline, ISIGoogle Scholar
    • 15. E. Castillo, D. Peteiro-Barral, B. Guijarro Berdinas and O. Fontenla-Romero, Distributed one-class support vector machine, Int. J. Neural Syst. 25(7) (2015) 1550029 (17 pages). Link, ISIGoogle Scholar
    • 16. V. D. Calhoun, R. F. Silva, T. Adal and S. Rachakonda, Comparison of PCA approaches for very large group ICA, Neuroimage 118 (2015) 662–666. Crossref, Medline, ISIGoogle Scholar
    • 17. G. Chetelat, B. Desgranges, V. de la Sayette, F. Viader, F. Eustache and J.-C. Baron, Mapping gray matter loss with voxel-based morphometry in mild cognitive impairment, Neuroreport 13(15) (2002) 1939–1943 Crossref, Medline, ISIGoogle Scholar
    • 18. J. S. Chou and A. D. Pham, Smart artificial firefly colony-based support vector regression for enhanced forecasting in civil engineering, Comput.-Aided Civ. Infrastruct. Eng. 30(9) (2015) 715–732. Crossref, ISIGoogle Scholar
    • 19. C. Chu, A.-L. Hsu, K.-H. Chou, P. Bandettini and C. Lin, Does feature selection improve classification accuracy? Impact of sample size and feature selection on classification using anatomical magnetic resonance images, NeuroImage 60 (2012) 59–70. Crossref, Medline, ISIGoogle Scholar
    • 20. D. Chyzhyk, M. Graña, A. Savio and J. Maiora, Hybrid dendritic computing with kernel-lica applied to Alzheimer’s disease detection in MRI, Neurocomputing 75 (2012) 72–77. Crossref, ISIGoogle Scholar
    • 21. A. Convit, J. de Asis, M. J. de Leon, C. Y. Tarshish, S. de Santi and H. Rusinek, Atrophy of the medial occipitotemporal, inferior, and middle temporal gyri in non-demented elderly predict decline to alzheimer’s disease, Neurobiol. Aging 21(1) (2000) 19–26. Crossref, Medline, ISIGoogle Scholar
    • 22. R. Cuingnet, E. Gerardin, J. Tessieras, G. Auzias and S. Lehricy, Automatic classification of patients with Alzheimer’s disease from structural MRI: a comparison of ten methods using the adni database, Neuroimage 56 (2010) 766–781. Crossref, Medline, ISIGoogle Scholar
    • 23. C. Davatzikos, Y. Fan, X. Wu, D. Shen and S. M. Resnick, Detection of prodromal Alzheimer’s disease via pattern classification of magnetic resonance imaging, Neurobiol. Aging 29 (2008) 514–523. Crossref, Medline, ISIGoogle Scholar
    • 24. B. C. Dickerson et al., MRI-derived entorhinal and hippocampal atrophy in incipient and very mild alzheimer’s disease, Neurobiol. Aging 22(5) (2001) 747–754 Crossref, Medline, ISIGoogle Scholar
    • 25. B. Dubois, H. H. Feldman, C. Jacova, S. T. DeKosky, P. Barberger-Gateau, J. Cummings, A. Delacourte, D. Galasko, S. Gauthier and G. Jicha, Research criteria for the diagnosis of alzheimer’s disease: revising the nincdsadrda criteria, Lancet Neurol. 6 (2007) 734–746. Crossref, Medline, ISIGoogle Scholar
    • 26. R. P. W. Duin, Classifiers in almost empty spaces, in Proc. 15th Int. Conf. on Pattern Recognition, Berulona, 2000 (IEEE, Spain, 2000) Vol. 2, pp. 1–7. Google Scholar
    • 27. A. Drzezga, N. Lautenschlager, H. Siebner, M. Riemenschneider, F. Willoch, S. Minoshima, M. Schwaiger and A. Kurz, Cerebral metabolic changes accompanying conversion of mild cognitive impairment into Alzheimer’s disease: a PET follow-up study, Eur. J. Nucl. Med. Mol. Imag. 30 (2003) 1104–1113. Crossref, Medline, ISIGoogle Scholar
    • 28. F. Falahati, E. Westman and A. Simmons, Multivariate data analysis and machine learning in Alzheimer’s disease with a focus on structural magnetic resonance imaging J. Alzheimer’s Dis. 41(3) (2014) 685–708. Crossref, Medline, ISIGoogle Scholar
    • 29. Y. Fan, N. Batmanghelich, C. M. Clark and C. Davatzikos, Spatial patterns of brain atrophy in MCI patients, identified via high-dimensional pattern classification, predict subsequent cognitive decline, NeuroImage 39 (2008) 1731–1743. Crossref, Medline, ISIGoogle Scholar
    • 30. Y. Fan, S. M. Resnick, X. Wu and C. Davatzikos, Structural and functional biomarkers of prodromal Alzheimer’s disease: A high-dimensional pattern classification study, NeuroImage 41 (2008) 277–285. Crossref, Medline, ISIGoogle Scholar
    • 31. R. Filipovych and C. Davatzikos, Semi-supervised pattern classification of medical images: Application to mild cognitive impairment (MCI), Neuroimage 55(3) (2011) 1109–1119, Crossref, Medline, ISIGoogle Scholar
    • 32. K. J. Friston, J. Ashburner, S. J. Kiebel, T. E. Nichols and W. D. Penny, Statistical Parametric Mapping: The Analysis of Functional Brain Images (Academic Press, London, 2007). CrossrefGoogle Scholar
    • 33. S. Gauthier, B. Reisberg, M. Zaudig, R. C. Petersen, K. Ritchie and K. Broich, Mild cognitive impairment, Lancet 367 (2006) 1262–1270. Crossref, Medline, ISIGoogle Scholar
    • 34. S. Ghosh-Dastidar, H. Adeli and N. Dadmehr, Principal component analysis-enhanced cosine radial basis function neural network for robust epilepsy and seizure detection, IEEE Trans. Biomed. Eng. 55(2) (2008) 512–518. Crossref, Medline, ISIGoogle Scholar
    • 35. L. Harper, G. G. Fumagalli, F. Barkhof, P. Scheltens, J. T. O’Brien, F. Bouwman, E. J. Burton, J. D. Rohrer, N. C. Fox, G. R. Ridgway and J. M. Schott, MRI visual rating scales in the diagnosis of dementia: evaluation in 184 post-mortem confirmed cases, Brain 139 (2016) 1211–1225. Crossref, Medline, ISIGoogle Scholar
    • 36. S. Haller, K.-O. Lovblad, P. Giannakopoulos and D. Ville, Multivariate pattern recognition for diagnosis and prognosis in clinical neuroimaging: state of the art current challenges and future trends, Brain Topogr. 27 (2014) 329–337. Crossref, Medline, ISIGoogle Scholar
    • 37. A. Hyvarinen, Fast and robust fixed-point algorithms for independent component analysis. IEEE Trans. Neural Netw. 10 (1999) 626–634. Crossref, Medline, ISIGoogle Scholar
    • 38. I. A. Illán, J. M. Górriz, J. Ramírez, D. Salas-Gonzalez, M. M. López, F. Segovia, P. Padilla and C. G. Puntonet, Projecting independent components of SPECT images for computer aided diagnosis of Alzheimers disease, Pattern Recognit. Lett. 31 (2010) 1342–1347. Crossref, ISIGoogle Scholar
    • 39. G. Karas, P. Scheltens, S. Rombouts, P. Visser, R. van Schijndel, N. Fox and F. Barkhof, Global and local gray matter loss in mild cognitive impairment and Alzheimer’s disease, NeuroImage 23 (2004) 708–716. Crossref, Medline, ISIGoogle Scholar
    • 40. L. Khedher, J. Ramírez, J. M. Górriz and A. Brahim, Early diagnosis of Alzheimer’s disease based on partial least squares, principal component analysis and support vector machine using segmented MRI images, Neurocomputing 151 (2015) 139–150. Crossref, ISIGoogle Scholar
    • 41. L. Khedher, J. Ramírez, J. M. Górriz and A. Brahim, Automatic classification of segmented MRI data combining independent component analysis and support vector machines, in Innovation in Medicine and Healthcare—InMed, Lecture notes in IOS Press, Vol. 207 (Spring, Berlin, 2014). Google Scholar
    • 42. R. J. Killiany, et al., Use of structural magnetic resonance imaging to predict who will get alzheimer’s disease, Ann. Neurol 47(4) (2000) 430–439. Crossref, Medline, ISIGoogle Scholar
    • 43. S. Kloppel, C. M. Stonnington, C. Chu, B. Draganski, R. I. Scahill, J. D. Rohrer, N. C. Fox, C. R. Jack Jr, J. Ashburner and R. S. J. Frackowiak, Automatic classification of MR scans in Alzheimer’s disease, Brain 131 (2008) 681–689. Crossref, Medline, ISIGoogle Scholar
    • 44. Z. Lao, D. Shen, Z. Xue, B. Karacali, S. M. Resnick and C. Davatzikos, Morphological classification of brains via high-dimensional shape transformations and machine learning methods, NeuroImage 21 (2004) 46–57. Crossref, Medline, ISIGoogle Scholar
    • 45. E. Luders, C. Gaser, L. Jancke and G. A. Schlaug, Voxel-based approach to gray matter asymmetries, Neuroimage 22 (2004) 656–664. Crossref, Medline, ISIGoogle Scholar
    • 46. C. D. Meyer, Matrix Analysis and Applied Linear Algebra, (Society for Industrial and Applied Mathematics, Philadelphia, 2000). CrossrefGoogle Scholar
    • 47. S. McGee, Simplifying likelihood ratios. J. Gen. Intern. Med. 17 (2002) 646–649. Crossref, Medline, ISIGoogle Scholar
    • 48. C. Misra, Y. Fan and C. Davatzikos, 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 (2009) 1415–1422. Crossref, Medline, ISIGoogle Scholar
    • 49. J. C. Morris, Clinical dementia rating: a reliable and valid diagnostic and staging measure for dementia of the Alzheimer type, Int. Psychogeriatr. 9 (1997) 173–176. Crossref, MedlineGoogle Scholar
    • 50. L. Mosconi, Brain glucose metabolism in the early and specific diagnosis of Alzheimer’s disease. FDG-PET studies in MCI and AD, Eur. J. Nucl. Med. Mol. Imag. 32 (2005) 486–510. Crossref, Medline, ISIGoogle Scholar
    • 51. L. Mosconi, W. H. Tsui, K. Herholz, A. Pupi, A. Drzezga, G. Lucignani, E. M. Reiman, V. Holthoff, E. Kalbe, S. Sorbi, J. Diehl-Schmid, R. Perneczky, F. Clerici, R. Caselli, B. Beuthien-Baumann, A. Kurz and S. Minoshima, Multicenter standardized 18F-FDG PET diagnosis of mild cognitive impairment, Alzheimer’s disease, and other dementias, J. Nucl. Med. 49 (2008) 390–398. Crossref, Medline, ISIGoogle Scholar
    • 52. F. C. Morabito, M. Campolo, D. Labate, G. Morabito, L. Bonanno, A. Bramanti, S. de Salvo, A. Marra and P. Bramanti, A longitudinal EEG study of Alzheimer’s disease progression based on a complex network approach, Int. J. Neural Syst. 25(2) (2015) 1550005. Link, ISIGoogle Scholar
    • 53. G. McKhann, D. Drachman and M. Folstein, Clinical diagnosis of Alzheimer’s disease: report of the NINCDS-ADRDA work group under the auspices of department of health and human services task force on Alzheimer’s disease, Neurology 34 (1984) 939–944. Crossref, Medline, ISIGoogle Scholar
    • 54. E. Oja, A fast fixed-point algorithm for independent component analysis, Neural Comput. 9 (1997) 1483–1492. Crossref, ISIGoogle Scholar
    • 55. G. Orru, W. Pettersson-Yeo, A. F. Marquand, G. Sartori and A. Mechelli, Using support vector machine to identify imaging biomarkers of neurological and psychiatric disease: A critical review, Neurosci. Biobehav. Rev. 36 (2012) 1140–1152. Crossref, Medline, ISIGoogle Scholar
    • 56. E. Osuna, R. Freund and F. Girosit, Training support vector machines: an application to face detection, in Proc. 1997, IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, 17–19 June 1997, San Juan, pp. 130–136. Google Scholar
    • 57. G. Perez, A. Conci, A. B. Moreno and J. A. Hernandez-Tamames, Rician noise attenuation in the wavelet packet transformed domain for brain MRI, Integr. Comput.-Aided Eng. 21(2) (2014) 163–175. Crossref, ISIGoogle Scholar
    • 58. M. Pievani, M. Bocchetta, M. Boccardi, E. Cavedo, M. Bonetti, P. M. Thompson and G. B. Frisoni, Striatal morphology in early-onset and late-onset alzheimers disease: a preliminary study, Neurobiol. Aging 34 (2013) 1728–1739. Crossref, Medline, ISIGoogle Scholar
    • 59. Psychiatry SBMGD, Vbm toolboxes, University of Jena, (2013), URL Google Scholar
    • 60. J. Ramírez, P. Yélamos, J. M. Górriz, C. G. Puntonet and J. C. Segura, SVM-enabled voice activity detection, Comput. Sci. 3972 (2006) 676–681. ISIGoogle Scholar
    • 61. A. Retico, P. Bosco, P. Cerello, E. Fiorina, A. Chincarini and M. E. Fantacci, Predictive models based on support vector machines: whole-brain versus regional analysis of structural MRI in the Alzheimer’s disease, J. Neuroimaging 25 (2015) 552–563 Crossref, Medline, ISIGoogle Scholar
    • 62. M. R. Sabuncu and E. Konukoglu, Clinical prediction from structural brain MRI scans: A large-scale empirical study, Neuroinformatics 13 (2014) 31–46 Crossref, ISIGoogle Scholar
    • 63. Z. Sankari and H. Adeli, Probabilistic neural networks for EEG-based diagnosis of Alzheimers disease using conventional and wavelet coherence, J. Neurosci. Methods 197(1) (2011) 165–170. Crossref, Medline, ISIGoogle Scholar
    • 64. Z. Sankari, H. Adeli and A. Adeli, Intrahemispheric, interhemispheric and distal EEG coherence in Alzheimers disease, Clin. Neurophysiol. 122(5) (2011) 897–906. Crossref, Medline, ISIGoogle Scholar
    • 65. Z. Sankari, H. Adeli and A. Adeli, Wavelet coherence model for diagnosis of Alzheimers disease, Clin. EEG Neurosci. 43(3) (2012) 268–278. Crossref, Medline, ISIGoogle Scholar
    • 66. F. Segovia, J. M. Górriz, J. Ramírez, D. Salas-Gonzalez and I. Álvarez, A comparative study of the feature extraction methods for the diagnosis of Alzheimer’s disease using the adni database, Neurocomputing 75 (2012) 64–71. Crossref, ISIGoogle Scholar
    • 67. L. Sirovich and M. Meytlis, Symmetry, probability, and recognition in face space, Proc. Natl. Acad. Sci. 106(17) (2009) 6895–6899, Crossref, Medline, ISIGoogle Scholar
    • 68. V. N. Vapnik, Statistical Learning Theory (John Wiley and Sons, Inc., New York, 1998). Google Scholar
    • 69. A. A. Willette, V. D. Calhoun, J. M. Egan and D. Kapogiannis, Prognostic classification of mild cognitive impairment and Alzheime’s disease: MRI independent component analysis, Psychiat. Res.-Neuroimag. 224 (2014) 81–88 Crossref, Medline, ISIGoogle Scholar
    • 70. L. Xu, G. Pearlson and V. D. Calhoun, Joint source based morphometry identifies linked gray and white matter group differences, Neuroimage 44 (2009) 777–789. Crossref, Medline, ISIGoogle Scholar
    • 71. W. Yang et al., Independent component analysis-based classification of Alzheimer’s MRI data, Neuroimage 24(4) (2011) 775–783. Google Scholar
    • 72. Y. Zhang and W. Zhou, Multifractal analysis and relevance vector machine-based automatic seizure detection in intracranial, Int. J. Neural Syst. 25(6) (2015) 1550020 (14 pages). Link, ISIGoogle Scholar
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