Abstract
The usual computer aided diagnosis approaches of Alzheimer’s disease patients based on fMRI often require a lot of manual intervention. By contrast, H-ELM needs only less manual intervention and can extract features by a multi-layer feature representation framework. Therefore, an AD CADx model based on H-ELM is proposed. First, the common spatial pattern is used to extract information from the BOLD signals, and then the features are encoded and trained by H-ELM. H-ELM is used to realize the expression of deep feature of the brain, so as to further improve the diagnostic accuracy. Finally, experimental evaluation proved the effectiveness of the proposed algorithms.
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References
Cass, S.P.: Alzheimer’s disease and exercise: a literature review. Curr. Sport. Med. Rep. 16(1), 19–22 (2017)
Bassiony, M.M., Lyketsos, C.G.: Delusions and hallucinations in alzheimer’s disease: review of the brain decade. Psychosomatics 44(5), 388–401 (2003)
Rathore, S., Habes, M., Iftikhar, M.A., et al.: A review on neuroimaging-based classification studies and associated feature extraction methods for alzheimer’s disease and its prodromal stages. Neuroimage 155, 530–548 (2017)
Logothetis, N.K.: What we can do and what we cannot do with fMRI. Nature 453(7197), 869–878 (2008)
Hennig, J., Speck, O., Koch, M.A., et al.: Functional magnetic resonance imaging: a review of methodological aspects and clinical applications. J. Magn. Reson. Imaging 18(1), 1–15 (2003)
Grossman, M., Peelle, J.E., Smith, E.E., et al.: Category-specific semantic memory: converging evidence from bold fmri and alzheimer’s disease. Neuroimage 68, 263–274 (2013)
Galvin, J.E., Price, J.L., Yan, Z., et al.: Resting bold fMRI differentiates dementia with lewy bodies vs alzheimer disease. Alzheimers Dement. J. Alzheimers Assoc. 7(4), e69–e69 (2011)
Cantin, S., Villien, M., Moreaud, O., et al.: Impaired cerebral vasoreactivity to CO2 in alzheimer’s disease using BOLD fMRI. Neuroimage 58(2), 579–587 (2011)
Liu, S., Liu, S., Cai, W., et al.: Early diagnosis of alzheimer’s disease with deep fearning. In: IEEE 11th International Symposium on Biomedical Imaging, pp. 1015–1018. IEEE Press, Beijing (2014)
Sarraf, S., Tofighi, G.: Deep Learning-based pipeline to recognize alzheimer’s disease using fMRI data. In: Proceedings of 2016 Future Technologies Conference, pp. 816–820. IEEE Press, San Francisco, CA (2017)
Tang, J., Deng, C., Huang, G.B.: Extreme learning machine for multilayer perceptron. IEEE Trans. Neural Netw. Learn. Syst 4, 809–821 (2017)
Zhang, Y., Wang, Y., Zhou, G., et al.: Multi-kernel extreme learning machine for EEG classification in brain-computer interfaces. Expert Syst. Appl. 96, 302–310 (2018)
Yan, C.G., Zane, Y.F.: DPARSF: a MATLAB toolbox for ‘pipeline’ data analysis of resting-state fMRI. Front. Syst. Neurosci. 4(13), 13 (2010)
Eickhoff, S.B., et al.: A new SPM toolbox for combining probabilistic Cytoarchitectonic maps and functional imaging data. Neuroimage 25(4), 1325C–1335C (2005)
Tzouriomazoyer, N., Landeau, B., Papathanassiou, D., et al.: Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage 15(1), 273–289 (2002)
Khazaee, A., Ebrahimzadeh, A., Babajaniferemi, A.: Classification of patients with MCI And AD from healthy controls using directed graph measures of resting-state fMRI. Behav. Brain Res. 322(PtB), 339–350 (2017)
Acknowledgments
This research was partially supported by the National Natural Science Foundation of China (Nos. 61472069, 61402089 and U1401256), the Fundamental Research Funds for the Central Universities (Nos. N161602003, N171607010, N161904001, and N160601001), the Natural Science Foundation of Liaoning Province (No. 2015020553).
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Wang, Z., Xin, J., Zhao, Y., Guo, Q. (2020). Alzheimer’s Disease Computer Aided Diagnosis Based on Hierarchical Extreme Learning Machine. In: Cao, J., Vong, C., Miche, Y., Lendasse, A. (eds) Proceedings of ELM 2018. ELM 2018. Proceedings in Adaptation, Learning and Optimization, vol 11. Springer, Cham. https://doi.org/10.1007/978-3-030-23307-5_5
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DOI: https://doi.org/10.1007/978-3-030-23307-5_5
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