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Prediction of Conversion to Alzheimer’s Disease Using 3D-DWT and PCA

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IoT Technologies for Health Care (HealthyIoT 2021)

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Abstract

Alzheimer’ Disease (AD) is the most common form of dementia worldwide. Structural Magnetic Resonance Imaging (sMRI) is the supportive tool for the diagnosis of this disease. Even, it can be used to predict the conversion of the disease from the mild cognitive impairment (MCI) to AD stage. Nevertheless, the 3D image produced by sMRI is high dimensional data, which raises the risk of overfitting in the classification model. For this reason, the combination of Discrete Wavelet Transform (DWT) and Principal Component Analysis (PCA) was proposed as the feature extraction techniques to reduce the dimensional and extract significant features concurrently. The issues of DWT are the selection of level of decomposition and wavelet filter to decompose the image. In order to deal with these issues, a series of experiments were conducted to find the suitable parameters. By using 2D-DWT, spatial information of 3D data cannot be captured. The connection between the slices is neglected. Hence, 3D-DWT has been adopted instead of 2D-DWT in this paper. In the classification step, Support Vector Machine (SVM) was used as the classifier to predict the conversion of normal control (NC) and stable MCI (SMCI) to progressive MCI (PMCI) and AD for datasets collected up to 2 years before the progression. The dataset used in this paper was collected from Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. In the validation, the proposed method outperformed the other methods by attaining 79%, 79%, 82% and 82% in accuracy for the datasets collected at different time points, which were 1% to 4% higher than the model adopted 2D-DWT and PCA.

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Acknowledgements

The authors would like to thank Universiti Teknologi Malaysia (UTM) for supporting this research. The authors also acknowledge that the data used in this study was obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). 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: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf.

The data was funded by ADNI (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12–2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-la Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the Study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.

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Authors and Affiliations

  1. School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, 81310, Johor Bahru, Johor, Malaysia

    Li Yew Aow Yong & Mohd Shafry Mohd Rahim

  2. Faculty of Computing and Information Technology, Tunku Abdul Rahman University College, Kuala Lumpur, Malaysia

    Chi Wee Tan

Authors
  1. Li Yew Aow Yong
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  2. Mohd Shafry Mohd Rahim
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  3. Chi Wee Tan
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Correspondence to Li Yew Aow Yong .

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Editors and Affiliations

  1. Marche Polytechnic University, Ancona, Italy

    Susanna Spinsante

  2. University of Lisbon, Lisbon, Portugal

    Bruno Silva

  3. New Bulgarian University, Sofia, Bulgaria

    Rossitza Goleva

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© 2022 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Aow Yong, L.Y., Mohd Rahim, M.S., Tan, C.W. (2022). Prediction of Conversion to Alzheimer’s Disease Using 3D-DWT and PCA. In: Spinsante, S., Silva, B., Goleva, R. (eds) IoT Technologies for Health Care. HealthyIoT 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 432. Springer, Cham. https://doi.org/10.1007/978-3-030-99197-5_16

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  • DOI: https://doi.org/10.1007/978-3-030-99197-5_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-99196-8

  • Online ISBN: 978-3-030-99197-5

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