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Exploring multifractal-based features for mild Alzheimer's disease classification

Journal Article


Abstract


  • Purpose

    Multifractal applications to resting state functional MRI (rs-fMRI) time series for diagnosing Alzheimer's disease (AD) are still limited. We aim to address two issues: (I) if and what multifractal features are sufficiently discriminative to detect AD from the healthy; (II) if AD classification could be further improved by combining multifractal features with traditional features in this field.

    Methods

    Rs-fMRI data of 25 AD patients and 38 normal controls were analyzed. A set of multifractal features were systematically investigated. Traditional features in monofractal, linear, and network-based categories were also extracted for comparison and combination. Both support vector machines and multiple kernel learning (MKL) were used to perform classification with individual and combined features.

    Results

    We identified a multifractal feature, inline image, which has the strongest discriminative power among all the features in our study. Moreover, we found that the classification accuracy could be significantly improved from 69% (by inline image only) to up to 76%, when nonsparse MKL is used to combine inline image with the monofractal feature, Hurst. Finally, we showed that incorporating other multifractal features, inline image, inline image and inline image, could also improve traditional-feature-based AD classification.

    Conclusion

    Our work demonstrated the potential usefulness of multifractal analysis for AD research, especially when combining with the traditional rs-fMRI features. It contributes to distinguishing AD from NC subjects.

Authors


Publication Date


  • 2016

Citation


  • Ni, H., Zhou, L., Ning, X. & Wang, L. (2016). Exploring multifractal-based features for mild Alzheimer's disease classification. Magnetic Resonance in Medicine, 76 (1), 259-269.

Scopus Eid


  • 2-s2.0-84974846236

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/5829

Has Global Citation Frequency


Number Of Pages


  • 10

Start Page


  • 259

End Page


  • 269

Volume


  • 76

Issue


  • 1

Place Of Publication


  • United States

Abstract


  • Purpose

    Multifractal applications to resting state functional MRI (rs-fMRI) time series for diagnosing Alzheimer's disease (AD) are still limited. We aim to address two issues: (I) if and what multifractal features are sufficiently discriminative to detect AD from the healthy; (II) if AD classification could be further improved by combining multifractal features with traditional features in this field.

    Methods

    Rs-fMRI data of 25 AD patients and 38 normal controls were analyzed. A set of multifractal features were systematically investigated. Traditional features in monofractal, linear, and network-based categories were also extracted for comparison and combination. Both support vector machines and multiple kernel learning (MKL) were used to perform classification with individual and combined features.

    Results

    We identified a multifractal feature, inline image, which has the strongest discriminative power among all the features in our study. Moreover, we found that the classification accuracy could be significantly improved from 69% (by inline image only) to up to 76%, when nonsparse MKL is used to combine inline image with the monofractal feature, Hurst. Finally, we showed that incorporating other multifractal features, inline image, inline image and inline image, could also improve traditional-feature-based AD classification.

    Conclusion

    Our work demonstrated the potential usefulness of multifractal analysis for AD research, especially when combining with the traditional rs-fMRI features. It contributes to distinguishing AD from NC subjects.

Authors


Publication Date


  • 2016

Citation


  • Ni, H., Zhou, L., Ning, X. & Wang, L. (2016). Exploring multifractal-based features for mild Alzheimer's disease classification. Magnetic Resonance in Medicine, 76 (1), 259-269.

Scopus Eid


  • 2-s2.0-84974846236

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/5829

Has Global Citation Frequency


Number Of Pages


  • 10

Start Page


  • 259

End Page


  • 269

Volume


  • 76

Issue


  • 1

Place Of Publication


  • United States