Bispectrum-based Cross-frequency Functional Connectivity: Classification of Alzheimer's disease

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Bispectrum-based Cross-frequency Functional Connectivity: Classification of Alzheimer's disease
Title:
Bispectrum-based Cross-frequency Functional Connectivity: Classification of Alzheimer's disease
Journal Title:
2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
Keywords:
Publication Date:
08 September 2022
Citation:
Klepl, D., He, F., Min, W., Blackburn, D., & Sarrigiannis, P. (2022). Bispectrum-based Cross-frequency Functional Connectivity: Classification of Alzheimer’s disease. 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). https://doi.org/10.1109/embc48229.2022.9871366
Abstract:
Alzheimer’s disease (AD) is a neurodegenerative disease known to affect brain functional connectivity (FC). Linear FC measures have been applied to study the differences in AD by splitting neurophysiological signals such as electroencephalography (EEG) recordings into discrete frequency bands and analysing them in isolation. We address this limitation by quantifying cross-frequency FC in addition to the traditional within-band approach. Cross-bispectrum, a higher-order spectral analysis, is used to measure the nonlinear FC and is compared with the cross-spectrum, which only measures the linear FC within bands. Each frequency coupling is then used to construct a FC network, which is in turn vectorised and used to train a classifier. We show that fusing features from networks improves classification accuracy. Although both within-frequency and cross-frequency networks can be used to predict AD with high accuracy, our results show that bispectrum-based FC outperforms cross-spectrum suggesting an important role of cross-frequency FC.
License type:
Publisher Copyright
Funding Info:
There was no specific funding for the research done
Description:
© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
ISSN:
2694-0604
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