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 an 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.
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 an 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.
| Original language | English |
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| Title of host publication | 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) |
| Publisher | IEEE |
| Pages | 305-308 |
| Number of pages | 4 |
| ISBN (Electronic) | 978-1-7281-2782-8 |
| ISBN (Print) | 978-1-7281-2783-5 |
| DOIs | |
| Publication status | Published - 8 Sept 2022 |
| Event | 44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. - Glasgow, United Kingdom Duration: 11 Jul 2022 → 15 Jul 2022 |
Publication series
| Name | Annual International Conference of the IEEE Engineering in Medicine and Biology Society |
|---|---|
| Publisher | IEEE |
| ISSN (Electronic) | 2694-0604 |
Conference
| Conference | 44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. |
|---|---|
| Country/Territory | United Kingdom |
| City | Glasgow |
| Period | 11/07/22 → 15/07/22 |
Bibliographical note
Funding Information:*The EEG data was funded by a grant from the Alzheimer’s Research UK (ARUK-PPG20114B-25). The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health. 1 Dominik Klepl ([email protected]) and Fei He are with Centre for Computational Science and Mathematical Modelling, Coventry University, Coventry CV1 2JH, UK 2Wu Min is with Institute for Infocomm Research, A*STAR, Singapore. 3Daniel J. Blackburn is with Department of Neuroscience, University of Sheffield, Sheffield, S10 2HQ, UK 4Ptolemaios Sarrigiannis is with Department of Neurophysiology, Royal Devon and Exeter NHS Foundation Trust, Exeter, EX2 5DW, UK
Publisher Copyright:
© 2022 IEEE.
Keywords
- Couplings
- Electroencephalography
- Biology
- Frequency measurement
- Recording
- Alzheimer's disease
- Spectral analysis