Principal Dynamic Mode Analysis of EEG Data for Assisting the Diagnosis of Alzheimer's Disease
- Submitting institution
-
University of Plymouth
- Unit of assessment
- 12 - Engineering
- Output identifier
- 108
- Type
- D - Journal article
- DOI
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10.1109/jtehm.2015.2401005
- Title of journal
- IEEE Journal of Translational Engineering in Health and Medicine
- Article number
- 1800110
- First page
- -
- Volume
- 3
- Issue
- -
- ISSN
- 2168-2372
- Open access status
- Out of scope for open access requirements
- Month of publication
- -
- Year of publication
- 2015
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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4
- Research group(s)
-
-
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- With the increase in life expectancy, more people are developing Alzheimer's disease (AD) and other forms of dementia. As a result there is a need for an objective, non-invasive and affordable means to detect and monitor the disease. The paper is significant as provides a novel perspective for the use of an electroencephalogram (EEG) for dementia assessment and a new way to generate reliable indices to characterize the disease. The approach enables the estimation of reliable linear or nonlinear dynamic models, with much reduced parameters compared to existing methods.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -