The DOI matches its Crossref record; 44 of the 45 references it cites were found in a registry or an index.
Recurrent neural network-based approach for early recognition of Alzheimer's disease in EEG
Abstract
Objective We explored the ability of specifically designed and trained recurrent neural networks (RNNs), combined with wavelet preprocessing, to discriminate between the electroencephalograms (EEGs) of patients with mild Alzheimer's disease (AD) and their age-matched control subjects. Methods Twomin recordings of resting eyes-closed continuous EEGs (as well as their wavelet-filtered subbands) obtained from parieto-occipital channels of 10 early AD patients and 10 healthy controls were input into RNNs for training and testing purposes. The RNNs were chosen because they can implement extremely non-linear decision boundaries and possess memory of the state, which is crucial for the considered task. Results The best training/testing results were achieved using a 3-layer RNN on left parietal channel level 4 high-pass wavelet subbands. When trained on 3 AD and 3 control recordings, the resulting RNN tested well on all remaining controls and 5 out of 7 AD patients. This represented a significantly better than chance performance of about 80% sensitivity at 100% specificity. Conclusion The suggested combined wavelet/RNN approach may be useful in analyzing long-term continuous EEGs for early recognition of AD. This approach should be extended on larger patient populations before its clinical diagnostic value can be established. Further lines of investigation might also require that EEGs be recorded from patients engaged in certain mental (cognitive) activities.
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“In addition, EEG can be used in the differential diagnosis between AD and other diseases leading to dementia, such as vascular brain damage [20,21] and Lewy body dementia [22,23,24,25,26].”
From Alzheimer's Disease and Frontotemporal Dementia: A Robust Classification Method of EEG Signals and a Comparison of Validation Methods (2021) · cites this paper as [22]
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Metadata
- Authors
- Petrosian, A. A., Prokhorov, D. V., Lajara-Nanson, W., & Schiffer, R. B.
- Affiliations
- Texas Tech University (US)
- Ford Motor Company (United States) (US)
- Texas Tech University Health Sciences Center (US)
- Corresponding author
- Petrosian, A.A.
- ORCID
- Prokhorov, D.V.
- Venue
- Clinical Neurophysiology
- Publisher
- Elsevier BV
- Published
- 2001-08
- Volume
- 112
- Issue
- 8
- Pages
- 1378-1387
- ISSN
- 1388-2457 (ISSN-L)
- OpenAlex
- W1997792515
- Cited by
- 126
- References
- 45
- Citations by year
- 2026: 2 · 2025: 3 · 2024: 5 · 2023: 4 · 2022: 6 · 2021: 7 · 2020: 7 · 2019: 6 · 2018: 8 · 2017: 5 · 2016: 5 · 2015: 4 · 2014: 4 · 2013: 6 · 2012: 2
- FWCI
- 1.40
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Clinical Neurophysiology (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Else Kröner-Fresenius-Stiftung
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Physical Sciences › Computer Science › Artificial Intelligence › Emotion and Mood Recognition
- Keywords
- Alzheimer's disease, early diagnosis, EEG, recurrent neural networks, wavelet filtering, EEG classification, resting-state EEG
- MeSH terms
- Aged, Alzheimer Disease, Diagnosis, Differential, Electroencephalography, Female, Humans, Male, Nerve Net, Predictive Value of Tests
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