Deep learning of resting-state electroencephalogram signals for three-class classification of Alzheimer’s disease, mild cognitive impairment and healthy ageing
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Objective. This study aimed to produce a novel deep learning (DL) model for the classification of subjects with Alzheimer’s disease (AD), mild cognitive impairment (MCI) subjects and healthy ageing (HA) subjects using resting-state scalp electroencephalogram (EEG) signals. Approach. The raw EEG data were pre-processed to remove unwanted artefacts and sources of noise. The data were then processed with the continuous wavelet transform, using the Morse mother wavelet, to create time-frequency graphs with a wavelet coefficient scale range of 0–600. The graphs were combined into tiled topographical maps governed by the 10–20 system orientation for scalp electrodes. The application of this processing pipeline was used on a data set of resting-state EEG samples from age-matched groups of 52 AD subjects (82.3 ± 4.7 years of age), 37 MCI subjects (78.4 ± 5.1 years of age) and 52 HA subjects (79.6 ± 6.0 years of age). This resulted in the formation of a data set of 16197 topographical images. This image data set was then split into training, validation and test images and used as input to an AlexNet DL model. This model was comprised of five hidden convolutional layers and optimised for various parameters such as learning rate, learning rate schedule, optimiser, and batch size. Main results. The performance was assessed by a tenfold cross-validation strategy, which produced an average accuracy result of 98.9 ± 0.4% for the three-class classification of AD vs MCI vs HA. The results showed minimal overfitting and bias between classes, further indicating the strength of the model produced. Significance. These results provide significant improvement for this classification task compared to previous studies in this field and suggest that DL could contribute to the diagnosis of AD from EEG recordings.
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The above CNN applications on AD classification focus more on learning locally and continuously changed multiscaled features on the Euclidean space from EEG signals, neglecting functional connectivity features.
“The above CNN applications on AD classification (Bi & Wang, 2019; Deepthi et al., 2020; Huggins et al., 2021; Ieracitano et al., 2019; Li, Wang, et al., 2021) focus more on learning the locally and continuously changed multiscaled features on the Euclidean space from the EEG signals, neglecting the functional connectivity features.”
From <scp>Spatial–temporal</scp> graph convolutional network for Alzheimer classification based on brain functional connectivity imaging of electroencephalogram (2022) · cites this paper as Huggins et al., 2021
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- Authors
- Huggins, C. J., Escudero, J., Parra, M. A., Scally, B., Anghinah, R., Vitória Lacerda De Araújo, A., Basile, L. F., & Abasolo, D.
- Affiliations
- University of Surrey (GB)
- University of Edinburgh (GB)
- University of Strathclyde (GB)
- University of Leeds (GB)
- Universidade de São Paulo (BR)
- Corresponding author
- Huggins, Cameron J
- ORCID
- Venue
- Journal of Neural Engineering
- Publisher
- IOP Publishing
- Published
- 2021-06-17
- Volume
- 18
- Issue
- 4
- Pages
- 046087
- ISSN
- 1741-2552 (ISSN-L)
- 1741-2560
- OpenAlex
- W3165302245
- Cited by
- 105
- References
- 42
- Citations by year
- 2026: 23 · 2025: 28 · 2024: 14 · 2023: 24 · 2022: 14 · 2021: 2
- FWCI
- 7.11
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of Neural Engineering (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Open Research (University of Surrey) (acceptedVersion, other-oa)
- Edinburgh Research Explorer (University of Edinburgh) (submittedVersion, cc-by)
- Strathprints: The University of Strathclyde institutional repository (University of Strathclyde) (submittedVersion)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Keywords
- Alzheimer's disease, mild cognitive impairment, healthy aging, resting-state EEG, deep learning classification, AlexNet, continuous wavelet transform, EEG artifact removal
- MeSH terms
- Healthy Aging, Deep Learning, Adult, Aged, Aged, 80 and over, Alzheimer Disease, Electroencephalography, Humans, Middle Aged, Cognitive Dysfunction
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