The DOI matches its Crossref record; 42 of the 42 references it cites were found in a registry or an index.
Deep learning of resting-state electroencephalogram signals for three-class classification of Alzheimer’s disease, mild cognitive impairment and healthy ageing
Open accessAbstract
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 (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 Spatial–temporal 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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Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562
McKhann, G. M., Knopman, D. S., Chertkow, H., Hyman, B. T., Jack, C. R., Kawas, C. H., Klunk, W. E., Koroshetz, W. J., Manly, J. J., Mayeux, R., et al. (2011) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2011.03.005 · Cited by 18,657
NIA‐AA Research Framework: Toward a biological definition of Alzheimer's disease
Jack, C. R., Bennett, D. A., Blennow, K., Carrillo, M. C., Dunn, B., Haeberlein, S. B., Holtzman, D. M., Jagust, W., Jessen, F., Karlawish, J., et al. (2018) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2018.02.018 · Cited by 10,537
Albert, M. S., DeKosky, S. T., Dickson, D., Dubois, B., Feldman, H. H., Fox, N. C., Gamst, A., Holtzman, D. M., Jagust, W. J., Petersen, R. C., et al. (2011) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2011.03.008 · Cited by 10,152
An overview of gradient descent optimization algorithms
Ruder, S. (2016) · arXiv · doi:10.48550/arxiv.1609.04747 · Cited by 4,962
The neuropathological diagnosis of Alzheimer's disease
DeTure, M. A., & Dickson, D. W. (2019) · Molecular Neurodegeneration · doi:10.1186/s13024-019-0333-5 · Cited by 3,329 · cited 2×
“At present, the only definitive way to diagnose AD is via post-mortem examination to find the plaques or tangles within the brain (DeTure and Dickson 2019).”
A Wavelet Tour of Signal Processing : The Sparse Way
Mallat, S., & Peyré, G. (2008) · Cited by 2,759
A Review on Evaluation Metrics for Data Classification Evaluations
M, H., & M.N, S. (2015) · International Journal of Data Mining & Knowledge Management Process · doi:10.5121/ijdkp.2015.5201 · Cited by 2,693
Nichols, E., Szoeke, C. E. I., Vollset, S. E., Abbasi, N., Abd-Allah, F., Abdela, J., Aichour, M. T. E., Akinyemi, R. O., Alahdab, F., Asgedom, S. W., et al. (2019) · The Lancet Neurology · doi:10.1016/s1474-4422(18)30403-4 · Cited by 2,412
Research works printed but not linked: 1
- Krizhevsky, 2012, ImageNet classification with deep convolutional, Adv. Neural Inf. Process Syst., 25, 1
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Alvi, A. M., Siuly, S., & Wang, H. (2022) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2022.3186180 · Cited by 115
Primate brain pattern-based automated Alzheimer's disease detection model using EEG signals
Doğan, Ş., Bayğın, M., Taşçı, B., Loh, H. W., Barua, P. D., Tuncer, T., Tan, R., & Acharya, U. R. (2022) · Cognitive Neurodynamics · doi:10.1007/s11571-022-09859-2 · Cited by 103
Nour, M., Şentürk, Ü., & Polat, K. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.105751 · Cited by 93
Adazd-Net: Automated adaptive and explainable Alzheimer’s disease detection system using EEG signals
Khare, S. K., & Acharya, U. R. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.110858 · Cited by 88
Data leakage in deep learning studies of translational EEG
Brookshire, G., Kasper, J., Blauch, N. M., Wu, Y. C., Glatt, R., Merrill, D. A., Gerrol, S., Yoder, K. J., Quirk, C., & Lucero, C. (2024) · Frontiers in Neuroscience · doi:10.3389/fnins.2024.1373515 · Cited by 103
Metadata
- 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
- Physical Sciences › Computer Science › Artificial Intelligence › 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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