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Feature Extraction and Identification of Alzheimer’s Disease based on Latent Factor of Multi-Channel EEG
Open accessAbstract
Alzheimer's disease is a neurodegenerative disease in old age, early diagnosis will help to delay the progression of the disease. Presently, the features of brain functional diseases can be obtained with EEG analysis, but the relationship between characteristics of EEG and Alzheimer's disease has not been clearly clarified. In this work, we hypothesize that there exist default brain variables (latent factors) across subjects in disease processes, decoding latent factor from brain activity contributes to the study of cognitive impairment. To that end, this work proposes to extract characteristics of Alzheimer's disease by combing latent factors of EEG with variational auto-encoder to realize disease identification. Primarily, power spectrum characteristics is investigated and it is found that the dominant frequency of two groups is different. Further analysis reveals that latent factor distribution of Alzheimer's disease exists obvious differences with normal group in the theta frequency band. Moreover, the latent factors are projected onto the three-dimensional state space and the transient rotation of neural state is found, which shows the dynamic characteristics of latent factors. In addition, Takagi-Sugeno-Kang classifier is adopted and multiple latent factors are fed into Takagi-Sugeno-Kang classifier for decoding. Compared with linear classifier, Takagi-Sugeno-Kang fuzzy classifier has better performance in classification of energy feature from sub-frequency bands of latent factors. The accuracy of identification could up to 98.10% when the combination of energy features of four frequency bands is used as model input. Collectively, this work provides a feasible tool for identification of neurological dysfunction from the view of latent factors, especially contributing to the diagnosis of Alzheimer's disease.
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“In some studies on brain disorders based on GCN (Chen et al., 2020; Li, Liu, et al., 2021; Zeng et al., 2020; Zhang et al., 2021; Zhao et al., 2021), the relationship of different channels of EEG is short of effective prior guidance and the adjacency matrix cannot ensure the utilisation of the coupling information between each channel.”
From Spatial–temporal graph convolutional network for Alzheimer classification based on brain functional connectivity imaging of electroencephalogram (2022) · cites this paper as Li, Liu, et al., 2021
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“Traditional statistical functional correlation measures include Pearson correlation (PC) (Chen et al., 2020; Zhao et al., 2021), Tanh nonlinearity (Li, Liu, et al., 2021), average correlation coefficients (Zeng et al., 2020), and covariance (Zhang et al., 2021), which cannot fully represent the complex brain connectivity.”
From Spatial–temporal graph convolutional network for Alzheimer classification based on brain functional connectivity imaging of electroencephalogram (2022) · cites this paper as Li, Liu, et al., 2021
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Strategic roadmap for an early diagnosis of Alzheimer's disease based on biomarkers
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Cited by
Sorted by citations
Shan, X., Cao, J., Huo, S., Chen, L., Sarrigiannis, P. G., & Zhao, Y. (2022) · Human Brain Mapping · doi:10.1002/hbm.25994 · Cited by 75
“In some studies on brain disorders based on GCN (Chen et al., 2020; Li, Liu, et al., 2021; Zeng et al., 2020; Zhang et al., 2021; Zhao et al., 2021), the relationship of different channels of EEG is short of effective prior guidance and the adjacency matrix cannot ensure the utilisation of the…”
BackgroundMethodInfluential
Metadata
- Authors
- Li, K., Wang, J., Li, S., Yu, H., Zhu, L., Liu, J., & Wu, L.
- Affiliations
- Tianjin University (CN)
- Tangshan Gongren Hospital (CN)
- Venue
- IEEE Transactions on Neural Systems and Rehabilitation Engineering
- Published
- 2021
- Volume
- 29
- Pages
- 1557-1567
- ISSN
- 1534-4320 (ISSN-L)
- 1558-0210
- OpenAlex
- W3186320158
- Cited by
- 61
- References
- 1
- Citations by year
- 2026: 9 · 2025: 15 · 2024: 11 · 2023: 19 · 2022: 6 · 2021: 1
- FWCI
- 4.84
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- IEEE Transactions on Neural Systems and Rehabilitation Engineering (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Funders
- National Natural Science Foundation of China (62071324)
- Natural Science Foundation of Tianjin (19JCYBJC18800)
- 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, multichannel EEG, latent variables, variational autoencoder, disease identification, theta band, power spectrum, fuzzy classifier, frequency band energy features, early diagnosis
- MeSH terms
- Alzheimer Disease, Brain, Electroencephalography, Humans, Neurodegenerative Diseases, Cognitive Dysfunction
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- Li, K., Wang, J., Li, S., Yu, H., Zhu, L., Liu, J., & Wu, L. (2021). Feature Extraction and Identification of Alzheimer’s Disease based on Latent Factor of Multi-Channel EEG. IEEE Transactions on Neural Systems and Rehabilitation Engineering. https://doi.org/10.1109/tnsre.2021.3101240
