Detection of Early Stage Alzheimer’s Disease using EEG Relative Power with Deep Neural Network
Abstract
Electroencephalogram (EEG) signal based early diagnosis of Alzheimer's Disease (AD), especially a discrimination between healthy control (HC) and mild cognitive impairment (MCI) has received remarkable attentions to complement conventional diagnosing methods in clinical fields. A relative power (RP) metric which quantifies the abnormal EEG pattern 'slowing' has widely been used as a major feature to distinguish HC and MCI, however, the optimal spectral ranges of the RP are influenced by the given dataset. In this study, we proposed the deep neural network based classifier using the RP to fully exploit and recombine the features through its own learning structure. The DNN enhanced the diagnosis results compared to shallow neural network, and enabled to interpret the results as we used the wellknown RP features as the domain knowledge. We investigated and explored the potentials of DNN based detection of the earlystage AD.
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Kim et al. achieved 0.75 accuracy on a private dataset using EEG segment with respect to RP.
“| Kim et al. [92] | EEG segment with respect to RP(Absolute power of EEG signals in three different frequency bands) | EEG | Private dataset | 0.75 (ACC) |”
From A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis (2024) · cites this paper as [92]
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- Authors
- Kim, D., & Kim, K.
- Affiliations
- Gwangju Institute of Science and Technology (KR)
- Venue
- 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
- Publisher
- IEEE
- Published
- 2018-07
- Volume
- 2018
- Pages
- 352-355
- ISSN
- 2375-7477 (ISSN-L)
- OpenAlex
- W2898889089
- Cited by
- 76
- References
- 17
- Citations by year
- 2026: 5 · 2025: 7 · 2024: 12 · 2023: 18 · 2022: 13 · 2021: 11 · 2020: 6 · 2019: 4
- FWCI
- 9.01
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (publishedVersion)
- PubMed (publishedVersion)
- 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, early diagnosis, mild cognitive impairment, EEG slowing, deep neural network classifier, spectral properties, EEG diagnosis
- MeSH terms
- Alzheimer Disease, Electroencephalography, Female, Humans, Neural Networks, Computer, Early Diagnosis, Cognitive Dysfunction
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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