CNN for a Regression Machine Learning Algorithm for Predicting Cognitive Impairment Using qEEG
DC Field | Value | Language |
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dc.contributor.author | Simfukwe, Chanda | - |
dc.contributor.author | Youn, Young Chul | - |
dc.contributor.author | Kim, Min-Jae | - |
dc.contributor.author | Paik, Joonki | - |
dc.contributor.author | Han, Su-Hyun | - |
dc.date.accessioned | 2024-07-12T00:30:27Z | - |
dc.date.available | 2024-07-12T00:30:27Z | - |
dc.date.issued | 2023-04 | - |
dc.identifier.issn | 1176-6328 | - |
dc.identifier.issn | 1178-2021 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/91984 | - |
dc.description.abstract | Purpose: Electroencephalogram (EEG) signals give detailed information on the electrical brain activities occurring in the cerebral cortex. They are used to study brain-related disorders such as mild cognitive impairment (MCI) and Alzheimer's disease (AD). Brain signals obtained using an EEG machine can be a neurophysiological biomarker for early diagnosis of dementia through quantitative EEG (qEEG) analysis. This paper proposes a machine learning methodology to detect MCI and AD from qEEG time-frequency (TF) images of the subjects in an eyes-closed resting state (ECR). Participants and Methods: The dataset consisted of 16,910 TF images from 890 subjects: 269 healthy controls (HC), 356 MCI, and 265 AD. First, EEG signals were transformed into TF images using a Fast Fourier Transform (FFT) containing different event-rated changes of frequency sub-bands preprocessed from the EEGlab toolbox in the MATLAB R2021a environment software. The preprocessed TF images were applied in a convolutional neural network (CNN) with adjusted parameters. For classification, the computed image features were concatenated with age data and went through the feed-forward neural network (FNN).Results: The trained models', HC vs MCI, HC vs AD, and HC vs CASE (MCI + AD), performance metrics were evaluated based on the test dataset of the subjects. The accuracy, sensitivity, and specificity were evaluated: HC vs MCI was 83%, 93%, and 73%, HC vs AD was 81%, 80%, and 83%, and HC vs CASE (MCI + AD) was 88%, 80%, and 90%, respectively.Conclusion: The proposed models trained with TF images and age can be used to assist clinicians as a biomarker in detecting cognitively impaired subjects at an early stage in clinical sectors. | - |
dc.format.extent | 13 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | DOVE MEDICAL PRESS LTD | - |
dc.title | CNN for a Regression Machine Learning Algorithm for Predicting Cognitive Impairment Using qEEG | - |
dc.type | Article | - |
dc.identifier.wosid | 000970011000001 | - |
dc.identifier.doi | 10.2147/NDT.S404528 | - |
dc.identifier.bibliographicCitation | NEUROPSYCHIATRIC DISEASE AND TREATMENT, v.19, pp 851 - 863 | - |
dc.description.isOpenAccess | Y | - |
dc.identifier.scopusid | 2-s2.0-85156167984 | - |
dc.citation.endPage | 863 | - |
dc.citation.startPage | 851 | - |
dc.citation.title | NEUROPSYCHIATRIC DISEASE AND TREATMENT | - |
dc.citation.volume | 19 | - |
dc.type.docType | Article | - |
dc.publisher.location | 뉴질랜드 | - |
dc.subject.keywordAuthor | neurodegenerative diseases | - |
dc.subject.keywordAuthor | electroencephalography | - |
dc.subject.keywordAuthor | supervised machine learning | - |
dc.subject.keywordAuthor | regression analysis | - |
dc.subject.keywordPlus | ALZHEIMERS-DISEASE | - |
dc.subject.keywordPlus | QUANTITATIVE ELECTROENCEPHALOGRAPHY | - |
dc.subject.keywordPlus | EEG | - |
dc.subject.keywordPlus | DEMENTIA | - |
dc.subject.keywordPlus | NEUROLOGY | - |
dc.subject.keywordPlus | DYNAMICS | - |
dc.relation.journalResearchArea | Neurosciences & Neurology | - |
dc.relation.journalResearchArea | Psychiatry | - |
dc.relation.journalWebOfScienceCategory | Clinical Neurology | - |
dc.relation.journalWebOfScienceCategory | Psychiatry | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
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