دورية أكاديمية
CNN for a Regression Machine Learning Algorithm for Predicting Cognitive Impairment Using qEEG
| العنوان: | CNN for a Regression Machine Learning Algorithm for Predicting Cognitive Impairment Using qEEG |
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| المؤلفون: | Simfukwe C, Youn YC, Kim MJ, Paik J, Han SH |
| المصدر: | Neuropsychiatric Disease and Treatment, Vol Volume 19, Pp 851-863 (2023) |
| بيانات النشر: | Dove Medical Press, 2023. |
| سنة النشر: | 2023 |
| المجموعة: | LCC:Neurosciences. Biological psychiatry. Neuropsychiatry LCC:Neurology. Diseases of the nervous system |
| مصطلحات موضوعية: | neurodegenerative diseases, electroencephalography, supervised machine learning, regression analysis., Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571, Neurology. Diseases of the nervous system, RC346-429 |
| الوصف: | Chanda Simfukwe,1 Young Chul Youn,1 Min-Jae Kim,2 Joonki Paik,2 Su-Hyun Han1 1Department of Neurology, Chung-Ang University College of Medicine, Seoul, South Korea; 2Department of Image, Chung-Ang University, Seoul, South KoreaCorrespondence: Young Chul Youn; Su-Hyun Han, Department of Neurology, Chung-Ang University Hospital, Seoul, Republic of Korea, Email neudoc@cau.ac.kr; freesu84@cau.ac.krPurpose: 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.Keywords: neurodegenerative diseases, electroencephalography, supervised machine learning, regression analysis |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 1178-2021 |
| Relation: | https://www.dovepress.com/cnn-for-a-regression-machine-learning-algorithm-for-predicting-cogniti-peer-reviewed-fulltext-article-NDT; https://doaj.org/toc/1178-2021 |
| URL الوصول: | https://doaj.org/article/0e2349f492fc4d18b74d9dfc855471a4 |
| رقم الأكسشن: | edsdoj.0e2349f492fc4d18b74d9dfc855471a4 |
| قاعدة البيانات: | Directory of Open Access Journals |
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Full Text Finder | تدمد: | 11782021 |
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