CNN for a Regression Machine Learning Algorithm for Predicting Cognitive Impairment Using qEEGopen access
- Authors
- Simfukwe, Chanda; Youn, Young Chul; Kim, Min-Jae; Paik, Joonki; Han, Su-Hyun
- Issue Date
- Apr-2023
- Publisher
- DOVE MEDICAL PRESS LTD
- Keywords
- neurodegenerative diseases; electroencephalography; supervised machine learning; regression analysis
- Citation
- NEUROPSYCHIATRIC DISEASE AND TREATMENT, v.19, pp 851 - 863
- Pages
- 13
- Journal Title
- NEUROPSYCHIATRIC DISEASE AND TREATMENT
- Volume
- 19
- Start Page
- 851
- End Page
- 863
- URI
- https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/91984
- DOI
- 10.2147/NDT.S404528
- ISSN
- 1176-6328
1178-2021
- 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.
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