CNN for a Regression Machine Learning Algorithm for Predicting Cognitive Impairment Using qEEG

• Published in: Neuropsychiatric disease and treatment Vol. 19; pp. 851 - 863
• Main Authors: Simfukwe, Chanda, Youn, Young Chul, Kim, Min-Jae, Paik, Joonki, Han, Su-Hyun
• Format: Journal Article
• Language: English
• Published: New Zealand Dove Medical Press Limited 01.01.2023; Dove; Dove Medical Press
• Subjects: Algorithms; Cognition disorders; Diagnosis; Electroencephalography; Machine learning; Neural networks; neurodegenerative diseases; Original Research; regression analysis; Risk factors; supervised machine learning; South Korea; supervised machine learning; electroencephalography; neurodegenerative diseases; regression analysis
• ISSN: 1178-2021; 1176-6328; 1178-2021
• DOI: 10.2147/NDT.S404528

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). 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). 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. 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.