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

Abstract

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 [email protected]; [email protected]: 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

Keywords

• neurodegenerative diseases
• electroencephalography
• supervised machine learning
• regression analysis.