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

Abstract

AbstractBackgroundElectroencephalogram (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 dementia. 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 dementia from qEEG time‐frequency (TF) images of the subjects in an eyes‐closed resting state (ECR).MethodThe dataset consisted of 16,910 TF images from 890 subjects: 269 healthy controls (HC), 356 MCI, and 265 dementia. 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 (FFN).ResultThe trained models’, HC vs. MCI, HC vs. dementia, and HC vs. CASE (MCI + dementia), 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. dementia was 81%, 80%, and 83%, and HC vs. CASE (MCI + dementia) was 88%, 80%, and 90%, respectively.ConclusionThe 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.