Title
A Method For Diagnosis Support Of Mild Cognitive Impairment Through Eeg Rhythms Source Location During Working Memory Tasks
Abstract
Objective: We investigated group differences in current source density (CSD) patterns from EEG signals before and after a working memory (WM) task performed by mild cognitive impaired (MCI) subjects and healthy elderly (HE).Methods: EEG was recorded during N-back WM tasks in 41 age-, sex- and education-matched participants divided into MCI (N = 19) and HE (N = 22) groups. EEG epochs were divided into pre- and post-stimulus periods, named herein as working memory epochs (WME) and event-related epochs (ERE), respectively. Frequency-domain CSD was extracted for both WME and ERE on delta, theta, alpha, beta, and gamma bands using LORETA. Group comparisons were performed under Statistical non-Parametric Mapping. Moreover, after feature selection, we performed cross-validation with a Support Vector Machine (SVM) classifier.Results: MCI displayed increased spectral CSD on delta and theta (low-frequency) and decreased spectral CSD on (high-frequency) alpha and beta bands when compared to HE. Surprisingly, MCI patients presented an increase in gamma at precuneus and a decrease at occipital cortex. Group prediction through SVM achieved 96% accuracy, 98% specificity and 93% sensitivity when WME and ERE spectral CSD features were combined.Conclusions: Our findings confirmed the overall EEG slowing observed in classical MCI resting-state EEG literature as well as alpha desynchronization changes observed in task-related EEG literature. Furthermore, they also revealed MCI abnormalities in the gamma band. Significance: Our frequency-domain analysis of CSD patterns in task-related EEG, focusing both on pre- and poststimulus periods, may be a clinically relevant tool to support MCI diagnosis.
Year
DOI
Venue
2021
10.1016/j.bspc.2021.102499
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
Keywords
DocType
Volume
Mild Cognitive Impairment, Alzheimer's disease, Working memory, Source localization (LORETA), Machine learning, Support vector machine (SVM)
Journal
66
ISSN
Citations 
PageRank 
1746-8094
0
0.34
References 
Authors
0
6