Title | ||
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A Method For Diagnosis Support Of Mild Cognitive Impairment Through Eeg Rhythms Source Location During Working Memory Tasks |
Abstract | ||
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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 |
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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 |
Name | Order | Citations | PageRank |
---|---|---|---|
Rodrigo San-Martin | 1 | 0 | 0.68 |
Erin Johns | 2 | 0 | 0.34 |
Godofredo Quispe Mamani | 3 | 0 | 0.34 |
Guilherme Tavares | 4 | 0 | 0.34 |
Natalie A. Phillips | 5 | 0 | 0.34 |
Francisco J. Fraga | 6 | 14 | 2.53 |