Diagnosis of Early Alzheimer's Disease Based on EEG Source Localization and a Standardized Realistic Head Model

Abstract

In this paper, distributed electroencephalographic (EEG) sources in the brain have been mapped with the objective of early diagnosis of Alzheimer's disease (AD). To this end, records from a montage of a high-density EEG from 17 early AD patients and 17 matched healthy control subjects were considered. Subjects were in eyes-closed, resting-state condition. Cortical EEG sources were modeled by the standardized low-resolution brain electromagnetic tomography (sLORETA) method. Relative logarithmic power spectral density values were obtained in the four conventional frequency bands (alpha, beta, delta, and theta) and 12 cortical regions. Results show that in the left brain hemisphere, the theta band of AD subjects shows an increase in the power, whereas the alpha band shows a decreased activity (P-value <0.05). In the right brain hemisphere of AD subjects, a decreased activity is observed in all frequency bands. It was also noticed that the right temporal region shows a significant difference between the two groups in all frequency bands. Using a support vector machine, control and patient groups are discriminated with an accuracy of 84.4%, sensitivity 75.0%, and specificity of 93.7%.

Systematic Review on Resting-State EEG for Alzheimer’s Disease Diagnosis and Progression Assessment

“…Another point to be considered, detailed in subsequent Section 3.4.7 , is the minimal density required in source location analysis, as greater numbers of channels increase precision [ 182 ]. Regarding electrode layout, 107 studies used the 10-20 international positioning system (and its variations, 10-10 or 10-5 systems) or the Maudsley system; the remaining five studies [ 77 , 98 , 99 , 124 , 128 ] acquired EEG signals with 110 or more electrodes using the geodesic system.…”

“…Among the reviewed articles, 17 studies used source localization methods for characterizing AD. Fifteen of these articles utilized the low-resolution electromagnetic tomography (LORETA) method or its derivatives (eLORETA and sLORETA) [ 26 , 37 , 66 , 77 , 81 , 82 , 103 , 115 , 128 , 131 , 141 , 150 , 163 , 169 , 176 ]. The remaining two articles used the local autoregressive average (LAURA) source localization method [ 99 , 171 ].…”

Measuring Mental Workload with EEG+fNIRS

“…SVM can be a useful tool in the case of non-regularity in the data, for example when the data are not regularly distributed or have an unknown distribution (Auria and Moro, 2008). Linear SVM constructs an optimal hyperplane creating a decision surface maximizing the margin of separation between the closest data points belonging to different classes (Aghajani et al, 2013). The observations were randomly partitioned into k groups of approximately the same size.…”

“…Each training example contained a vector of feature values in a given window and its label that denoted one of the two classes of interest. The SVM constructed an optimal hyper-plane creating a decision surface maximizing the margin of separation between the closest data points belonging to different classes (Aghajani et al, 2013). In this study there were 10 possible pairs of binary classifications corresponding to our five distinct classes.…”

Directed Functional Networks in Alzheimer's Disease: Disruption of Global and Local Connectivity Measures

“…Previous studies showed that properties of EEG-based functional networks are altered in brain disorders such as Alzheimer's disease (AD) [6,[8][9][10][11][12]. AD is the most common type of neurodegenerative dementia that causes disruption in recalling the memories and thinking, impairment of the behavior and performing normal activities.…”