Article

Quantitative EEG in early Alzheimer's disease patients - power spectrum and complexity features.

Institute for Psychology, Hungarian Academy of Sciences, Budapest, Hungary.
International Journal of Psychophysiology (Impact Factor: 2.04). 05/2008; 68(1):75-80. DOI: 10.1016/j.ijpsycho.2007.11.002
Source: PubMed

ABSTRACT The goal of this study was to investigate the EEG signs of early stage Alzheimer's disease (AD) by conventional analyses and by methods quantifying linear and nonlinear EEG-complexity. The EEG was recorded in 12 mild AD patients and in an age-matched healthy control group (24 subjects) in both eyes open and eyes closed conditions. Frequency spectra, Omega-complexity and Synchronization likelihood were calculated on the data. In the patients a significant decrease of the relative alpha and increase of the theta power were found. Remarkably increased Omega-complexity and lower Synchronization likelihood were observed in AD in the 0.5-25 Hz frequency ranges. It is concluded that both spectral- and EEG-complexity changes can be found already in the early stage of AD in a wide frequency range. Application of conventional EEG analysis methods in combination with quantification of EEG-complexity may improve the chances of early diagnosis of AD.

1 Bookmark
 · 
95 Views
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: To evaluate the hypothesis that quantitative EEG (qEEG) analysis is susceptible to detect early functional changes in familial Alzheimer's disease (AD) preclinical stages. Three groups of subjects were selected from five extended families with hereditary AD: a Probable AD group (18 subjects), an asymptomatic carrier (ACr) group (21 subjects), with the mutation but without any clinical symptoms of dementia, and a normal group of 18 healthy subjects. In order to reveal significant differences in the spectral parameter, the Mahalanobis distance (D (2)) was calculated between groups. To evaluate the diagnostic efficiency of this statistic D (2), the ROC models were used. The ROC curve was summarized by accuracy index and standard deviation. The D (2) using the parameters of the energy in the fast frequency bands shows accurate discrimination between normal and ACr groups (area ROC = 0.89) and between AD probable and ACr groups (area ROC = 0.91). This is more significant in temporal regions. Theses parameters could be affected before the onset of the disease, even when cognitive disturbance is not clinically evident. Spectral EEG parameter could be firstly used to evaluate subjects with E280A Presenilin-1 mutation without impairment in cognitive function.
    International journal of Alzheimer's disease. 01/2014; 2014:180741.
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Over the last decade, electroencephalography (EEG) has emerged as a reliable tool for the diagnosis of cortical disorders such as Alzheimer's disease (AD). EEG signals, however, are susceptible to several artifacts, such as ocular, muscular, movement, and environmental. To overcome this limitation, existing diagnostic systems commonly depend on experienced clinicians to manually select artifact-free epochs from the collected multi-channel EEG data. Manual selection, however, is a tedious and time-consuming process, rendering the diagnostic system "semi-automated." Notwithstanding, a number of EEG artifact removal algorithms have been proposed in the literature. The (dis)advantages of using such algorithms in automated AD diagnostic systems, however, have not been documented; this paper aims to fill this gap. Here, we investigate the effects of three state-of-the-art automated artifact removal (AAR) algorithms (both alone and in combination with each other) on AD diagnostic systems based on four different classes of EEG features, namely, spectral, amplitude modulation rate of change, coherence, and phase. The three AAR algorithms tested are statistical artifact rejection (SAR), blind source separation based on second order blind identification and canonical correlation analysis (BSS-SOBI-CCA), and wavelet enhanced independent component analysis (wICA). Experimental results based on 20-channel resting-awake EEG data collected from 59 participants (20 patients with mild AD, 15 with moderate-to-severe AD, and 24 age-matched healthy controls) showed the wICA algorithm alone outperforming other enhancement algorithm combinations across three tasks: diagnosis (control vs. mild vs. moderate), early detection (control vs. mild), and disease progression (mild vs. moderate), thus opening the doors for fully-automated systems that can assist clinicians with early detection of AD, as well as disease severity progression assessment.
    Frontiers in Aging Neuroscience 01/2014; 6:55. · 5.20 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Alzheimer's disease (AD) is a neurodegenerative disorder that is characterized by cognitive deficits, problems in activities of daily living, and behavioral disturbances. Electroencephalogram (EEG) has been demonstrated as a reliable tool in dementia research and diagnosis. The application of EEG in AD has a wide range of interest. EEG contributes to the differential diagnosis and the prognosis of the disease progression. Additionally such recordings can add important information related to the drug effectiveness. This review is prepared to form a knowledge platform for the project entitled "Cognitive Signal Processing Lab," which is in progress in Information Technology Institute in Thessaloniki. The team tried to focus on the main research fields of AD via EEG and recent published studies.
    International journal of Alzheimer's disease. 01/2014; 2014:349249.

Full-text

Download
25 Downloads
Available from
Jun 3, 2014