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: 2.65). 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.

2 Followers
 · 
118 Views
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Background: Slowing of the electroencephalogram (EEG) is frequent in Parkinson's (PD) and Alzheimer's disease (AD) and correlates with cognitive decline. As overlap pathology plays a role in the pathogenesis of dementia, it is likely that demented patients in PD show similar physiological alterations as in AD. Objective: To analyze distinctive quantitative EEG characteristics in early cognitive dysfunction in PD and AD. Methods: Forty patients (20 PD- and 20 AD patients with early cognitive impairment) and 20 normal controls (NC) were matched for gender, age, and education. Resting state EEG was recorded from 256 electrodes. Relative power spectra, median frequency (4-14 Hz), and neuropsychological outcome were compared between groups. Results: Relative theta power in left temporal region and median frequency separated the three groups significantly (p = 0.002 and p < 0.001). Relative theta power was increased and median frequency reduced in patients with both diseases compared to NC. Median frequency was higher in AD than in PD and classified groups significantly (p = 0.02). Conclusion: Increase of theta power in the left temporal region and a reduction of median frequency were associated with presence of AD or PD. PD patients are characterized by a pronounced slowing as compared to AD patients. Therefore, in both disorders EEG slowing might be a useful biomarker for beginning cognitive decline.
    Frontiers in Aging Neuroscience 11/2014; 6:314. DOI:10.3389/fnagi.2014.00314 · 2.84 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: In this paper, we have combined experimental neurophysiologic recording and statistical analysis to investigate the nonlinear characteristic and the cognitive function of the brain. Spectrum and bispectrum analyses are proposed to extract multiple effective features of electroencephalograph (EEG) signals from Alzheimer's disease (AD) patients and further applied to distinguish AD patients from the normal controls. Spectral analysis based on autoregressive Burg method is first used to quantify the power distribution of EEG series in the frequency domain. Compared to the control group, the relative power spectral density of AD group is significantly higher in the theta frequency band, while lower in the alpha frequency bands. In addition, median frequency of spectrum is decreased, and spectral entropy ratio of these two frequency bands undergoes drastic changes at the P3 electrode in the central-parietal brain region, implying that the electrophysiological behavior in AD brain is much slower and less irregular. In order to explore the nonlinear high order information, bispectral analysis which measures the complexity of phase-coupling is further applied to P3 electrode in the whole frequency band. It is demonstrated that less bispectral peaks appear and the amplitudes of peaks fall, suggesting a decrease of non-Gaussianity and nonlinearity of EEG in ADs. Notably, the application of this method to five brain regions shows higher concentration of the weighted center of bispectrum and lower complexity reflecting phase-coupling by bispectral entropy. Based on spectrum and bispectrum analyses, six efficient features are extracted and then applied to discriminate AD from the normal in the five brain regions. The classification results indicate that all these features could differentiate AD patients from the normal controls with a maximum accuracy of 90.2%. Particularly, different brain regions are sensitive to different features. Moreover, the optimal combination of features obtained by discriminant analysis may improve the classification accuracy. These results demonstrate the great promise for scape EEG spectral and bispectral features as a potential effective method for detection of AD, which may facilitate our understanding of the pathological mechanism of the disease.
    Chaos (Woodbury, N.Y.) 01/2015; 25(1):013110. DOI:10.1063/1.4906038 · 1.76 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Objective. Recently, significant advances have been made in the early diagnosis of Alzheimer’s disease (AD) from electroencephalography (EEG). However, choosing suitable measures is a challenging task. Among other measures, frequency relative power (RP) and loss of complexity have been used with promising results. In the present study we investigate the early diagnosis of AD using synchrony measures and frequency RP on EEG signals, examining the changes found in different frequency ranges. Approach. We first explore the use of a single feature for computing the classification rate (CR), looking for the best frequency range. Then, we present a multiple feature classification system that outperforms all previous results using a feature selection strategy. These two approaches are tested in two different databases, one containing mild cognitive impairment (MCI) and healthy subjects (patients age: 71.9 ± 10.2, healthy subjects age: 71.7 ± 8.3), and the other containing Mild AD and healthy subjects (patients age: 77.6 ± 10.0; healthy subjects age: 69.4 ± 11.5). Main results. Using a single feature to compute CRs we achieve a performance of 78.33% for the MCI data set and of 97.56% for Mild AD. Results are clearly improved using the multiple feature classification, where a CR of 95% is found for the MCI data set using 11 features, and 100% for the Mild AD data set using four features. Significance. The new features selection method described in this work may be a reliable tool that could help to design a realistic system that does not require prior knowledge of a patient's status. With that aim, we explore the standardization of features for MCI and Mild AD data sets with promising results.
    Journal of Neural Engineering 01/2015; 12(1). DOI:10.1088/1741-2560/12/1/016018 · 3.42 Impact Factor

Full-text

Download
40 Downloads
Available from
Jun 3, 2014