Parameters of EEG dimensional complexity in Alzheimer's disease

Analysis of regularity in the EEG background activity of Alzheimer's disease patients with Approximate Entropy

“…We infer that brains affected by AD show a more regular and less complex electrophysiological behaviour in the parietal region. This confirms findings associated with the fact that a diffuse slowing of the background activity may be found in the EEGs of patients with AD (Markand, 1990) and that the dynamic processes underlying the EEG recording are less complex for AD patients than for normal subjects (Besthorn et al, 1995(Besthorn et al, , 1997Jeong et al, 1998;Jelles et al, 1999;Jeong et al, 2001a). This reduction of irregularity could be explained by a decrease of dynamical complexity of part of the brain in AD patients.…”

“…Results showed that AD patients had lower D 2 values than controls (Stam et al, 1995;Jeong et al, 1998Jeong et al, , 2001a. Furthermore, Besthorn et al (1995) showed that a lower D 2 was correlated with increased severity of dementia and also found that this method correctly classified AD patients and controls with an accuracy of 70% (Besthorn et al, 1997).…”

“…Most of these studies have been carried out applying the D 2 (Stam et al, 1995;Besthorn et al, 1995Besthorn et al, , 1997Jeong et al, 1998;Jelles et al, 1999;Jeong et al, 2001a) or the first positive Lyapunov exponent (Jeong et al, 1998(Jeong et al, , 2001a. Measures such as D 2 , K-S entropy, the Lyapunov spectrum and related algorithms have been much studied in the presence of noise and limited data.…”

Nonlinear analysis of electroencephalogram and magnetoencephalogram recordings in patients with Alzheimer's disease

“…These results show a decrease in the complexity of the electrical activity in brains injured by AD (Jeong 2004). Furthermore, Besthorn et al (1995) showed that a lower D 2 was correlated with increased severity of dementia and suggested that this reduced D 2 may be associated with an increase in the proportion of a lower-frequency component in the EEGs of AD patients. Pritchard et al (1994) reported that the addition of nonlinear measures (D 2 ) and a neural net classification procedure to linear methods improved the classification accuracy of AD patients and controls up to 92 per cent.…”

On the Early Diagnosis of Alzheimer’s Disease from EEG Signals: A Mini-Review

“…The following complexity measures have been used to quantify this reduction in EEG complexity: approximate entropy [6], auto mutual information [6], sample entropy [6,8], multiscale entropy [6], Lempel-Ziv complexity [6], and fractal dimension [7].…”