scholarly journals Applications of Blind Source Separation to the Magnetoencephalogram Background Activity in Alzheimer’s Disease

2010 ◽  
Author(s):  
Javier Escudero Rodríguez
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Blind Source Separation ◽  
Background Activity ◽  
Source Separation

EEG filtering based on blind source separation (BSS) for early detection of Alzheimer's disease

2005 ◽  
Vol 116 (3) ◽  
pp. 729-737 ◽  
Author(s):  
Andrzej Cichocki ◽  
Sergei L. Shishkin ◽  
Toshimitsu Musha ◽  
Zbigniew Leonowicz ◽  
Takashi Asada ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Early Detection ◽  
Blind Source Separation ◽  
Source Separation

scholarly journals Assessment of classification improvement in patients with Alzheimer's disease based on magnetoencephalogram blind source separation

2008 ◽  
Vol 43 (1) ◽  
pp. 75-85 ◽  
Author(s):  
Javier Escudero ◽  
Roberto Hornero ◽  
Jesús Poza ◽  
Daniel Abásolo ◽  
Alberto Fernández
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Blind Source Separation ◽  
Source Separation

scholarly journals Complexity analysis of the magnetoencephalogram background activity in Alzheimer's disease patients

2006 ◽  
Vol 28 (9) ◽  
pp. 851-859 ◽  
Author(s):  
Carlos Gómez ◽  
Roberto Hornero ◽  
Daniel Abásolo ◽  
Alberto Fernández ◽  
Miguel López
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Complexity Analysis ◽  
Background Activity

scholarly journals Entropy and Complexity Analyses in Alzheimer’s Disease: An MEG Study

2010 ◽  
Vol 4 (1) ◽  
pp. 223-235 ◽  
Author(s):  
Carlos Gómez ◽  
Roberto Hornero
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Fractal Dimension ◽  
Background Activity ◽  
Senile Plaques ◽  
Approximate Entropy ◽  
Brain Regions ◽  
Brain Disorder ◽  
Complexity Measures ◽  
Control Subjects

Alzheimer’s disease (AD) is one of the most frequent disorders among elderly population and it is considered the main cause of dementia in western countries. This irreversible brain disorder is characterized by neural loss and the appearance of neurofibrillary tangles and senile plaques. The aim of the present study was the analysis of the magnetoencephalogram (MEG) background activity from AD patients and elderly control subjects. MEG recordings from 36 AD patients and 26 controls were analyzed by means of six entropy and complexity measures: Shannon spectral entropy (SSE), approximate entropy (ApEn), sample entropy (SampEn), Higuchi’s fractal dimension (HFD), Maragos and Sun’s fractal dimension (MSFD), and Lempel-Ziv complexity (LZC). SSE is an irregularity estimator in terms of the flatness of the spectrum, whereas ApEn and SampEn are embbeding entropies that quantify the signal regularity. The complexity measures HFD and MSFD were applied to MEG signals to estimate their fractal dimension. Finally, LZC measures the number of different substrings and the rate of their recurrence along the original time series. Our results show that MEG recordings are less complex and more regular in AD patients than in control subjects. Significant differences between both groups were found in several brain regions using all these methods, with the exception of MSFD (p-value < 0.05, Welch’s t-test with Bonferroni’s correction). Using receiver operating characteristic curves with a leave-one-out cross-validation procedure, the highest accuracy was achieved with SSE: 77.42%. We conclude that entropy and complexity analyses from MEG background activity could be useful to help in AD diagnosis.

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