Early Detection of Alzheimer’s Disease by Blind Source Separation, Time Frequency Representation, and Bump Modeling of EEG Signals

2005 ◽  
pp. 683-692 ◽  
Author(s):  
François Vialatte ◽  
Andrzej Cichocki ◽  
Gérard Dreyfus ◽  
Toshimitsu Musha ◽  
Sergei L. Shishkin ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Early Detection ◽  
Blind Source Separation ◽  
Source Separation ◽  
Eeg Signals ◽  
Time Frequency ◽  
Separation Time ◽  
Frequency Representation

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 Improving the Specificity of EEG for Diagnosing Alzheimer's Disease

2011 ◽  
Vol 2011 ◽  
pp. 1-7 ◽  
Author(s):  
François-B. Vialatte ◽  
Justin Dauwels ◽  
Monique Maurice ◽  
Toshimitsu Musha ◽  
Andrzej Cichocki
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Frequency Domain ◽  
Matched Control ◽  
Cost Effective ◽  
Eeg Signals ◽  
Time Frequency ◽  
Eyes Closed ◽  
Eeg Data ◽  
Rest Condition

Objective. EEG has great potential as a cost-effective screening tool for Alzheimer's disease (AD). However, the specificity of EEG is not yet sufficient to be used in clinical practice. In an earlier study, we presented preliminary results suggesting improved specificity of EEG to early stages of Alzheimer's disease. The key to this improvement is a new method for extracting sparse oscillatory events from EEG signals in the time-frequency domain. Here we provide a more detailed analysis, demonstrating improved EEG specificity for clinical screening of MCI (mild cognitive impairment) patients.Methods. EEG data was recorded of MCI patients and age-matched control subjects, in rest condition with eyes closed. EEG frequency bands of interest wereθ(3.5–7.5 Hz),α1(7.5–9.5 Hz),α2(9.5–12.5 Hz), andβ(12.5–25 Hz). The EEG signals were transformed in the time-frequency domain using complex Morlet wavelets; the resulting time-frequency maps are represented by sparse bump models.Results. Enhanced EEG power in theθrange is more easily detected through sparse bump modeling; this phenomenon explains the improved EEG specificity obtained in our previous studies.Conclusions. Sparse bump modeling yields informative features in EEG signal. These features increase the specificity of EEG for diagnosing AD.

Early Detection Method of Alzheimer’s Disease Using EEG Signals

2014 ◽  
pp. 25-33
Author(s):  
Dhiya Al-Jumeily ◽  
Shamaila Iram ◽  
Abir Jaffar Hussain ◽  
Vialatte Francois-Benois ◽  
Paul Fergus
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Early Detection ◽  
Detection Method ◽  
Eeg Signals
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