scholarly journals Univariate and Multivariate Generalized Multiscale Entropy to Characterise EEG Signals in Alzheimer’s Disease

Entropy ◽  
2017 ◽  
Vol 19 (1) ◽  
pp. 31 ◽  
Author(s):  
Hamed Azami ◽  
Daniel Abásolo ◽  
Samantha Simons ◽  
Javier Escudero
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Multiscale Entropy ◽  
Eeg Signals

scholarly journals Discrimination of Severity of Alzheimer’s Disease with Multiscale Entropy Analysis of EEG Dynamics

2020 ◽  
Vol 10 (4) ◽  
pp. 1244
Author(s):  
Chang Francis Hsu ◽  
Hsuan-Hao Chao ◽  
Albert C. Yang ◽  
Chih-Wei Yeh ◽  
Long Hsu ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Time Scale ◽  
Healthy Subjects ◽  
Small Time ◽  
Multiscale Entropy ◽  
Weighted Sum ◽  
Eeg Signals ◽  
Linear Discriminant ◽  
Scale Factors

Multiscale entropy (MSE) was used to analyze electroencephalography (EEG) signals to differentiate patients with Alzheimer’s disease (AD) from healthy subjects. It was found that the MSE values of the EEG signals from the healthy subjects are higher than those of the AD ones at small time scale factors in the MSE algorithm, while lower than those of the AD patients at large time scale factors. Based on the finding, we applied the linear discriminant analysis (LDA) to optimize the differentiating performance by comparing the resulting weighted sum of the MSE values under some specific time scales of each subject. The EEG data from 15 healthy subjects, 69 patients with mild AD, and 15 patients with moderate to severe AD were recorded. As a result, the weighted sum values are significantly higher for the healthy than the patients with moderate to severe AD groups. The optimal testing accuracy under five specific scales is 100% based on the EEG signals acquired from the T4 electrode. The resulting weighted sum value for the mild AD group is in the middle of those for the healthy and the moderate to severe AD groups. Therefore, the MSE-based weighted sum value can potentially be an index of severity of Alzheimer’s disease.

Alzheimer's disease patients classification through EEG signals processing

2014 ◽  
Author(s):  
Giulia Fiscon ◽  
Emanuel Weitschek ◽  
Giovanni Felici ◽  
Paola Bertolazzi ◽  
Simona De Salvo ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Eeg Signals

scholarly journals Diagnosis of Alzheimer's Disease from EEG Signals: Where Are We Standing?

2010 ◽  
Vol 999 (999) ◽  
pp. 1-19 ◽  
Author(s):  
Justin Dauwels ◽  
Francois Vialatte ◽  
Andrzej Cichocki
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Eeg Signals

Alzheimer’s Disease Recognition with Artificial Neural Networks

2013 ◽  
pp. 102-118 ◽  
Author(s):  
Pedro Miguel Rodrigues ◽  
João Paulo Teixeira
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Memory Loss ◽  
Eeg Signals ◽  
Intellectual Abilities ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Short Time ◽  
And Control ◽  
Electroencephalogram Eeg

Alzheimer’s Disease (AD) is the most common cause of dementia, and is well known for its affect on memory loss and other intellectual abilities. The Electroencephalogram (EEG) has been used as a diagnosis tool for dementia for several decades. The main objective of this work was to develop an Artificial Neural Network (ANN) to classify EEG signals between AD patients and control subjects. For this purpose, two different methodologies and variations were used. The Short Time Fourier Transform (STFT) was applied to one of the methodologies and the Wavelet Transform (WT) was applied to the other methodology. The studied features of the EEG signals were the Relative Power in conventional EEG bands and their associated Spectral Ratios (r1, r2, r3, and r4). The best classification was performed by the ANN using the WT Biorthogonal 3.5 with AROC of 0.97, Sensitivity of 92.1%, Specificity of 90.8%, and 91.5% of Accuracy.

scholarly journals Analysis of electroencephalograms in Alzheimer's disease patients with multiscale entropy

2006 ◽  
Vol 27 (11) ◽  
pp. 1091-1106 ◽  
Author(s):  
J Escudero ◽  
D Abásolo ◽  
R Hornero ◽  
P Espino ◽  
M López
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Multiscale Entropy

Supervised Network-Based Fuzzy Learning of EEG Signals for Alzheimer's Disease Identification

2020 ◽  
Vol 28 (1) ◽  
pp. 60-71 ◽  
Author(s):  
Haitao Yu ◽  
Xinyu Lei ◽  
Zhenxi Song ◽  
Chen Liu ◽  
Jiang Wang
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Eeg Signals ◽  
Disease Identification ◽  
Fuzzy Learning
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