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

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.

Download Full-text

Analysis of Volume Conduction Effects on Different Functional Connectivity Metrics: Application to Alzheimer’s Disease EEG Signals

2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) ◽

10.1109/embc.2019.8856548 ◽

2019 ◽

Author(s):

Saul J. Ruiz-Gomez ◽

Carlos Gomez ◽

Jesus Poza ◽

Aaron Maturana-Candelas ◽

Victor Rodriguez-Gonzalez ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Functional Connectivity ◽

Volume Conduction ◽

Eeg Signals

Download Full-text

Alzheimer's disease patients classification through EEG signals processing

2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) ◽

10.1109/cidm.2014.7008655 ◽

2014 ◽

Cited By ~ 9

Author(s):

Giulia Fiscon ◽

Emanuel Weitschek ◽

Giovanni Felici ◽

Paola Bertolazzi ◽

Simona De Salvo ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Eeg Signals

Download Full-text

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

Current Alzheimer Research ◽

10.2174/1567210204558652050 ◽

2010 ◽

Vol 999 (999) ◽

pp. 1-19 ◽

Cited By ~ 15

Author(s):

Justin Dauwels ◽

Francois Vialatte ◽

Andrzej Cichocki

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Eeg Signals

Download Full-text

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

Advances in Cognitive Neurodynamics (II) ◽

10.1007/978-90-481-9695-1_106 ◽

2010 ◽

pp. 709-716 ◽

Cited By ~ 14

Author(s):

Justin Dauwels ◽

François-Benoît Vialatte ◽

Andrzej Cichocki

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Early Diagnosis ◽

Eeg Signals

Download Full-text

Alzheimer’s Disease Recognition with Artificial Neural Networks

Information Systems and Technologies for Enhancing Health and Social Care ◽

10.4018/978-1-4666-3667-5.ch007 ◽

2013 ◽

pp. 102-118 ◽

Cited By ~ 6

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.

Download Full-text