Permutation Entropy of the Electroencephalogram Background Activity in Alzheimer’s Disease - Investigation into the Incidence of Repeated Values

2014 ◽  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Background Activity ◽  
Permutation Entropy

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.

scholarly journals Inter-band Bispectral Analysis of EEG Background Activity to Characterize Alzheimer's Disease Continuum

2020 ◽  
Vol 14 ◽  
Author(s):  
Aarón Maturana-Candelas ◽  
Carlos Gómez ◽  
Jesús Poza ◽  
Saúl J. Ruiz-Gómez ◽  
Roberto Hornero
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Background Activity ◽  
Bispectral Analysis

scholarly journals A Robust Discriminant Framework Based on Functional Biomarkers of EEG and Its Potential for Diagnosis of Alzheimer’s Disease

Healthcare ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 476
Author(s):  
Qi Ge ◽  
Zhuo-Chen Lin ◽  
Yong-Xiang Gao ◽  
Jin-Xin Zhang
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
High Performance ◽  
Pearson Correlation ◽  
Quantitative Eeg ◽  
Permutation Entropy ◽  
Discrete Wavelet ◽  
Diagnostic Biomarkers ◽  
Linear Discriminant ◽  
Diagnostic Framework

(1) Background: Growing evidence suggests that electroencephalography (EEG), recording the brain’s electrical activity, can be a promising diagnostic tool for Alzheimer’s disease (AD). The diagnostic biomarkers based on quantitative EEG (qEEG) have been extensively explored, but few of them helped clinicians in their everyday practice, and reliable qEEG markers are still lacking. The study aims to find robust EEG biomarkers and propose a systematic discrimination framework based on signal processing and computer-aided techniques to distinguish AD patients from normal elderly controls (NC). (2) Methods: In the proposed study, EEG signals were preprocessed firstly and Maximal overlap discrete wavelet transform (MODWT) was applied to the preprocessed signals. Variance, Pearson correlation coefficient, interquartile range, Hoeffding’s D measure, and Permutation entropy were extracted as the input of the candidate classifiers. The AD vs. NC discriminant performance of each model was evaluated and an automatic diagnostic framework was eventually developed. (3) Results: A classification procedure based on the extracted EEG features and linear discriminant analysis based classifier achieved the accuracy of 93.18 ± 3.65 (%), the AUC of 97.92 ± 1.66 (%), the F-measure of 94.06 ± 4.04 (%), separately. (4) Conclusions: The developed discrimination framework can identify AD from NC with high performance in a systematic routine.

scholarly journals Analysis of the magnetoencephalogram background activity in Alzheimer's disease patients with auto-mutual information

2007 ◽  
Vol 87 (3) ◽  
pp. 239-247 ◽  
Author(s):  
Carlos Gómez ◽  
Roberto Hornero ◽  
Daniel Abásolo ◽  
Alberto Fernández ◽  
Javier Escudero
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Mutual Information ◽  
Background Activity

scholarly journals Permutation Entropy and Statistical Complexity in Mild Cognitive Impairment and Alzheimer’s Disease: An Analysis Based on Frequency Bands

Entropy ◽  
2020 ◽  
Vol 22 (1) ◽  
pp. 116 ◽  
Author(s):  
Ignacio Echegoyen ◽  
David López-Sanz ◽  
Johann H. Martínez ◽  
Fernando Maestú ◽  
Javier M. Buldú
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Frequency Band ◽  
Local Level ◽  
Permutation Entropy ◽  
Frequency Bands ◽  
Individual Level ◽  
Statistical Complexity

We present one of the first applications of Permutation Entropy (PE) and Statistical Complexity (SC) (measured as the product of PE and Jensen-Shanon Divergence) on Magnetoencephalography (MEG) recordings of 46 subjects suffering from Mild Cognitive Impairment (MCI), 17 individuals diagnosed with Alzheimer’s Disease (AD) and 48 healthy controls. We studied the differences in PE and SC in broadband signals and their decomposition into frequency bands ( δ , θ , α and β ), considering two modalities: (i) raw time series obtained from the magnetometers and (ii) a reconstruction into cortical sources or regions of interest (ROIs). We conducted our analyses at three levels: (i) at the group level we compared SC in each frequency band and modality between groups; (ii) at the individual level we compared how the [PE, SC] plane differs in each modality; and (iii) at the local level we explored differences in scalp and cortical space. We recovered classical results that considered only broadband signals and found a nontrivial pattern of alterations in each frequency band, showing that SC does not necessarily decrease in AD or MCI.

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