scholarly journals Multivariate Multi-Scale Permutation Entropy for Complexity Analysis of Alzheimer’s Disease EEG

Entropy ◽  
2012 ◽  
Vol 14 (7) ◽  
pp. 1186-1202 ◽  
Author(s):  
Francesco Carlo Morabito ◽  
Domenico Labate ◽  
Fabio La Foresta ◽  
Alessia Bramanti ◽  
Giuseppe Morabito ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Complexity Analysis ◽  
Permutation Entropy ◽  
Multi Scale

scholarly journals Multivariate multi-scale weighted permutation entropy analysis of EEG complexity for Alzheimer’s disease

2016 ◽  
Vol 11 (3) ◽  
pp. 217-231 ◽  
Author(s):  
Bin Deng ◽  
Lihui Cai ◽  
Shunan Li ◽  
Ruofan Wang ◽  
Haitao Yu ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Permutation Entropy ◽  
Entropy Analysis ◽  
Multi Scale

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 Complexity analysis of cortical surface detects changes in future Alzheimer's disease converters

2017 ◽  
Vol 38 (12) ◽  
pp. 5905-5918 ◽  
Author(s):  
Juan Ruiz de Miras ◽  
Víctor Costumero ◽  
Vicente Belloch ◽  
Joaquín Escudero ◽  
César Ávila ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Complexity Analysis ◽  
Cortical Surface

Autonomic impairment in Alzheimer’s disease is revealed by complexity analysis of functional thermal imaging signals during cognitive tasks

2019 ◽  
Vol 40 (3) ◽  
pp. 034002 ◽  
Author(s):  
David Perpetuini ◽  
Daniela Cardone ◽  
Antonio Maria Chiarelli ◽  
Chiara Filippini ◽  
Pierpaolo Croce ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Thermal Imaging ◽  
Complexity Analysis ◽  
Cognitive Tasks

scholarly journals Multi-scale graph-based grading for Alzheimer’s disease prediction

2021 ◽  
Vol 67 ◽  
pp. 101850
Author(s):  
Kilian Hett ◽  
Vinh-Thong Ta ◽  
Ipek Oguz ◽  
José V. Manjón ◽  
Pierrick Coupé
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Disease Prediction ◽  
Multi Scale

scholarly journals Working Memory Decline in Alzheimer’s Disease Is Detected by Complexity Analysis of Multimodal EEG-fNIRS

Entropy ◽  
2020 ◽  
Vol 22 (12) ◽  
pp. 1380
Author(s):  
David Perpetuini ◽  
Antonio Maria Chiarelli ◽  
Chiara Filippini ◽  
Daniela Cardone ◽  
Pierpaolo Croce ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Working Memory ◽  
Near Infrared ◽  
Complexity Analysis ◽  
Brain Activity ◽  
Conditional Entropy ◽  
Functional Near Infrared Spectroscopy ◽  
Complex Figure ◽  
Raven's Progressive Matrices

Alzheimer’s disease (AD) is characterized by working memory (WM) failures that can be assessed at early stages through administering clinical tests. Ecological neuroimaging, such as Electroencephalography (EEG) and functional Near Infrared Spectroscopy (fNIRS), may be employed during these tests to support AD early diagnosis within clinical settings. Multimodal EEG-fNIRS could measure brain activity along with neurovascular coupling (NC) and detect their modifications associated with AD. Data analysis procedures based on signal complexity are suitable to estimate electrical and hemodynamic brain activity or their mutual information (NC) during non-structured experimental paradigms. In this study, sample entropy of whole-head EEG and frontal/prefrontal cortex fNIRS was evaluated to assess brain activity in early AD and healthy controls (HC) during WM tasks (i.e., Rey–Osterrieth complex figure and Raven’s progressive matrices). Moreover, conditional entropy between EEG and fNIRS was evaluated as indicative of NC. The findings demonstrated the capability of complexity analysis of multimodal EEG-fNIRS to detect WM decline in AD. Furthermore, a multivariate data-driven analysis, performed on these entropy metrics and based on the General Linear Model, allowed classifying AD and HC with an AUC up to 0.88. EEG-fNIRS may represent a powerful tool for the clinical evaluation of WM decline in early AD.

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.

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