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

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.

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 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 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.

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