scholarly journals Electroencephalogram Background Activity Characterization with Approximate Entropy and Auto Mutual Information in Alzheimer's Disease Patients

2007 ◽  
Author(s):  
Daniel Abasolo ◽  
Roberto Hornero ◽  
Pedro Espino ◽  
Javier Escudero ◽  
Carlos Gomez
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Mutual Information ◽  
Background Activity ◽  
Approximate Entropy

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 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 Analysis of regularity in the EEG background activity of Alzheimer's disease patients with Approximate Entropy

2005 ◽  
Vol 116 (8) ◽  
pp. 1826-1834 ◽  
Author(s):  
Daniel Abásolo ◽  
Roberto Hornero ◽  
Pedro Espino ◽  
Jesús Poza ◽  
Clara I. Sánchez ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Background Activity ◽  
Approximate 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

PET Imaging of Tau Pathology and Amyloid-β, and MRI for Alzheimer’s Disease Feature Fusion and Multimodal Classification

2021 ◽  
pp. 1-18
Author(s):  
Mehdi Shojaie ◽  
Solale Tabarestani ◽  
Mercedes Cabrerizo ◽  
Steven T. DeKosky ◽  
David E. Vaillancourt ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Feature Selection ◽  
Mutual Information ◽  
Feature Fusion ◽  
Early Stage ◽  
Model Performance ◽  
Amyloid Β ◽  
Feature Ranking ◽  
Promising Tool

Background: Machine learning is a promising tool for biomarker-based diagnosis of Alzheimer’s disease (AD). Performing multimodal feature selection and studying the interaction between biological and clinical AD can help to improve the performance of the diagnosis models. Objective: This study aims to formulate a feature ranking metric based on the mutual information index to assess the relevance and redundancy of regional biomarkers and improve the AD classification accuracy. Methods: From the Alzheimer’s Disease Neuroimaging Initiative (ADNI), 722 participants with three modalities, including florbetapir-PET, flortaucipir-PET, and MRI, were studied. The multivariate mutual information metric was utilized to capture the redundancy and complementarity of the predictors and develop a feature ranking approach. This was followed by evaluating the capability of single-modal and multimodal biomarkers in predicting the cognitive stage. Results: Although amyloid-β deposition is an earlier event in the disease trajectory, tau PET with feature selection yielded a higher early-stage classification F1-score (65.4%) compared to amyloid-β PET (63.3%) and MRI (63.2%). The SVC multimodal scenario with feature selection improved the F1-score to 70.0% and 71.8% for the early and late-stage, respectively. When age and risk factors were included, the scores improved by 2 to 4%. The Amyloid-Tau-Neurodegeneration [AT(N)] framework helped to interpret the classification results for different biomarker categories. Conclusion: The results underscore the utility of a novel feature selection approach to reduce the dimensionality of multimodal datasets and enhance model performance. The AT(N) biomarker framework can help to explore the misclassified cases by revealing the relationship between neuropathological biomarkers and cognition.

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