scholarly journals Combining complexity measures of EEG data: multiplying measures reveal previously hidden information

2015 ◽  
Author(s):  
Thomas F Burns
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Complete Information ◽  
Eeg Signals ◽  
Hidden Information ◽  
Complexity Measures ◽  
Human Eeg ◽  
Unique Information ◽  
Eeg Data ◽  
Two Measures

Many studies have noted significant differences among human EEG results when participants or patients are exposed to different stimuli, undertaking different tasks, or being affected by conditions such as epilepsy or Alzheimer's disease. Such studies often use only one or two measures of complexity and do not regularly justify their choice of measure beyond the fact that it has been used in previous studies. If more measures were added to such studies, however, more complete information might be found about these reported differences. Such information might be useful in confirming the existence or extent of such differences, or in understanding their physiological bases. In this study I analysed publically-available EEG data using a range of complexity measures to determine how well the measures correlated with one another. The complexity measures did not all significantly correlate, suggesting that different measures were measuring unique features of the EEG signals and thus revealing information which other measures were unable to detect. Therefore, the results from this analysis suggests that combinations of complexity measures reveal unique information which is in addition to the information captured by other measures of complexity in EEG data. For this reason, researchers using individual complexity measures for EEG data should consider using combinations of measures to more completely account for any differences they observe and to ensure the robustness of any relationships identified.

scholarly journals Combining complexity measures of EEG data: multiplying measures reveal previously hidden information

2015 ◽  
Author(s):  
Thomas F Burns
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Complete Information ◽  
Eeg Signals ◽  
Hidden Information ◽  
Complexity Measures ◽  
Human Eeg ◽  
Unique Information ◽  
Eeg Data ◽  
Two Measures

Many studies have noted significant differences among human EEG results when participants or patients are exposed to different stimuli, undertaking different tasks, or being affected by conditions such as epilepsy or Alzheimer's disease. Such studies often use only one or two measures of complexity and do not regularly justify their choice of measure beyond the fact that it has been used in previous studies. If more measures were added to such studies, however, more complete information might be found about these reported differences. Such information might be useful in confirming the existence or extent of such differences, or in understanding their physiological bases. In this study I analysed publically-available EEG data using a range of complexity measures to determine how well the measures correlated with one another. The complexity measures did not all significantly correlate, suggesting that different measures were measuring unique features of the EEG signals and thus revealing information which other measures were unable to detect. Therefore, the results from this analysis suggests that combinations of complexity measures reveal unique information which is in addition to the information captured by other measures of complexity in EEG data. For this reason, researchers using individual complexity measures for EEG data should consider using combinations of measures to more completely account for any differences they observe and to ensure the robustness of any relationships identified.

scholarly journals Combining complexity measures of EEG data: multiplying measures reveal previously hidden information

F1000Research ◽  
2015 ◽  
Vol 4 ◽  
pp. 137 ◽  
Author(s):  
Thomas Burns ◽  
Ramesh Rajan
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Complete Information ◽  
Eeg Signals ◽  
Hidden Information ◽  
Complexity Measures ◽  
Unique Information ◽  
Eeg Data ◽  
Two Measures

Many studies have noted significant differences among human electroencephalograph (EEG) results when participants or patients are exposed to different stimuli, undertaking different tasks, or being affected by conditions such as epilepsy or Alzheimer's disease. Such studies often use only one or two measures of complexity and do not regularly justify their choice of measure beyond the fact that it has been used in previous studies. If more measures were added to such studies, however, more complete information might be found about these reported differences. Such information might be useful in confirming the existence or extent of such differences, or in understanding their physiological bases. In this study we analysed publically-available EEG data using a range of complexity measures to determine how well the measures correlated with one another. The complexity measures did not all significantly correlate, suggesting that different measures were measuring unique features of the EEG signals and thus revealing information which other measures were unable to detect. Therefore, the results from this analysis suggests that combinations of complexity measures reveal unique information which is in addition to the information captured by other measures of complexity in EEG data. For this reason, researchers using individual complexity measures for EEG data should consider using combinations of measures to more completely account for any differences they observe and to ensure the robustness of any relationships identified.

scholarly journals Improving the Specificity of EEG for Diagnosing Alzheimer's Disease

2011 ◽  
Vol 2011 ◽  
pp. 1-7 ◽  
Author(s):  
François-B. Vialatte ◽  
Justin Dauwels ◽  
Monique Maurice ◽  
Toshimitsu Musha ◽  
Andrzej Cichocki
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Frequency Domain ◽  
Matched Control ◽  
Cost Effective ◽  
Eeg Signals ◽  
Time Frequency ◽  
Eyes Closed ◽  
Eeg Data ◽  
Rest Condition

Objective. EEG has great potential as a cost-effective screening tool for Alzheimer's disease (AD). However, the specificity of EEG is not yet sufficient to be used in clinical practice. In an earlier study, we presented preliminary results suggesting improved specificity of EEG to early stages of Alzheimer's disease. The key to this improvement is a new method for extracting sparse oscillatory events from EEG signals in the time-frequency domain. Here we provide a more detailed analysis, demonstrating improved EEG specificity for clinical screening of MCI (mild cognitive impairment) patients.Methods. EEG data was recorded of MCI patients and age-matched control subjects, in rest condition with eyes closed. EEG frequency bands of interest wereθ(3.5–7.5 Hz),α1(7.5–9.5 Hz),α2(9.5–12.5 Hz), andβ(12.5–25 Hz). The EEG signals were transformed in the time-frequency domain using complex Morlet wavelets; the resulting time-frequency maps are represented by sparse bump models.Results. Enhanced EEG power in theθrange is more easily detected through sparse bump modeling; this phenomenon explains the improved EEG specificity obtained in our previous studies.Conclusions. Sparse bump modeling yields informative features in EEG signal. These features increase the specificity of EEG for diagnosing AD.

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

Entropy ◽  
2017 ◽  
Vol 19 (1) ◽  
pp. 31 ◽  
Author(s):  
Hamed Azami ◽  
Daniel Abásolo ◽  
Samantha Simons ◽  
Javier Escudero
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Multiscale Entropy ◽  
Eeg Signals

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 Direction of Information Flow in Alzheimer's Disease and MCI Patients

2011 ◽  
Vol 2011 ◽  
pp. 1-7 ◽  
Author(s):  
Fabrizio Vecchio ◽  
Claudio Babiloni
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Information Flow ◽  
Additive Model ◽  
Functional Coupling ◽  
Directed Transfer Function ◽  
Directional Flow ◽  
Eeg Data ◽  
Rest Condition ◽  
Amnesic Mild Cognitive Impairment

Is directionality of electroencephalographic (EEG) synchronization abnormal in amnesic mild cognitive impairment (MCI) and Alzheimer's disease (AD)? And, do cerebrovascular and AD lesions represent additive factors in the development of MCI as a putative preclinical stage of AD? Here we reported two studies that tested these hypotheses. EEG data were recorded in normal elderly (Nold), amnesic MCI, and mild AD subjects at rest condition (closed eyes). Direction of information flow within EEG electrode pairs was performed by directed transfer function (DTF) atδ(2–4 Hz),θ(4–8 Hz),α1 (8–10 Hz),α2 (10–12 Hz),β1 (13–20 Hz),β2 (20–30 Hz), andγ(30–40 Hz). Parieto-to-frontal direction was stronger in Nold than in MCI and/or AD subjects forαandβrhythms. In contrast, the directional flow within interhemispheric EEG functional coupling did not discriminate among the groups. More interestingly, this coupling was higher atθ,α1,α2, andβ1 in MCI with higher than in MCI with lower vascular load. These results suggest that directionality of parieto-to-frontal EEG synchronization is abnormal not only in AD but also in amnesic MCI, supporting the additive model according to which MCI state would result from the combination of cerebrovascular and neurodegenerative lesions.

Discrimination of Alzheimer's disease and normal aging by EEG data

1997 ◽  
Vol 103 (2) ◽  
pp. 241-248 ◽  
Author(s):  
C. Besthorn ◽  
R. Zerfass ◽  
C. Geiger-Kabisch ◽  
H. Sattel ◽  
S. Daniel ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Normal Aging ◽  
Eeg Data

Diagnostic and Prognostic Value of the Combination of Two Measures of Verbal Memory in Mild Cognitive Impairment due to Alzheimer’s Disease

2017 ◽  
Vol 58 (3) ◽  
pp. 909-918 ◽  
Author(s):  
Isabel Sala ◽  
Ignacio Illán-Gala ◽  
Daniel Alcolea ◽  
Ma Belén Sánchez-Saudinós ◽  
Sergio Andrés Salgado ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Verbal Memory ◽  
Prognostic Value ◽  
Two Measures ◽  
Diagnostic And Prognostic Value
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