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 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.

The Value of Quantitative EEG Measures in the Early Diagnosis of Alzheimer’s Disease

2011 ◽  
pp. 132-140
Author(s):  
Hideaki Tanaka
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Quantitative Eeg ◽  
Frequency Resolution ◽  
Parahippocampal Gyrus ◽  
Data Sets ◽  
Electromagnetic Tomography ◽  
Eyes Closed ◽  
Eeg Data ◽  
Z Value

There is growing interest in the discovery of clinically useful, robust biomarkers for Alzheimer’s disease (AD) and pre-AD; the ability to accurately diagnose AD or to predict conversion from a preclinical state to AD would aid in both prevention and early intervention. This study aimed to evaluate the usefulness of a statistical assessment of cortical activity using electroencephalograms (EEGs) with normative data and the ability of such an assessment to contribute to the diagnosis of AD. 15 patients with AD and 8 patients with mild cognitive impairment (MCI) were studied. Eyes-closed resting EEGs were digitally recorded at 200 Hz from 20 electrodes placed according to the international 10/20 system on the scalp, and 20 artifact-free EEG epochs lasting 2.56 ms were selected. Each EEG epoch was down-sampled to 100 Hz and matched to the normal data sets. The selected EEGs from each subject were analyzed by standardized Low Resolution Electromagnetic Tomography (sLORETA) and statistically compared with the age-matched normal data sets at all frequencies. This procedure resulted in cortical z values for each EEG frequency with 0.39 Hz frequency resolution for each subject. Some of the AD and MCI patients presented a peak of negative z value around 20 Hz, revealing hypoactivity of the parahippocampal gyrus and the insula in the sLORETA cortical image. In addition, severe cases of AD showed decreased parietal activation. These results were in agreement with evidence from statistical neuroimaging using MRI/SPECT. Submission of normal EEG data sets to sLORETA might be useful for the detection of diagnostic and predictive markers of AD and MCI in individual patients.

EEG Stochastic Nonlinear Oscillator Models for Alzheimer’s Disease

2015 ◽  
Author(s):  
Parham Ghorbanian ◽  
Subramanian Ramakrishnan ◽  
Hashem Ashrafiuon
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Nonlinear Oscillator ◽  
Model Performance ◽  
Model Parameters ◽  
Decision Variable ◽  
Eeg Signals ◽  
Brain Disorder ◽  
Eyes Closed ◽  
Oscillator Models

In this article, we derive unique stochastic nonlinear coupled oscillator models of EEG signals from an Alzheimer’s Disease (AD) study. EEG signals recorded during resting eyes-open (EO) and eyes-closed (EC) conditions in a pilot study with AD patients and age-matched healthy control subjects (CTL) are employed. An optimization scheme is then utilized to match the output of the stochastic Duffing - van der Pol double oscillator network with EEG signals recorded during each condition for AD and CTL subjects. The selected decision variable are the model parameters and noise intensity. While, the selected signal characteristics are power spectral densities in major brain frequency bands and Shannon and sample entropies to match the signal information content and complexity. It is shown that statistically significant unique models represent the EC and EO conditions for both CTL and AD subjects. Moreover, the inclusion of sample entropy in the optimization process significantly enhances the stochastic nonlinear oscillator model performance. The study suggests that EEG signals recorded under different brain states as well as those belonging to a brain disorder such as Alzheimer’s disease can be uniquely represented by stochastic nonlinear oscillators paving the way for identification of new discriminants.

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.

The Value of Quantitative EEG Measures in the Early Diagnosis of Alzheimer’s Disease

2012 ◽  
Vol 3 (4) ◽  
pp. 1-10
Author(s):  
Hideaki Tanaka
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Normative Data ◽  
Quantitative Eeg ◽  
Frequency Resolution ◽  
Parahippocampal Gyrus ◽  
Data Sets ◽  
Electromagnetic Tomography ◽  
Eyes Closed ◽  
Eeg Data

There is growing interest in the discovery of clinically useful, robust biomarkers for Alzheimer’s disease (AD) and pre-AD; the ability to accurately diagnose AD or to predict conversion from a preclinical state to AD would aid in both prevention and early intervention. This study aimed to evaluate the usefulness of a statistical assessment of cortical activity using electroencephalograms (EEGs) with normative data and the ability of such an assessment to contribute to the diagnosis of AD. 15 patients with AD and 8 patients with mild cognitive impairment (MCI) were studied. Eyes-closed resting EEGs were digitally recorded at 200 Hz from 20 electrodes placed according to the international 10/20 system on the scalp, and 20 artifact-free EEG epochs lasting 2.56 ms were selected. Each EEG epoch was down-sampled to 100 Hz and matched to the normal data sets. The selected EEGs from each subject were analyzed by standardized Low Resolution Electromagnetic Tomography (sLORETA) and statistically compared with the age-matched normal data sets at all frequencies. This procedure resulted in cortical z values for each EEG frequency with 0.39 Hz frequency resolution for each subject. Some of the AD and MCI patients presented a peak of negative z value around 20 Hz, revealing hypoactivity of the parahippocampal gyrus and the insula in the sLORETA cortical image. In addition, severe cases of AD showed decreased parietal activation. These results were in agreement with evidence from statistical neuroimaging using MRI/SPECT. Submission of normal EEG data sets to sLORETA might be useful for the detection of diagnostic and predictive markers of AD and MCI in individual patients.

scholarly journals Research on EEG Features in Different Emotional States

2021 ◽  
Author(s):  
Xiaotian Wang ◽  
Zuo Wang ◽  
Jiawei Guo ◽  
Chunying Pang ◽  
Jikui Liu
Keyword(s):  
Frequency Domain ◽  
Time Domain ◽  
Reference Group ◽  
Neurological Diseases ◽  
Eysenck Personality Questionnaire ◽  
Emotional States ◽  
Eeg Signals ◽  
Time Frequency ◽  
Eyes Closed ◽  
Eeg Features

Abstract In order to improve the detection and treatment of neurological diseases effectively, it is a significant means to analysis EEG features. In this study, extrovert and stable persons were selected as the subjects according to the Eysenck Personality Questionnaire. Then set the subjects’ EEG signals in a quiet state with eyes closed as a reference group. Four types of pure music were selected as stimulus materials to induce four different kinds of emotions: pleasure, sadness, irritability, and fear. During the period, evoked EEG signals was acquired. Then, some signal processing methods were used to de-noise for EEG and separate EOG artifacts from EEG signals. Finally, EEG signals’ features in time domain, frequency domain and time-frequency domain were extracted, especially the method which combined Hilbert transform based on EMD with information entropy to calculate EEG signals’ Hilbert spectrum entropy for four emotional states. The results showed that EEG signals’ features in different emotional states changed with gender, brain and mood objectively, all differences mainly reflected in time domain features, frequency domain features and time-frequency domain features. All the results reveal that EEG signals’ variation characteristics in the process of auditory stimulation, and can be an adjustment basis for detection and treatment of neurological diseases.

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 Phenotyping Neuropsychiatric Symptoms Profiles of Alzheimer’s Disease Using Cluster Analysis on EEG Power

2021 ◽  
Vol 13 ◽  
Author(s):  
Friedrich Liu ◽  
Jong-Ling Fuh ◽  
Chung-Kang Peng ◽  
Albert C. Yang
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cluster Analysis ◽  
Psychological Symptoms ◽  
Neuropsychiatric Symptoms ◽  
Cost Effective ◽  
Eeg Signals ◽  
Clinical Dementia Rating ◽  
Frequency Bands ◽  
Eeg Power

Background: There has been an increasing interest in studying electroencephalogram (EEG) as a biomarker of Alzheimer’s disease but the association between EEG signals and patients’ neuropsychiatric symptoms remains unclear. We studied EEG signals of patients with Alzheimer’s disease to explore the associations between patients’ neuropsychiatric symptoms and clusters of patients based on their EEG powers.Methods: A total of 69 patients with mild Alzheimer’s disease (the Clinical Dementia Rating = 1) were enrolled and their EEG signals from 19 channels/electrodes were recorded in three sessions for each patient. The EEG power was calculated by Fourier transform for the four frequency bands (beta: 13–40 Hz, alpha: 8–13 Hz, theta: 4–8 Hz, and delta: <4 Hz). We performed K-means cluster analysis to classify the 69 patients into two distinct groups by the log-transformed EEG powers (4 frequency bands × 19 channels) for the three EEG sessions. In each session, both clusters were compared with each other to assess the differences in their behavioral/psychological symptoms in terms of the Neuropsychiatric Inventory (NPI) score.Results: While EEG band powers were highly consistent across all three sessions before clustering, EEG band powers were different between the two clusters in each session, especially for the delta waves. The delta band powers differed significantly between the two clusters in most channels across the three sessions. Patients’ demographics and cognitive function were not different between both clusters. However, their behavioral/psychological symptoms were different between the two clusters classified based on EEG powers. A higher NPI score was associated with the clustering of higher EEG powers.Conclusion: The present study suggests that EEG power correlates to behavioral and psychological symptoms among patients with mild Alzheimer’s disease. The clustering approach of EEG signals may provide a novel and cost-effective method to differentiate the severity of neuropsychiatric symptoms and/or predict the prognosis for Alzheimer’s patients.

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.

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