Analysis of electroencephalographic signals complexity regarding Alzheimer's Disease

Tzimourta, Katerina D.; Afrantou, Theodora; Ioannidis, Panagiotis; Karatzikou, Maria; Tzallas, Alexandros T.; Γιαννακέας, Νικόλαος; Astrakas, Loukas G.; Angelidis, Pantelis; Glavas, Euripidis; Grigoriadis, Nikolaos; Tsalikakis, Dimitrios G.; Tsipouras, Markos G.

doi:10.1016/j.compeleceng.2019.03.018

Cited by 42 publications

(23 citation statements)

References 25 publications

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“…When the results were evaluated, it was seen that the patients in the study group were statistically higher than the control group (Table 1). These findings are consistent with previous studies 45,46,55,56 .…”

Section: Discussionsupporting

confidence: 94%

Investigation of the Levels of Blood MDA, GSH and Nitric Oxide Levels in Patients with Probable Alzheimer’s Disease

Güven¹,

Dalginli²,

Çulhaoğlu³

et al. 2020

Kafkas J Med Sci

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Aim:The main focus of this study is to determine blood malondialdehyde (MDA), Glutathione (GSH) and nitric oxide (NO) levels in patients with possible Alzheimer's disease (AD). Material andMethod: The study included 15 healthy persons and 15 Alzheimer's patients aged between 65 and 79 living in Kars. Before taking blood samples from patients who were diagnosed with Alzheimer at Kafkas University Faculty of Medicine Neurology Service, they were applied Standardized Mini Mental State Examination (SMMSE) and Clinical Dementia Rating. In the next step, erythrocyte GSH and serum MDA, NO levels were determined. Results: The difference between healthy persons and Alzheimer's disease group erythrocyte GSH (p<0.05) and serum MDA (p<0.01), NO (p<0.001) levels were statistically significant. MMSE scores were significantly lower than the control group (p<0.001). Conclusion: It was observed that AD caused lipid peroxidation and as a conclusion significantly increased the MDA, GSH and NO levels in the blood of Alzheimer patients. This study supports the theory that the brain is affected by increased oxidative stress in AD based on the data obtained.

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Section: Discussionsupporting

confidence: 94%

Investigation of the Levels of Blood MDA, GSH and Nitric Oxide Levels in Patients with Probable Alzheimer’s Disease

Güven¹,

Dalginli²,

Çulhaoğlu³

et al. 2020

Kafkas J Med Sci

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“…Research studies focus on evaluating specific EEG markers that provide a highly accurate discrimination of AD patients that are on different medication in order to assist neurologists in the adjustment of intervention plans in clinical trials [39,40,41]. The proposed study is an extend of our previous work [42] and investigated the ability of several statistical and spectral features to accurately discriminate AD patients with mild or moderate AD from healthy, age-matched subjects. Despite the good classification performance, improvements need to be done concerning the statistical significance of the results.…”

Section: Discussionmentioning

confidence: 99%

EEG Window Length Evaluation for the Detection of Alzheimer’s Disease over Different Brain Regions

et al. 2019

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Alzheimer’s Disease (AD) is a neurogenerative disorder and the most common type of dementia with a rapidly increasing world prevalence. In this paper, the ability of several statistical and spectral features to detect AD from electroencephalographic (EEG) recordings is evaluated. For this purpose, clinical EEG recordings from 14 patients with AD (8 with mild AD and 6 with moderate AD) and 10 healthy, age-matched individuals are analyzed. The EEG signals are initially segmented in nonoverlapping epochs of different lengths ranging from 5 s to 12 s. Then, a group of statistical and spectral features calculated for each EEG rhythm (δ, θ, α, β, and γ) are extracted, forming the feature vector that trained and tested a Random Forests classifier. Six classification problems are addressed, including the discrimination from whole-brain dynamics and separately from specific brain regions in order to highlight any alterations of the cortical regions. The results indicated a high accuracy ranging from 88.79% to 96.78% for whole-brain classification. Also, the classification accuracy was higher at the posterior and central regions than at the frontal area and the right side of temporal lobe for all classification problems.

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“…The complexity measurement algorithms and their applications are the current research hotspots in the field of nonlinear signal processing. It is widely used to evaluate the irregularities of time series obtained from various systems, such as EEG signals [1][2][3], ECG signals [4,5], walking stride interval signals [6], stock fluctuations [7] and weather prediction [8]. At the meantime, many researchers have conducted in-depth analysis on the complexity of chaotic systems [9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

A Modified Multivariable Complexity Measure Algorithm and Its Application for Identifying Mental Arithmetic Task

Ma¹,

He²,

Sun³

2021

Entropy

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Properly measuring the complexity of time series is an important issue. The permutation entropy (PE) is a widely used as an effective complexity measurement algorithm, but it is not suitable for the complexity description of multi-dimensional data. In this paper, in order to better measure the complexity of multi-dimensional time series, we proposed a modified multivariable PE (MMPE) algorithm with principal component analysis (PCA) dimensionality reduction, which is a new multi-dimensional time series complexity measurement algorithm. The analysis results of different chaotic systems verify that MMPE is effective. Moreover, we applied it to the comlexity analysis of EEG data. It shows that the person during mental arithmetic task has higher complexity comparing with the state before mental arithmetic task. In addition, we also discussed the necessity of the PCA dimensionality reduction.

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Analysis of electroencephalographic signals complexity regarding Alzheimer's Disease

Cited by 42 publications

References 25 publications

Investigation of the Levels of Blood MDA, GSH and Nitric Oxide Levels in Patients with Probable Alzheimer’s Disease

Investigation of the Levels of Blood MDA, GSH and Nitric Oxide Levels in Patients with Probable Alzheimer’s Disease

EEG Window Length Evaluation for the Detection of Alzheimer’s Disease over Different Brain Regions

A Modified Multivariable Complexity Measure Algorithm and Its Application for Identifying Mental Arithmetic Task

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