DCCA cross-correlation coefficients reveals the change of both synchronization and oscillation in EEG of Alzheimer disease patients

Chen, Yingyuan; Cai, Lihui; Wang, Ruofan; Song, Zhenxi; Deng, Bin; Wang, Jiang; Yu, Haitao

doi:10.1016/j.physa.2017.08.009

Cited by 38 publications

(25 citation statements)

References 65 publications

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“…Finally, we summarize and draw some general conclusions. In view of the abovementioned interdisciplinary research by other authors [22,24,25,[27][28][29][30], through the conclusions from our present work, we would like also to support advantages of multifractal detrended cross-correlation method and its wide applications to study any time series with nonlinear correlations, not only in the foreign exchange market but also across other fields of pure and applied sciences.…”

Section: Introductionsupporting

confidence: 51%

“…We would like to emphasize that our method based on detrended cross-correlation analysis is quite novel and only recently a plethora of applications started to emerge across many fields of nonlinear correlations studies, including meteorological data [22], electricity spot market [23], effects of weather on agricultural market [24], stock markets [25], cryptocurrency markets [26], electroencephalography (EEG) signals [27], electrocardiography (ECG) and arterial blood pressure [28] as well as air pollution [29,30]. Such wide interest across different fields of research in application of detrended cross-correlation analysis to nonlinear time series studies serves as an additional strong motivation for elucidating such analysis in terms of its potential and limitations.…”

Section: Introductionmentioning

confidence: 99%

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Detecting correlations and triangular arbitrage opportunities in the Forex by means of multifractal detrended cross-correlations analysis

et al. 2019

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Multifractal detrended cross-correlation methodology is described and applied to Foreign exchange (Forex) market time series. Fluctuations of high-frequency exchange rates of eight major world currencies over 2010-2018 period are used to study cross-correlations. The study is motivated by fundamental questions in complex systems' response to significant environmental changes and by potential applications in investment strategies, including detecting triangular arbitrage opportunities. Dominant multiscale cross-correlations between the exchange rates are found to typically occur at smaller fluctuation levels. However, hierarchical organization of ties expressed in terms of dendrograms, with a novel application of the

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Section: Introductionsupporting

confidence: 51%

Section: Introductionmentioning

confidence: 99%

Detecting correlations and triangular arbitrage opportunities in the Forex by means of multifractal detrended cross-correlations analysis

et al. 2019

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“…Support Vector Machines (SVM) were trained and tested with PME and obtained a 90.60% classification accuracy for the CN/AD problem. For the same classification problem, a Linear Discriminant Analysis classifier in a study [13] indicated a 90% accuracy with a maximum detrended cross-correlation coefficient when the C3-P3 channels were used as the input.…”

Section: Discussionmentioning

confidence: 99%

“…Several studies have been proposed aimed at finding a correlation between the MMSE score and EEG features [7,8,9] or discriminating AD patients from patients with other neurological conditions through their EEG findings. In particular, methods have been proposed for the automated discrimination of AD patients from healthy elderly subjects [10,11,12,13,14,15,16], frontotemporal dementia [17], vascular dementia [18], Mild Cognitive Impairment ( MCI ) [19,20], or even epilepsy [21]. Generally, the EEG activity is analyzed from each electrode site [6,22] or from electrode clusters [7,8,23].…”

Section: Introductionmentioning

confidence: 99%

“…The features are extracted directly from raw EEG segments [10,12,15,19,20] or after a signal decomposition with a Wavelet Analysis [25,27], Power Spectral Density using Berg’s method [28], Hilbert–Huang Transform [10], or Multivariate Multiscale Analysis [11]. Concerning the epoch duration in which the signal is segmented, there is no common agreement regarding the appropriate window length and there is a diversity among research studies [10,11,12,13,14,15,16,17,18,19,20,21,22,24,29]. According to the literature, the EEG window length is usually selected between 5 s to 12 s arbitrarily or based on literature survey.…”

Section: Introductionmentioning

confidence: 99%

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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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Response of cross-correlations between high PM2.5 and O3 with increasing time scales to the COVID-19: different trends in BTH and PRD

Bao

Liu

et al. 2023

Environ Monit Assess

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The air pollution in China currently is characterized by high fine particulate matter (PM 2.5 ) and ozone (O 3 ) concentrations. Compared with single high pollution events, such double high pollution (DHP) events (both PM 2.5 and O 3 are above the National Ambient Air Quality Standards (NAAQS)) pose a greater threat to public health and environment. In 2020, the outbreak of COVID-19 provided a special time window to further understand the cross-correlation between PM 2.5 and O 3 . Based on this background, a novel detrended cross-correlation analysis (DCCA) based on maximum time series of variable time scales (VM-DCCA) method is established in this paper to compare the cross-correlation between high PM 2.5 and O 3 in Beijing-Tianjin-Heibei (BTH) and Pearl River Delta (PRD). At first, the results show that PM 2.5 decreased while O 3 increased in most cities due to the effect of COVID-19, and the increase in O 3 is more significant in PRD than in BTH. Secondly, through DCCA, the results show that the PM 2.5 -O 3 DCCA exponents α decrease by an average of 4.40% and 2.35% in BTH and PRD respectively during COVID-19 period compared with non-COVID-19 period. Further, through VM-DCCA, the results show that the PM 2.5 -O 3 VM-DCCA exponents in PRD weaken rapidly with the increase of time scales, with decline range of about 23.53% and 22.90% during the non-COVID-19 period and COVID-19 period respectively at 28-h time scale. BTH is completely different. Without significant tendency, its is always higher than that in PRD at different time scales. Finally, we explain the above results with the self-organized criticality (SOC) theory. The impact of meteorological conditions and atmospheric oxidation capacity (AOC) variation during the COVID-19 period on SOC state are further discussed. The results show that the characteristics of cross-correlation between high PM 2.5 and O 3 are the manifestation of the SOC theory of atmospheric system. Relevant conclusions are important for the establishment of regionally targeted PM 2.5 -O 3 DHP coordinated control strategies.

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DCCA cross-correlation coefficients reveals the change of both synchronization and oscillation in EEG of Alzheimer disease patients

Cited by 38 publications

References 65 publications

Detecting correlations and triangular arbitrage opportunities in the Forex by means of multifractal detrended cross-correlations analysis

Detecting correlations and triangular arbitrage opportunities in the Forex by means of multifractal detrended cross-correlations analysis

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

Response of cross-correlations between high PM2.5 and O3 with increasing time scales to the COVID-19: different trends in BTH and PRD

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