Dataset Information

Amplitude- and Fluctuation-Based Dispersion Entropy.

ABSTRACT: Dispersion entropy (DispEn) is a recently introduced entropy metric to quantify the uncertainty of time series. It is fast and, so far, it has demonstrated very good performance in the characterisation of time series. It includes a mapping step, but the effect of different mappings has not been studied yet. Here, we investigate the effect of linear and nonlinear mapping approaches in DispEn. We also inspect the sensitivity of different parameters of DispEn to noise. Moreover, we develop fluctuation-based DispEn (FDispEn) as a measure to deal with only the fluctuations of time series. Furthermore, the original and fluctuation-based forbidden dispersion patterns are introduced to discriminate deterministic from stochastic time series. Finally, we compare the performance of DispEn, FDispEn, permutation entropy, sample entropy, and Lempel-Ziv complexity on two physiological datasets. The results show that DispEn is the most consistent technique to distinguish various dynamics of the biomedical signals. Due to their advantages over existing entropy methods, DispEn and FDispEn are expected to be broadly used for the characterization of a wide variety of real-world time series. The MATLAB codes used in this paper are freely available at http://dx.doi.org/10.7488/ds/2326.

SUBMITTER: Azami H

PROVIDER: S-EPMC7512725 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature

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Amplitude- and Fluctuation-Based Dispersion Entropy.

Azami Hamed H Escudero Javier J

Entropy (Basel, Switzerland) 20180320 3

Dispersion entropy (DispEn) is a recently introduced entropy metric to quantify the uncertainty of time series. It is fast and, so far, it has demonstrated very good performance in the characterisation of time series. It includes a mapping step, but the effect of different mappings has not been studied yet. Here, we investigate the effect of linear and nonlinear mapping approaches in DispEn. We also inspect the sensitivity of different parameters of DispEn to noise. Moreover, we develop fluctuat ...[more]

PMID: 33265301

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Project description:Background: Several vascular risk factors, including hypertension, diabetes, body mass index, and smoking status are found to be associated with cognitive decline and the risk of Alzheimer's disease (AD). We aimed to investigate whether an aggregation of vascular risk factors modulates the amplitude of low-frequency fluctuation (ALFF) in patients with mild cognitive impairment (MCI). Methods: Forty-three MCI patients and twenty-nine healthy controls (HCs) underwent resting-state functional MRI scans, and spontaneous brain activity was measured by the ALFF technique. The vascular risk profile was represented with the Framingham Heart Study general cardiovascular disease (FHS-CVD) risk score, and each group was further divided into high and low risk subgroups. Two-way ANOVA was performed to explore the main effects of diagnosis and vascular risk and their interaction on ALFF. Results: The main effect of diagnosis on ALFF was found in left middle temporal gyrus (LMTG) and left superior parietal gyrus (LSPG), and the main effect of risk on ALFF was detected in left fusiform gyrus (LFFG), left precuneus (LPCUN), and left cerebellum posterior lobe (LCPL). Patients with MCI exhibited increased ALFF in the LMTG and LSPG than HCs, and participants with high vascular risk showed increased ALFF in the LFFG and LCPL, while decreased ALFF in the LPCUN. An interaction between diagnosis (MCI vs. HC) and FHS-CVD risk (high vs. low) regarding ALFF was observed in the left hippocampus (LHIP). HCs with high vascular risk showed significantly increased ALFF in the LHIP than those with low vascular risk, while MCI patients with high vascular risk showed decreased ALFF in the LHIP than HCs with high vascular risk. Interestingly, the mean ALFF of LHIP positively correlated with word recall test in HCs with high vascular risk (rho = 0.630, P = 0.016), while negatively correlated with the same test in MCI patients with high vascular risk (rho = -0.607, P = 0.001). Conclusions: This study provides preliminary evidence highlighting that the aggregation of vascular risk factors modulates the spontaneous brain activity in MCI patients, and this may serve as a potential imaging mechanism underlying vascular contribution to AD.

Dataset Information

Amplitude- and Fluctuation-Based Dispersion Entropy.

Publications

Amplitude- and Fluctuation-Based Dispersion Entropy.

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