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

ACCESS DATA

Publications

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

Similar Datasets

Project description:Background and purposePrevious studies have focused on the changes of dynamic and static functional connections in cerebral small vessel disease (CSVD). However, the dynamic characteristics of local brain activity are poorly understood. The purpose of this study was to investigate the dynamic cerebral activity changes in patients with CSVD using the dynamic amplitude of low-frequency fluctuation (d-ALFF).MethodsA total of 104 CSVD patients with cognitive impairment (CSVD-CI, n = 52) or normal cognition (CSVD-NC, n = 52) and 63 matched healthy controls (HCs) were included in this study. Every participant underwent magnetic resonance imaging scans and a battery of neuropsychological examinations. The dynamics of spontaneous brain activity were assessed using dynamic changes in the amplitude of low-frequency fluctuation (ALFF) with the sliding-window method. We used voxel-wise one-way analysis of variance (ANOVA) to compare dynamic ALFF variability among the three groups. Post-hoc t-tests were used to evaluate differences between each group pair. Finally, the brain regions with d-ALFF values with differences between CSVD subgroups were taken as regions of interest (ROI), and the d-ALFF values corresponding to the ROI were extracted for partial correlation analysis with memory.Results(1) There was no significant difference in age (p = 0.120), sex (p = 0.673) and education (p = 0.067) among CSVD-CI, CSVD-NC and HC groups, but there were significant differences Prevalence of hypertension and diabetes mellitus among the three groups (p < 10-3). There were significant differences in scores of several neuropsychological scales among the three groups (p < 10-3). (2) ANOVA and post-hoc t-test showed that there were dynamic abnormalities of spontaneous activity in several brain regions in three groups, mainly located in bilateral parahippocampal gyrus and bilateral hippocampus, bilateral insular and frontal lobes, and the static activity abnormalities in bilateral parahippocampal gyrus and bilateral hippocampal regions were observed at the same time, suggesting that bilateral parahippocampal gyrus and bilateral hippocampus may be the key brain regions for cognitive impairment caused by CSVD. (3) The correlation showed that d-ALFF in the bilateral insular was slightly correlated with the Mini-Mental State Examination (MMSE) score and disease progression rate. The d-ALFF value of the left postcentral gyrus was negatively correlated with the Clock Drawing Test (CDT) score (r = -0.416, p = 0.004), and the d-ALFF value of the right postcentral gyrus was negatively correlated with the Rey's Auditory Verbal Learning Test (RAVLT) word recognition (r = -0.320, p = 0.028).ConclusionThere is a wide range of dynamic abnormalities of spontaneous brain activity in patients with CSVD, in which the abnormalities of this activity in specific brain regions are related to memory and execution or emotion.

Dataset Information

Amplitude- and Fluctuation-Based Dispersion Entropy.

Publications

Amplitude- and Fluctuation-Based Dispersion Entropy.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets