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Entropy Analysis of RR-Time Series From Stress Tests.

ABSTRACT: The RR-interval time series or tachograms obtained from electrocardiograms have been widely studied since they reflect the cardiac variability, and this is an indicative of the health status of a person. The tachogram can be seen as a highly non-linear and complex time series, and therefore, should be analyzed with non-linear techniques. In this work, several entropy measures, Sample Entropy (SampEn), Approximate Entropy (ApEn), and Fuzzy Entropy (FuzzyEn) are used as a measure of heart rate variability (HRV). Tachograms belonging to thirty-nine subjects were obtained from a cardiac stress test consisting of a rest period followed by a period of moderate physical activity. Subjects are grouped according to their physical activity using the IPAQ sedentary and active questionnaire, we work with youth and middle-aged adults. The entropy measures for each group show that for the sedentary subjects the values are high at rest and decrease appreciably with moderate physical activity, This happens for both young and middle-aged adults. These results are highly reproducible. In the case of the subjects that exercise regularly, an increase in entropy is observed or they tend to retain the entropy value that they had at rest. It seems that there is a possible correlation between the physical condition of a person with the increase or decrease in entropy during moderate physical activity with respect to the entropy at rest. It was also observed that entropy during longer physical activity tests tends to decrease as fatigue accumulates, but this decrease is small compared to the change that occurs when going from rest to physical activity.

SUBMITTER: Solis-Montufar EE

PROVIDER: S-EPMC7438833 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Entropy Analysis of RR-Time Series From Stress Tests.

Solís-Montufar Eric E EE Gálvez-Coyt Gonzalo G Muñoz-Diosdado Alejandro A

Frontiers in physiology 20200812

The RR-interval time series or tachograms obtained from electrocardiograms have been widely studied since they reflect the cardiac variability, and this is an indicative of the health status of a person. The tachogram can be seen as a highly non-linear and complex time series, and therefore, should be analyzed with non-linear techniques. In this work, several entropy measures, Sample Entropy (SampEn), Approximate Entropy (ApEn), and Fuzzy Entropy (FuzzyEn) are used as a measure of heart rate var ...[more]

PMID: 32903750

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Project description:BackgroundApproximate entropy (ApEn) and sample entropy (SampEn) have been previously used to quantify the regularity in centre of pressure (COP) time-series in different experimental groups and/or conditions. ApEn and SampEn are very sensitive to their input parameters: m (subseries length), r (tolerance) and N (data length). Yet, the effects of changing those parameters have been scarcely investigated in the analysis of COP time-series. This study aimed to investigate the effects of changing parameters m, r and N on ApEn and SampEn values in COP time-series, as well as the ability of these entropy measures to discriminate between groups.MethodsA public dataset of COP time-series was used. ApEn and SampEn were calculated for m = {2, 3, 4, 5}, r = {0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5} and N = {600, 1200} (30 and 60 s, respectively). Subjects were stratified in young adults (age < 60, n = 85), and older adults (age ≥ 60) with (n = 18) and without (n = 56) falls in the last year. The effects of changing parameters m, r and N on ApEn and SampEn were investigated with a three-way ANOVA. The ability of ApEn and SampEn to discriminate between groups was investigated with a mixed ANOVA (within-subject factors: m, r and N; between-subject factor: group). Specific combinations of m, r and N producing significant differences between groups were identified using the Tukey's honest significant difference procedure.ResultsA significant three-way interaction between m, r and N confirmed the sensitivity of ApEn and SampEn to the input parameters. SampEn showed a higher consistency and ability to discriminate between groups than ApEn. Significant differences between groups were mostly observed in longer (N = 1200) COP time-series in the anterior-posterior direction. Those differences were observed for specific combinations of m and r, highlighting the importance of an adequate selection of input parameters.ConclusionsFuture studies should favour SampEn over ApEn and longer time-series (≥ 60 s) over shorter ones (e.g. 30 s). The use of parameter combinations such as SampEn (m = {4, 5}, r = {0.25, 0.3, 0.35}) is recommended.

Project description:gene expression was measured in control and heat resistance selected adult female flies before and at 8 time points after heat stress for 1h @ 36 degrees Abstract The availability of full genome sequences has allowed the construction of microarrays, with which screening of the full genome for changes in gene expression is possible. This method can provide a wealth of information about biology at the level of gene expression and is a powerful method to identify genes and pathways involved in various processes. In this study, we report a detailed analysis of the full heat stress response in Drosophila melanogaster females, using whole genome gene expression arrays (Affymetrix Inc, Santa Clara, CA, USA). The study focuses on up- as well as downregulation of genes from just before and at 8 time points after an application of short heat hardening (368C for 1 hour). The expression changes were followed up to 64 hours after the heat stress, using 4 biological replicates. This study describes in detail the dramatic change in gene expression over time induced by a short-term heat treatment. We found both known stress responding genes and new candidate genes, and processes to be involved in the stress response. We identified 3 main groups of stress responsive genes that were early–upregulated, early– downregulated, and late–upregulated, respectively, among 1222 differentially expressed genes in the data set. Comparisons with stress sensitive genes identified by studies of responses to other types of stress allow the discussion of heat-specific and general stress responses in Drosophila. Several unexpected features were revealed by this analysis, which suggests that novel pathways and mechanisms are involved in the responses to heat stress and to stress in general. The majority of stress responsive genes identified in this and other studies were downregulated, and the degree of overlap among downregulated genes was relatively high, whereas genes responding by upregulation to heat and other stress factors were more specific to the stress applied or to the conditions of the particular study. As an expected exception, heat shock genes were generally found to be upregulated by stress in general. Keywords: Time series

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Entropy Analysis of RR-Time Series From Stress Tests.

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Entropy Analysis of RR-Time Series From Stress Tests.

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