Dataset Information

Are Complexity Metrics Reliable in Assessing HRV Control in Obese Patients During Sleep?

ABSTRACT: Obesity is associated with cardiovascular mortality. Linear methods, including time domain and frequency domain analysis, are normally applied on the heart rate variability (HRV) signal to investigate autonomic cardiovascular control, whose imbalance might promote cardiovascular disease in these patients. However, given the cardiac activity non-linearities, non-linear methods might provide better insight. HRV complexity was hereby analyzed during wakefulness and different sleep stages in healthy and obese subjects. Given the short duration of each sleep stage, complexity measures, normally extracted from long-period signals, needed be calculated on short-term signals. Sample entropy, Lempel-Ziv complexity and detrended fluctuation analysis were evaluated and results showed no significant differences among the values calculated over ten-minute signals and longer durations, confirming the reliability of such analysis when performed on short-term signals. Complexity parameters were extracted from ten-minute signal portions selected during wakefulness and different sleep stages on HRV signals obtained from eighteen obese patients and twenty controls. The obese group presented significantly reduced complexity during light and deep sleep, suggesting a deficiency in the control mechanisms integration during these sleep stages. To our knowledge, this study reports for the first time on how the HRV complexity changes in obesity during wakefulness and sleep. Further investigation is needed to quantify altered HRV impact on cardiovascular mortality in obesity.

SUBMITTER: Cabiddu R

PROVIDER: S-EPMC4404104 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Are Complexity Metrics Reliable in Assessing HRV Control in Obese Patients During Sleep?

Cabiddu Ramona R Trimer Renata R Borghi-Silva Audrey A Migliorini Matteo M Mendes Renata G RG Oliveira Antonio D AD Costa Fernando S M FS Bianchi Anna M AM

PloS one 20150420 4

Obesity is associated with cardiovascular mortality. Linear methods, including time domain and frequency domain analysis, are normally applied on the heart rate variability (HRV) signal to investigate autonomic cardiovascular control, whose imbalance might promote cardiovascular disease in these patients. However, given the cardiac activity non-linearities, non-linear methods might provide better insight. HRV complexity was hereby analyzed during wakefulness and different sleep stages in healthy ...[more]

PMID: 25893856

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Project description:NeuroKit2 is a Python Toolbox for Neurophysiological Signal Processing. The presented method is an adaptation of NeuroKit2 to simplify and automate computation of the various mathematical estimates of heart rate variability (HRV) or similar time series. By default, the present approach accepts as input electrocardiogram's R-R intervals (RRIs) or peak times, i.e., timestamp of each consecutive R peak, but the RRIs or peak times can also stem from other biosensors such as photoplethysmography (PPGs) or represent more general kinds of biological or non-biological time series oscillations. The data may be derived from a single or several sources such as conventional univariate heart rate time series or intermittently weakly coupled fetal and maternal heart rate data. The method describes preprocessing and computation of an output of 124 HRV measures including measures with a dynamic, time-series-specific optimal time delay-based complexity estimation with a user-definable time window length. I also provide an additional layer of HRV estimation looking at the temporal fluctuations of the HRV estimates themselves, an approach not yet widely used in the field, yet showing promise (doi: 10.3389/fphys.2017.01112). To demonstrate the application of the methodology, I present an approach to studying the dynamic relationships between sleep state architecture and multi-dimensional HRV metrics in 31 subjects. NeuroKit2's documentation is extensive. Here, I attempted to simplify things summarizing all you need to produce the most extensive HRV estimation output available to date as open source and all in one place. The presented Jupyter notebooks allow the user to run HRV analyses quickly and at scale on univariate or multivariate time-series data. I gratefully acknowledge the excellent support from the NeuroKit team.•Univariate or multivariate time series input; ingestion, preprocessing, and computation of 124 HRV metrics.•Estimation of intra- and inter-individual higher order temporal fluctuations of HRV metrics.•Application to a sleep dataset recorded using Apple Watch and expert sleep labeling.

Dataset Information

Are Complexity Metrics Reliable in Assessing HRV Control in Obese Patients During Sleep?

Publications

Are Complexity Metrics Reliable in Assessing HRV Control in Obese Patients During Sleep?

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