PubMed Health⌕ Search
Home/Archive/PubMed

PubMed · 8446124

Determining heart-rate variability: comparing methodologies using computer simulations.

J D Harry·R Freeman
View original source ↗Export metadata ↓

Abstract

Heart rate variation due to respiration is a window onto autonomic nervous system function and many measures exist that quantify this variability. Computer-based simulations of 1-minute deep-breathing tests, along with common artifacts, were used to compare the most frequently used measures. We found distinct differences in the performance of the measures. Some measures (e.g., SDRR and MSSD) are strongly influenced by the underlying mean heart rate while others (e.g., Max-Min and SDHR) are not. All of the measures tested, except R, were influenced by both shifting mean heart rate and single-beat anomalies. R, on the other hand, is strongly influenced by test duration and breathing asynchronies. Sensitivity to phenomena unrelated to ANS function can reduce the diagnostic discrimination of these measures. We suggest simple improvements to the measures and discuss how some measures may be theoretically superior to others.

Explore related subjects

Computer SimulationElectrocardiographyElectromyographyHeart Function TestsHeart RateHumansModels, CardiovascularRespirationComparative StudyJournal Article

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

J D Harry, R Freeman. 1993. Determining heart-rate variability: comparing methodologies using computer simulations.. https://doi.org/10.1002/mus.880160305

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed2025

Generating correlated data for omics simulation.

Simulation of realistic omics data is a key input for benchmarking studies that help users obtain optimal computational pipelines. Omics data involves large numbers of measured features on each sample and these measures are generally correlated with each other. However, simulation too often ignores these correlations, perhaps due to computational and statistical hurdles of doing so. To alleviate this, we describe three approaches for generating omics-scale data with correlated measures which mimic real datasets. These approaches are all based on a Gaussian copula approach with a covariance matrix that decomposes into a diagonal part and a low-rank part. This decomposition allows for extremely efficient simulation, overcoming a hurdle for adoption of past methods. We use these approaches to demonstrate the importance of including correlation in two benchmarking applications. First, we show that variance of results from the popular DESeq2 method increases when dependence is included. Second, we demonstrate that CYCLOPS, a method for inferring circadian time of collection from transcriptomics, improves in performance when given gene-gene dependencies in some circumstances. We provide an R package, dependentsimr, that has efficient implementations of these methods and can generate dependent data with arbitrary marginal distributions, including discrete (binary, ordered categorical, Poisson, negative binomial), continuous (normal), or with an empirical distribution.

Computer Simulation↗
PubMed2017

Addressing current challenges in cancer immunotherapy with mathematical and computational modelling.

The goal of cancer immunotherapy is to boost a patient's immune response to a tumour. Yet, the design of an effective immunotherapy is complicated by various factors, including a potentially immunosuppressive tumour microenvironment, immune-modulating effects of conventional treatments and therapy-related toxicities. These complexities can be incorporated into mathematical and computational models of cancer immunotherapy that can then be used to aid in rational therapy design. In this review, we survey modelling approaches under the umbrella of the major challenges facing immunotherapy development, which encompass tumour classification, optimal treatment scheduling and combination therapy design. Although overlapping, each challenge has presented unique opportunities for modellers to make contributions using analytical and numerical analysis of model outcomes, as well as optimization algorithms. We discuss several examples of models that have grown in complexity as more biological information has become available, showcasing how model development is a dynamic process interlinked with the rapid advances in tumour-immune biology. We conclude the review with recommendations for modellers both with respect to methodology and biological direction that might help keep modellers at the forefront of cancer immunotherapy development.

Computer Simulation↗
PubMed1998

Composite hip prosthesis design. II. Simulation.

An investigation was performed to study the mechanical performance of fiber-reinforced composite hip prostheses. In Part I of the study, a three-dimensional finite element code was developed for analyzing a composite hip prosthesis in a femur. The material properties of the composite were treated as anisotropic and inhomogeneous while the properties of the femoral bone were treated as anisotropic and homogeneous. All the materials were assumed to behave linear-elastically. Thermoplastic graphite/PEEK material was selected for the study. No slippage was assumed at the interface between the implant and the surrounding femoral bone. In Part II, numerical simulations were performed using the code to study the performance of a composite prosthesis in the femur. The stress/strain distributions, micromotions, and strain energy density of the surrounding femoral bone were evaluated and found to be related to initial fixation and long-term stability of the prosthesis in the femur. Numerous fiber orientations were studied, and the results of the calculations were compared with those generated from a prosthesis made of cobalt chrome and Ti-6Al-4V titanium alloys. Based on the analysis, it was shown that compared to conventional metallic implants more favorable stresses and deformations could be generated in the femur using composite implants. In addition, by changing fiber orientations according to femoral loads, a composite implant could be designed specifically for the left or the right femur.

Computer Simulation↗