Document Detail


A method for analyzing temporal patterns of variability of a time series from Poincare plots.
MedLine Citation:
PMID:  22556398     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
The Poincaré plot is a popular two-dimensional, time series analysis tool because of its intuitive display of dynamic system behavior. Poincaré plots have been used to visualize heart rate and respiratory pattern variabilities. However, conventional quantitative analysis relies primarily on statistical measurements of the cumulative distribution of points, making it difficult to interpret irregular or complex plots. Moreover, the plots are constructed to reflect highly correlated regions of the time series, reducing the amount of nonlinear information that is presented and thereby hiding potentially relevant features. We propose temporal Poincaré variability (TPV), a novel analysis methodology that uses standard techniques to quantify the temporal distribution of points and to detect nonlinear sources responsible for physiological variability. In addition, the analysis is applied across multiple time delays, yielding a richer insight into system dynamics than the traditional circle return plot. The method is applied to data sets of R-R intervals and to synthetic point process data extracted from the Lorenz time series. The results demonstrate that TPV complements the traditional analysis and can be applied more generally, including Poincaré plots with multiple clusters, and more consistently than the conventional measures and can address questions regarding potential structure underlying the variability of a data set.
Authors:
Mikkel Fishman; Frank J Jacono; Soojin Park; Reza Jamasebi; Anurak Thungtong; Kenneth A Loparo; Thomas E Dick
Related Documents :
22399958 - 3d digital surveying and modelling of cave geometry: application to paleolithic rock art.
22417868 - Prediction of the mechanical response of the femur with uncertain elastic properties.
21646148 - Nectar traits in nicotiana section alatae (solanaceae) in relation to floral traits, po...
21603108 - Pymc: bayesian stochastic modelling in python.
10783778 - Estimating data transformations in nonlinear mixed effects models.
9132908 - A longitudinal study of emergency room visits and air pollution for prince george, brit...
Publication Detail:
Type:  Journal Article; Research Support, N.I.H., Extramural; Research Support, U.S. Gov't, Non-P.H.S.     Date:  2012-05-03
Journal Detail:
Title:  Journal of applied physiology (Bethesda, Md. : 1985)     Volume:  113     ISSN:  1522-1601     ISO Abbreviation:  J. Appl. Physiol.     Publication Date:  2012 Jul 
Date Detail:
Created Date:  2012-07-20     Completed Date:  2012-12-18     Revised Date:  2013-09-26    
Medline Journal Info:
Nlm Unique ID:  8502536     Medline TA:  J Appl Physiol (1985)     Country:  United States    
Other Details:
Languages:  eng     Pagination:  297-306     Citation Subset:  IM    
Affiliation:
Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, Case Western Reserve University, Cleveland, OH 44106, USA.
Export Citation:
APA/MLA Format     Download EndNote     Download BibTex
MeSH Terms
Descriptor/Qualifier:
Algorithms*
Animals
Computer Simulation
Data Interpretation, Statistical*
Humans
Models, Biological*
Models, Statistical*
Sensitivity and Specificity
Signal Processing, Computer-Assisted*
Grant Support
ID/Acronym/Agency:
HL-080318/HL/NHLBI NIH HHS; HL-087377/HL/NHLBI NIH HHS; R01 NS069220/NS/NINDS NIH HHS
Comments/Corrections

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


Previous Document:  Does increased baseline ventilation heterogeneity following chest wall strapping predispose to airwa...
Next Document:  Effects of phenylephrine on cardiac output and venous return depend on the position of the heart on ...