2013
DOI: 10.1016/j.autneu.2013.05.004
|View full text |Cite
|
Sign up to set email alerts
|

Poincaré plot indexes of heart rate variability: Relationships with other nonlinear variables

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

6
131
0
5

Year Published

2014
2014
2019
2019

Publication Types

Select...
8
2

Relationship

2
8

Authors

Journals

citations
Cited by 167 publications
(142 citation statements)
references
References 40 publications
6
131
0
5
Order By: Relevance
“…For quantitative analysis of the plot an ellipse will be adjusted to the points of the graph where the indices SD1 and SD2 are obtained. SD1 is an instantaneous index of beat-to-beat variability and represents the parasympathetic modulation [24], while the SD2 Index represents the HRV in long-term records, and reflects the overall variability [24]. …”
Section: Methodsmentioning
confidence: 99%
“…For quantitative analysis of the plot an ellipse will be adjusted to the points of the graph where the indices SD1 and SD2 are obtained. SD1 is an instantaneous index of beat-to-beat variability and represents the parasympathetic modulation [24], while the SD2 Index represents the HRV in long-term records, and reflects the overall variability [24]. …”
Section: Methodsmentioning
confidence: 99%
“…Since the ANS is not a linear system, it has been argued that non-linear analysis would be informative for HRV (33) and nonlinear measures have also been proposed to be more accurate at predicting cardiac dysfunction, including ventricular tachycardia and sudden cardiac death (34, 35) when compared to traditional time and frequency domain analyses. Poincare analyses are commonly used as a nonlinear measures of HRV (36), including SD1, which represents a measure of rapid changes in R–R intervals. Because vagal effects on the sinus node are known to develop faster than sympathetically mediated effects, it is considered a parasympathetic index of sinus node control (37, 38).…”
Section: Methodsmentioning
confidence: 99%
“…23 Five time-domain parameters were computed from the PPIs: (1) the mean of the PPIs: representing the RR interval or the time interval between two consecutive R waves in the ECG, (2) the SD of PPIs: representing SDNN or the SD of the so-called normal-to-normal (NN) intervals, (3) the root mean square of the successive differences (RMSSD) between adjacent PPIs: representing RMSSD or the RMSSD between adjacent NN intervals and (4 and 5) two standard descriptors to evaluate non-linear short-term and long-term variability (SD1 and SD2) based on the Poincaré plot of PPIs. 24 For the frequency analysis, each segment of PPIs was resampled into an equivalent, uniformly spaced time series (sampling rate of 4 Hz), and the power spectral density was computed. The power in each frequency band was computed by integrating the area under the power spectral density curve bound by the band of interest: very low frequency (0.01-0.04 Hz), low frequency (LF; 0.04-0.15 Hz) and high frequency (HF; 0.15-0.4 Hz).…”
Section: Prv Characterisationmentioning
confidence: 99%