2019
DOI: 10.1371/journal.pone.0220692
|View full text |Cite
|
Sign up to set email alerts
|

Improving methodology in heart rate variability analysis for the premature infants: Impact of the time length

Abstract: Background Heart rate variability (HRV) has been emerging in neonatal medicine. It may help for the early diagnosis of pathology and estimation of autonomous maturation. There is a lack of standardization and automation in the selection of the sequences to analyze and some features have not been explored in this specific population. The main objective of this study was to analyze the impact of the time length of the sequences on the estimation of linear and non-linear HRV features, including horiz… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

0
16
0

Year Published

2020
2020
2023
2023

Publication Types

Select...
9

Relationship

3
6

Authors

Journals

citations
Cited by 22 publications
(21 citation statements)
references
References 43 publications
(53 reference statements)
0
16
0
Order By: Relevance
“…For HRV analysis in newborns, also nonlinear dynamicsbased methods, including Poincarè plot geometry, sequence plot, Approximate Entropy, SampEn Entropy, symbolic dynamics methods, and time irreversibility analysis were applied [26]. In addition to the traditional linear methods, such nonlinear methods can provide some additional information, i.e., the Poincaré plot geometry provides a graphical representation of the correlation of successive R-R intervals, the balance between short and long-term variability, and is related to rMSSD, while Approximate Entromy and SampEn entropy depend on autonomic nervous system activity with different mechanisms.…”
Section: Heart Rate Variabilitymentioning
confidence: 99%
“…For HRV analysis in newborns, also nonlinear dynamicsbased methods, including Poincarè plot geometry, sequence plot, Approximate Entropy, SampEn Entropy, symbolic dynamics methods, and time irreversibility analysis were applied [26]. In addition to the traditional linear methods, such nonlinear methods can provide some additional information, i.e., the Poincaré plot geometry provides a graphical representation of the correlation of successive R-R intervals, the balance between short and long-term variability, and is related to rMSSD, while Approximate Entromy and SampEn entropy depend on autonomic nervous system activity with different mechanisms.…”
Section: Heart Rate Variabilitymentioning
confidence: 99%
“…In addition, the search for new methods with better accuracy to further improve the early detection of LOS led us to use HRV analyses based on the representations of the horizontal and vertical visibility networks (Lacasa et al, 2008;Madl, 2016;Nguyen Phuc Thu et al, 2019). Analysis of the horizontal and vertical visibility graphs allow for simultaneously assessing periodicity, fractality, and discontinuity properties of RR time series, hence providing novel global insight into HRV.…”
Section: Heart-rate and Respiratory-rate Variabilitymentioning
confidence: 99%
“…Studies have found that some of the features derived from the visibility graph analysis of the HRV time series have a weak correlation to the traditional features, both in adults [19] and infants [20], which suggest that these features might add complementary information to HRV analysis. One previous study used network analysis of heart rate and blood pressure as input features, alongside multiscale entropy features and clinical measurements from the patients' electronic medical record, for a machine learning algorithm (MLA) that successfully predicted sepsis in adults, achieving an AUROC of 80% on the test population; an improvement of 7% over the AUROC obtained by their model trained on only the multiscale entropy features and clinical measurements [21].…”
Section: Introductionmentioning
confidence: 99%