Article ID Journal Published Year Pages File Type
10127187 Biomedical Signal Processing and Control 2019 5 Pages PDF
Abstract
The autocorrelation of the heart rate variability is presented by various methods and models, but Poincaré plots remain valuable analytic tools. Heart rate asymmetry analysis (HRA) is used for the quantification of unevenly distributed points above and below the line of identity. The aim of this work is to implement HRA analysis in newborns, to use it as a marker for acute stress. Forty healthy term newborn infants were included in the study. The protocol included two baseline phases, and two stress phases (heel stimulation and heel stick blood sampling), during which the heart rate was measured. Additionally, to the standard HRA indices, a new index (SKG) related to the first differences of the RR interval time series is introduced. A ROC curve analysis was applied to test the diagnostic properties of the asymmetry indices. With AUC significantly different from 0.5, the results show that HRA indices may be used as clinical markers. With higher AUC values (0.906 and 0.785), accuracy (87.5% and 81.3%) and sensitivity (87.5% and 81.3%), the SKG index outperformed the traditional indices. This novel application of HRA shows potential benefit in stress assessment of newborns, and in nonverbal patients in general.
Related Topics
Physical Sciences and Engineering Computer Science Signal Processing
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