Heart Rate Variability In Newborns Research Articles

The Effect of Caffeine on Heart Rate Variability in Newborns: A Pilot Study.

Neonatal apnoea can be treated with caffeine, which affects the central nervous and cardiovascular systems. Heart rate variability (HRV) reflects the activity of the autonomic nervous system (ANS) and might be used as a measure of ANS maturation in newborns. We aimed to establish the effect of caffeine on HRV in newborns and investigated the potential correlation between HRV and postmenstrual age (PMA). In 25 haemodynamically stable newborns hospitalized due to apnoea and treated with caffeine (2.5 mg/kg), we assessed breathing frequency, arterial oxygen saturation, body temperature, and the heart rate while they were sleeping. We assessed HRV by spectral analysis using fast Fourier transformation. The same protocol was reapplied 100 h after caffeine withdrawal to assess the control parameters. Caffeine increased breathing frequency (p = 0.023) but did not affect any other parameter assessed including HRV. We established a positive correlation between postmenstrual age and HRV during treatment with caffeine as well as after caffeine had been withdrawn (total power: p = 0.044; low-frequency band: p = 0.039). Apparently, the maintenance dose of caffeine is too low to affect the heart rate and HRV. A positive correlation between PMA and HRV might reflect maturation of the ANS, irrespective of caffeine treatment.

Heart rate variability in newborns with cardiorespiratory failure

Heart rate variability in newborns with cardiorespiratory failure

The effect of sleeping position on heart rate variability in newborns

BackgroundLower heart rate variability (HRV) in a newborn might represent a risk factor for unfavourable outcome, a longer recovery after illness, and a sudden infant death. Our aim was to determine whether the newborn’s sleeping position is associated with HRV.MethodsWe performed a prospective clinical study in 46 hospitalized cardiorespiratory stable term newborns. During sleeping, we measured the parameters of HRV in four body positions (supine, supine with tilt, prone, prone with tilt).ResultsThe TP (total power spectral density) was significantly higher when lying supine in comparison to prone (p = 0,048) and to prone with tilt (p = 0,046). The HF (high frequency of power spectral density) in the supine position without tilt tended to be higher compared to prone without tilt (p > 0,05). The LF (low frequency power) was significantly higher when lying supine compared to prone, both without tilt (p = 0,018). TP and HF showed a positive correlation with gestational but not postmenstrual age (p = 0.044 and p = 0.036, respectively).ConclusionsIn term newborns, sleeping position is associated with HRV. Higher TP and HF were found in the supine position, which might reflect better ANS stability. Gestational age positively correlated with TP and HF power, but only in supine position.Trial registrationISRCTN11702082, date of registration: March, 13th, 2020; retrospectively registered.

A method to assess heart rate variability in neonate rats: validation in normotensive and hypertensive animals.

Several studies have focused on the heart rate variability (HRV) of murine species, while studies discussing HRV in murine neonates and infants remain scarce, since recording hemodynamic signals through invasive methods in small animals has been found to be quite challenging. Thus, this study aimed at describing and validating a novel method to assess HRV in newborn rats. An electrocardiogram (ECG) system was used to determine RR intervals in awake newborns and evaluate HRV in normotensive (Wistar) and hypertensive (SHR) neonate rats. After birth, ECG was recorded in the awake newborns, and they were allowed to rest on a heated surface, restricted only by the weight of the adhesive ECG electrodes. The electrodes were cut and adapted to provide more comfort to the animal, and gently placed on the newborn's skin. RR intervals were recorded over a 30-min period using an ECG system together with LabChart software (4 KHz). Three sequences of 5 min each from the ECG recording period were analyzed in time and frequency domains, using CardioSeries software. ECG data resulted in a clearly interpretable signal that was used to generate an RR interval sequence through time for the analysis of HRV. SHR neonates presented increased cardiac sympathovagal balance compared to Wistar neonates (low frequency/high frequency: 3.85±0.71 vs 0.90±0.09). In conclusion, the ECG setup here described may be used to record RR intervals to assess HRV in neonate rats, thus detecting early impairment of HRV in hypertensive newborns.

Heart rate variability in newborns.

Heart rate (HR) and heart rate variability (HRV) in newborns is influenced by genetic determinants, gestational and postnatal age, and other variables. Premature infants have a reduced HRV. In neonatal HRV evaluated by spectral analysis, a dominant activity can be found in low frequency (LF) band (combined parasympathetic and sympathetic component). During the first postnatal days the activity in the high frequency (HF) band (parasympathetic component) rises, together with an increase in LF band and total HRV. Hypotrophy in newborn can cause less mature autonomic cardiac control with a higher contribution of sympathetic activity to HRV as demonstrated by sequence plot analysis. During quiet sleep (QS) in newborns HF oscillations increase - a phenomenon less expressed or missing in premature infants. In active sleep (AS), HRV is enhanced in contrast to reduced activity in HF band due to the rise of spectral activity in LF band. Comparison of the HR and HRV in newborns born by physiological vaginal delivery, without (VD) and with epidural anesthesia (EDA) and via sectio cesarea (SC) showed no significant differences in HR and in HRV time domain parameters. Analysis in the frequency domain revealed, that the lowest sympathetic activity in chronotropic cardiac chronotropic regulation is in the VD group. Different neonatal pathological states can be associated with a reduction of HRV and an improvement in the health conditions is followed by changes in HRV what can be use as a possible prognostic marker. Examination of heart rate variability in neonatology can provide information on the maturity of the cardiac chronotropic regulation in early postnatal life, on postnatal adaptation and in pathological conditions about the potential dysregulation of cardiac function in newborns, especially in preterm infants.

Pattern of brain injury and depressed heart rate variability in newborns with hypoxic ischemic encephalopathy.

BackgroundDecreased heart rate variability (HRV) is a measure of autonomic dysfunction and brain injury in newborns with hypoxic ischemic encephalopathy (HIE). This study aimed to characterize the relationship between HRV and brain injury pattern by MRI in newborns with HIE undergoing therapeutic hypothermia.MethodsHRV metrics were quantified in the time domain (αS, αL, and root mean square at short [RMSS] and long [RMSL] time scales) and frequency domain (relative low-[LF] and high-frequency [HF] power) during the time period 24–27 hours of life. Brain injury pattern by MRI was classified as no injury, pure cortical/white matter injury, mixed watershed/mild basal nuclei injury, predominant basal nuclei or global injury, and died. HRV metrics were compared across brain injury pattern groups using a random effects mixed model.ResultsData from 74 infants were analyzed. Brain injury pattern was significantly associated with degree of HRV suppression. Specifically, negative associations were observed between pattern of brain injury and RMSS (estimate −0.224, SE 0.082, p=0.006), RMSL (estimate −0.189, SE 0.082, p=0.021), and LF power (estimate −0.044, SE 0.016, p=0.006).ConclusionDegree of HRV depression is related to pattern of brain injury. HRV monitoring may provide insights into pattern of brain injury at the bedside.

Inflammatory cytokine response and reduced heart rate variability in newborns with hypoxic-ischemic encephalopathy.

ObjectiveTo determine whether systemic inflammation-modulating cytokine expression is related to heart rate variability (HRV) in newborns with hypoxic ischemic encephalopathy (HIE).STUDY DESIGNData from 30 newborns with HIE were analyzed. Cytokine levels (IL-2, IL-4, IL-6, IL-8, IL-10, IL-13, IL-1β, TNF-α, IFN-λ) were measured either at 24 hours of cooling (n=5), 72 hours of cooling (n=4), or at both timepoints (n=21). The following HRV metrics were quantified in the time domain: alpha_S, alpha_L, root mean square (RMS) at short time scales (RMS_S), RMS at long time scales (RMS_L), while low frequency power (LF) and high frequency power (HF) were quantified in the frequency domain. The relationships between HRV metrics and cytokines were evaluated using mixed-models.ResultsIL-6, IL-8, IL-10, and IL-13 levels were inversely related to selected HRV metrics.ConclusionInflammation-modulating cytokines may be important mediators in the autonomic dysfunction observed in newborns with HIE.

Assessment of quality of ECG for accurate estimation of Heart Rate Variability in newborns.

Heart Rate Variability has been recently used to determine the severity of Hypoxic Ischemic Encephalopathy in neonates. However, it was shown that ECG and subsequently Instantaneous Heart Rate can be heavily corrupted by artefacts which have to be manually removed. This work analyses a set of features to assess their sensitivity to normal and corrupted ECG in newborns. Specifically, the IHR signal is obtained by detecting R-Peaks using the Pan-Tompkins algorithm. Four features are extracted from both ECG and IHR signal using various temporal resolutions to discriminate normal and corrupted signal. The performance of these features in discrimination is then assessed using statistical tests.

Newborn Seizure Detection Based on Heart Rate Variability

In this paper, we investigate the use of heart rate variability (HRV) for automatic newborn seizure detection. The proposed method consists of a sequence of processing steps, namely, obtaining HRV from the ECG, extracting a discriminating HRV feature set, selecting an optimal subset from the full feature set, and, finally, classifying the HRV into seizure/nonseizure using a supervised statistical classifier. Due to the fact that HRV signals are nonstationary, a set of time-frequency features from the newborn HRV is proposed and extracted. In order to achieve efficient HRV-based automatic newborn seizure detection, a two-phase wrapper-based feature selection technique is used to select the feature subset with minimum redundancy and maximum class discriminability. Tested on ECG recordings obtained from eight newborns with identified EEG seizure, the proposed HRV-based neonatal seizure detection algorithm achieved 85.7% sensitivity and 84.6% specificity. These results suggest that the HRV is sensitive to changes in the cardioregulatory system induced by the seizure, and therefore, can be used as a basis for an automatic seizure detection.

Electromagnetic fields produced by incubators influence heart rate variability in newborns

Background:Incubators are largely used to preserve preterm and sick babies from postnatal stressors, but their motors produce high electromagnetic fields (EMFs). Newborns are chronically exposed to these EMFs, but no...

Heart rate and heart rate variability in newborns with intrauterine growth retardation : L. Spassov a, L. Curzi-Dascalova a, J. Clairambault b, F. Kauffmann c, M. Eiselt a, C. Medigue b, P. Peirano a, aINSERM, CJF 89-09, Hôpital A. Béclére, 92141 Clamart, bINRIA, Rocquencourt and cUniversité de Caen, France

Heart rate and heart rate variability in newborns with intrauterine growth retardation : L. Spassov a, L. Curzi-Dascalova a, J. Clairambault b, F. Kauffmann c, M. Eiselt a, C. Medigue b, P. Peirano a, aINSERM, CJF 89-09, Hôpital A. Béclére, 92141 Clamart, bINRIA, Rocquencourt and cUniversité de Caen, France