Heart rate variability in healthy newborn infants

doi:10.1016/S0002-9149(01)02162-2

The American Journal of Cardiology

Volume 89, Issue 1, 1 January 2002, Pages 50-53

https://doi.org/10.1016/S0002-9149(01)02162-2 Get rights and content

Abstract

In adults and older children, heart rate variability (HRV) is frequently used to study autonomic function noninvasively. Normal values of HRV in newborn infants, however, are not widely available. This problem may be partially attributed to the lack of standardization of different methods. This study assessed HRV in normal newborn infants using 24-hour Holter monitoring. From 1997 to 2000, we prospectively evaluated frequency- (spectral analysis), geometric-, and time-domain indexes of HRV in normal term infants. Ninety-six asymptomatic infants who were <72 hours old were studied. Frequency-domain parameters (power in the high, low, very low, ultra low, and total frequency domains), a geometric parameter (HRV triangular index), and time-domain parameters (SDNN, SDANN, SDNNi, r-MSSD, s-NN50) are reported as means ± SD, medians, and 5th and 95th percentiles to establish the normative values for newborns. A high degree of correlation (r ≥0.85, p <0.0001) was noted among the 3 vagal tone dependent parameters, such as high-frequency power (frequency domain), r-MSSD, and s-NN50 (time domain). Our study supports the use of vagal dependent time-domain parameters like r-MSSD and sNN50 as surrogates for high-frequency power in newborns. Because the data are reported as means ± SD, medians, and 5th and 95th percentiles, their use facilitates the study of parasympathetic and sympathetic activity in comparable populations.

Section snippets

Selection criteria:

All newborns were recruited from the level I normal newborn nursery at our primary institution from 1997 to 2000. This study included asymptomatic infants who were between 24 and 72 hours old, weighed >2,400 grams, and had a gestational period of between 36 and 42 weeks. A convenience sampling strategy was used. Infants were excluded from the study if they were: (1) born to mothers who used any medications during pregnancy known to affect the cardiovascular system (e.g., bronchodilators); (2)

Results

Ninety-six asymptomatic infants who were <72 hours old were studied. The racial distribution of neonates in this study was Caucasian (21%), African-American (66%), and others (13%). Fifty-two percent of the infants were males. All infants survived beyond 1 year of age; there were no sudden deaths or apparent life-threatening events. Other demographic and medical characteristics of these infants are listed in Table 1. The length of analyzable data was 18.5 ± 4.9 hours (median 20.28; range 2.37

Discussion

This is the first study to report long-term (24 hour) HRV parameters in a large cohort of normal newborns measured by time-, geometric-, and frequency-domain methods. In addition, vagal tone dependent parameters such as high-frequency power (frequency domain) and r-MSSD and s-NN50 (time domain) had a high degree of correlation. These findings are similar to those reported in adults4 and in 200 healthy children and 200 children with congenital heart disease (aged 3 days to 14 years) who did or

Acknowledgements

The authors sincerely appreciate Drs. Chandra Mohan, Maya Crosnyka-Myslenski, and Yuriy Estrin for their hard work and expertise in working with the Pathfinder scanner. We are thankful to the wonderful staff of our General Clinical Research Center for their constant support and help in providing great care to our patients and their families. We are also grateful to Maureen Crowley and Irene Szentkiralyi for their adminstrative help.

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The rational use of diagnostic methods in medicine is always a challenge, and cardiology is no exception. The conventional 12-lead electrocardiogram, taken at rest, remains essential in the diagnosis of cardiovascular diseases and is the most important tool for the diagnosis of long QT syndrome. The measured QT interval must be corrected for heart rate, and different mathematical formulas are used for this. This article shows some of the difficulties that both the measurement of the QT interval has as well as the use of the different formulas commonly used for the correction of the QT interval by heart rate. We must consider that, depending on the formula used, different corrected QT values are obtained, which could lead to the interpretation of said interval as normal, abnormal, or borderline, with the consequent diagnostic implications since this can make the difference between considering an individual as healthy or sick (a carrier of a long QT syndrome). To exemplify the importance that the results of the different formulas can have, we briefly present 2 pediatric cases.
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The digital revolution of noncontact physiological signal monitoring in clinical and home health care is underway, and deep learning techniques are incredibly popular. Camera-based physiological signal monitoring for adults has made considerable progress in recent years. However, most of existing methods and datasets are developed for adult subjects, and until now, there has been no neonatal public database that is collected for developing deep learning method. Thus, in this paper, we introduce a large-scale newborn baby database, named NBHR (newborn baby heart rate estimation database), to fill the abovementioned knowledge gap. A total of 9.6 h of clinical videos (1130 videos totaling 921 GB) and reference vital signs are recorded from 257 infants at 0–6 days old. The facial videos and corresponding synchronized physiological signals, including photoplethysmograph information, heart rate, and oxygen saturation level, are recorded in our database. This large-scale database could be used to develop deep learning methods to estimate heart rate or oxygen saturation levels. Furthermore, a multitask deep learning method, called NBHRnet, is proposed to estimate heart rate based on the NBHR database, and the model is succinct that it can be deployed on a computer without GPUs. The experimental results indicate that NBHRnet yields competitive performance in predicting infant heart rate, with a mean absolute error of 3.97 bpm and a mean absolute percentage error of 3.28%; additionally, it can estimate heart rate almost instantaneously (2 s/60 frames). Our datasets are freely publicly available by request.
Non-invasive biosignals detection for continuous monitoring of a neonate using quartz crystal resonator
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Human biosignals such as the respiratory rate and heart rate exhibit important information of their health conditions [1,2].
A wide-range load sensing device and signal processing algorithm were presented for the non-invasive monitoring of a neonate. The device was developed based on a quartz crystal resonator (QCR) that exhibited wide measurement range to the applied load. The resolution of the force sensor was improved to detect the small force generated by heartbeats and respirations of a neonate. The force signals of a neonate laid on a bed were successfully measured in the craniocaudal direction by using the device installed under the bed. In addition, an error elimination algorithm was developed based on the Fast Fourier Transform (FFT) analysis to detect the biosignals accurately from the load data. Using the proposed method, we achieved the detection of the heart rate with the average error of 0.7 beats per minute (bpm) and standard deviation of 3.5 bpm and respiratory rate with the average error of −1.6 breaths per minute (brpm) and standard deviation of 7.1 brpm respectively.
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Moreover, it should be acknowledged that standard guidelines for HRV spectral analysis in newborns are not clearly defined. For example, frequency ranges were adjusted to the respiratory frequencies of newborns in different ways by different authors, leading to data inconsistence (Fortrat, 2002; De Rogalski Landrot et al., 2007; Longin et al., 2005; Mehta et al., 2002; Porges, 1986; Rosenstock et al., 1999). Moreover, no adjustment was adopted for the very PT newborns.
Seizures are frequently observed in neurological conditions affecting newborns. Since autonomic alterations are commonly associated with neonatal seizures (NS), we investigated the utility of heart rate variability (HRV) indexes of cardiac autonomic regulation for NS detection.
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Newborns with NS showed lower resting state HRV compared to controls. Moreover, seizure episodes were characterized by a short-lasting increase in vagal indexes of HRV. Pre-term newborns with NS had a lower HRV than full-term at rest. In pre-term newborns, no changes in HRV were observed before and during NS. On the contrary, full-term newborns showed significantly higher HRV before and during NS compared to the respective baseline values.
Our data point to resting autonomic impairment in newborns with NS. In addition, an increment in HRV has been observed during NS only in full term newborns.
Although these findings do not allow validation of HRV measures for NS prediction and detection, they suggest that a putative protective vagal mechanism might be adopted when an advanced maturation of autonomic nervous system is achieved.
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^☆: Dr. Mehta was supported by grant RO1-DA09049 from the National Insitute of Drug Administration, National Institutes of Health, Bethesda, Maryland. This study was supported by grant M01 RR00080 to the General Clinical Research Center from the National Institutes of Health, Bethesda, Maryland.

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Heart rate variability in healthy newborn infants☆

Abstract

Section snippets

Selection criteria:

Results

Discussion

Acknowledgements

Am J Cardiol

Am J Cardiol

Early Hum Dev

J Pediatr

J Pediatr

Am J Cardiol

Am J Cardiol

J Pediatr

The ability of several short-term measures of RR variability to predict mortality after myocardial infarction

Circulation