Abstract
Infants born at 35–37 weeks’ gestational age (GA) are at higher risk for a range of pathological conditions and poorer neurodevelopmental outcomes. However, mechanisms responsible are not fully understood. The purpose of this paper is to use traditional and novel techniques to assess newborn autonomic development as a function of GA at birth, focusing on cardiorespiratory regulation. ECG and respiration were acquired during sleep on 329 healthy newborns. Infants were divided into GA groups: 35–36 weeks (late preterm (LPT)), 37–38 weeks (early term (ET)), and 39–40 weeks (full term (FT)). Time domain, frequency domain, and non-linear measures were calculated. Increased heart rate short-term variability and complexity as a function of GA were observed in time domain and non-linear measures. Decreasing inter-breath interval variability was found as a function of GA, with increasing linear cardiorespiratory coupling. A complexity parameter (quadratic sample entropy) was less affected by arrhythmias and artifacts when compared to traditional measures. Results suggest lower maturation in LPT, with less developed cardiorespiratory regulation. This may confer risk for altered outcome, convergent with epidemiological findings. Reported examples show that a combination of methodological approaches can be beneficial to characterize autonomic maturation.

ᅟ


Similar content being viewed by others
References
Adamkin DH (2013) Postnatal glucose homeostasis in late-preterm and term infants. Pediatrics 127(3):575–579. https://doi.org/10.1542/peds.2010-3851
Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B Methodol 57(1):289–300
DeMazumder D, Limpitikul WB, Dorante M, Dey S, Mukhopadhyay B, Zhang Y, Moorman JR, Cheng A, Berger RD, Guallar E, Jones SR, Tomaselli GF (2016) Entropy of cardiac repolarization predicts ventricular arrhythmias and mortality in patients receiving an implantable cardioverter-defibrillator for primary prevention of sudden death. Europace 18(12):1818–1828. https://doi.org/10.1093/europace/euv399
Dubin AM (2000) Arrhythmias in the Newborn. NeoReviews 1(8):e146 LP–e14e151
Faes L, Pinna GD, Porta A, Maestri R, Nollo G (2004) Surrogate data analysis for assessing the significance of the coherence function. IEEE Trans Biomed Eng 51(7):1156–1166. https://doi.org/10.1109/TBME.2004.827271
Ferrario M, Signorini MG, Magenes G (2005) Complexity analysis of the fetal heart rate for the identification of pathology in fetuses. Computers in Cardiology 2005 32(1):989–992. https://doi.org/10.1109/CIC.2005.1588275
Garcia AJ, Koschnitzky JE, Ramirez J-M (2013) The physiological determinants of sudden infant death syndrome. Respir Physiol Neurobiol 189(2):288–300. https://doi.org/10.1016/j.resp.2013.05.032
Hathorn MKS (1987) Respiratory sinus arrhythmia in new-born infants. J Physiol 385(1987):1–12
Hoffman H, Damus K, Hillman L, Krongrad E (1988) Risk factors for SIDS: results of the national institute of child health and human development SIDS cooperative epidemiological study. Ann N Y Acad Sci 533(1):13–30
Isler JR, Thai T, Myers MM, Fifer WP (2016) An automated method for coding sleep states in human infants based on respiratory rate variability. Dev Psychobiol 58(8):1108–1115. https://doi.org/10.1002/dev.21482
Javorka M, Zila I, Balhárek T, Javorka K (2002) Heart rate recovery after exercise: relations to heart rate variability and complexity. Braz J Med Biol Res 35:991–1000. https://doi.org/10.1590/S0100-879X2002000800018
Kaufmann T, Sütterlin S, Schulz SM, Vögele C (2011) ARTiiFACT: a tool for heart rate artifact processing and heart rate variability analysis. Behav Res Methods 43(4):1161–1170. https://doi.org/10.3758/s13428-011-0107-7
Killen SAS, Fish FA (2008) Fetal and neonatal arrhythmias. NeoReviews 9:e242–e252. https://doi.org/10.1542/neo.9-6-e242
Kugelman A, Colin AA (2013) Late preterm infants: near term but still in a critical developmental time period. Pediatrics 132(4):31–4005
Lake, D.E. 2011. Improved entropy rate estimation in physiological data. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS. (2011), 1463–1466. DOI: https://doi.org/10.1109/IEMBS.2011.6090339
Lake DE (2006) Renyi entropy measures of heart rate Gaussianity. IEEE Trans Biomed Eng 53(1):21–27. https://doi.org/10.1109/TBME.2005.859782
Lake DE, Richman JS, Griffin MP, Moorman JR (2002) Sample entropy analysis of neonatal heart rate variability. Am J Phys Regul Integr Comp Phys 283:R789–R797. https://doi.org/10.1152/ajpregu.00069.2002
Loftin RW, Habli M, Snyder CC, Cormier CM, Lewis DF, DeFranco EA (2010) Late preterm birth. Rev Obstet Gynecol 3(1):10–19. https://doi.org/10.3909/riog0098
Lucchini, M., Fifer, W.P., Ferrario, M. and Signorini, M.G. 2016. Feasibility study for the assessment of cardio-respiratory coupling in newborn infants. Engineering in Medicine and Biology Society (EMBC), 2016 IEEE 38th Annual International Conference of the 5509–5512
Lucchini M, Fifer WP, Sahni R, Signorini MG (2016) Novel heart rate parameters for the assessment of autonomic nervous system function in premature infants. Physiol Meas 37(9):1436–1446. https://doi.org/10.1088/0967-3334/37/9/1436
Lucchini M, Pini N, Fifer WP, Burtchen N, Signorini MG (2017) Entropy information of cardiorespiratory dynamics in Neonates during sleep. Entropy 19:5. https://doi.org/10.3390/e19050225
Martin JA, Hamilton BE, Sutton PD, Ventura SJ, Menacker F, Kirmeyer S, Mathews TJ, Statistics, V (2015) National Vital Statistics Reports Births: Final data for 2013. Statistics 64(1):1–104
Mulder LJM (1992) Measurement and analysis-methods of heart-rate and respiration for use in applied environments. Biol Psychol 34(2–3):205–236. https://doi.org/10.1016/0301-0511(92)90016-N
Palazzolo JA, Estafanous FG, Murray PA (1998) Entropy measures of heart rate variation in conscious dogs. Am J Phys 274(4 Pt 2):H1099–H1105
Peltola MA (2012) Role of editing of R-R intervals in the analysis of heart rate variability. Front Physiol 3:1–10. https://doi.org/10.3389/fphys.2012.00148
Pincus SM (1991) Approximate entropy as a measure of system complexity. Proc Natl Acad Sci 88(6):2297–2301. https://doi.org/10.1073/pnas.88.6.2297
Porges SW, Doussard-Roosevelt JA, Stifter CA, McClenny BD, Riniolo TC (1999) Sleep state and vagal regulation of heart period patterns in the human newborn: an extension of the polyvagal theory. Psychophysiology 36:14–21. https://doi.org/10.1017/S004857729997035X
Richman JS, Moorman JR (2000) Physiological time-series analysis using approximate entropy and sample entropy physiological time-series analysis using approximate entropy and sample entropy. Am J Physiol Heart Circ Physiol 278(6):2039–2049
Rosenstock EG, Cassuto Y, Zmora E (1999) Heart rate variability in the neonate and infant: analytical methods, physiological and clinical observations. Acta Paediatr (Oslo, Norway : 1992) 88(5):477–482. https://doi.org/10.1111/j.1651-2227.1999.tb00158.x
Salo MA, Huikuri HV, Seppänen T (2001) Ectopic beats in heart rate variability analysis: effects of editing on time and frequency domain measures. Ann Noninvasive Electrocardiol 6(1):5–17. https://doi.org/10.1111/j.1542-474X.2001.tb00080.x
Sassi R, Cerutti S, Lombardi F, Malik M, Huikuri HV, Peng CK, Schmidt G, Yamamoto Y (2015) Advances in heart rate variability signal analysis: joint position statement by the e-Cardiology ESC Working Group and the European Heart Rhythm Association co-endorsed by the Asia Pacific Heart Rhythm Society. Europace 17(9):1341–1353. https://doi.org/10.1093/europace/euv015
Shah P, Kaciroti N, Richards B, Oh W, Lumeng JC (2016) Developmental outcomes of late preterm infants from infancy to kindergarten. Pediatrics 138(2):8–15. https://doi.org/10.1542/peds.2015-3496
Signorini MG, Magenes G, Cerutti S, Arduini D (2003) Linear and nonlinear parameters for the analysis of fetal heart rate signal from cardiotocographic recordings. 50(3):365–374
Task Force of the European Society of Cardiology (1996) Heart rate variability. Standards of measurement physiological interpretation and clinical use. Circulation 93:1043–1065. https://doi.org/10.1161/01.CIR.93.5.1043
Thompson JMD, Mitchell EA (2006) Are the risk factors for SIDS different for preterm and term infants? Arch Dis Child 91(2):107–111. https://doi.org/10.1136/adc.2004.071167
Vohr B (2013) Long-term outcomes of moderately preterm, late preterm, and early term infants. Clin Perinatol 40(4):739–751. https://doi.org/10.1016/j.clp.2013.07.006
Voss A, Hnatkova K, Wessel N (1998) Multiparametric analysis of heart rate variability used for risk stratification among survivors of acute myocardial infarction. Pacing Clin Electrophysiol 21:186–196
Wang M, Dorer D, Fleming M, Catlin E (2004) Clinical outcomes of near-term infants. Pediatrics 114(2 PG-372-6):372–376 DOI:https://doi.org/114/2/372
Funding
The writing of this manuscript was supported by the Sackler Institute of Developmental Psychobiology at Columbia University and by National Institute of Health grants NIH Grants R37 HD32774 (WPF) and T32 MH018264 (NB) and by Rotary International Global Grant. This publication was also supported by the National Center for Advancing Translational Sciences and National Institutes of Health, through Grant Number UL1TR001873. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.
Author information
Authors and Affiliations
Corresponding author
Glossary of terms
- ANS
-
Autonomic nervous system
- ET
-
Early-term infants
- FT
-
Full-term infants
- GA
-
Gestational age
- HF
-
High frequency
- HoL
-
Hours of life
- HR
-
Heart rate
- HRV
-
Heart rate variability
- IBI
-
Inter-breath interval
- LPT
-
Late-preterm infants
- MoD
-
Mode of delivery
- NN
-
Normal to normal intervals, as the RR distances excluding anomalous beats
- QSE
-
Quadratic sample entropy
- RMSSD
-
Root mean of successive NN differences
- RR
-
distance between consecutive QRS peaks
- SDNN
-
Standard deviation of NN
- SIDS
-
Sudden infant death syndrome
Rights and permissions
About this article
Cite this article
Lucchini, M., Burtchen, N., Fifer, W.P. et al. Multi-parametric cardiorespiratory analysis in late-preterm, early-term, and full-term infants at birth. Med Biol Eng Comput 57, 99–106 (2019). https://doi.org/10.1007/s11517-018-1866-4
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11517-018-1866-4