Predicting Neonatal Sepsis Using Features of Heart Rate Variability, Respiratory Characteristics, and ECG-Derived Estimates of Infant Motion

Joshi, Rohan; Kommers, Deedee; Oosterwijk, Laurien; Feijs, Loe; Pul, Carola van; Andriessen, Peter

doi:10.1109/jbhi.2019.2927463

Cited by 72 publications

(70 citation statements)

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“…In infants, RMSSD correlated with several outcomes in both the short and the long term: ANS stage of development in the case of prematurity (Hoyer et al, 2009;Lucchini et al, 2016;Aye et al, 2018;Schneider et al, 2018), late-onset sepsis (Joshi et al, 2020), extubation success (Latremouille et al, 2018), recovering after admission in intensive care units (Marsillio et al, 2019), pain behavior (Weissman et al, 2012), short-term neurological development when measured during low blood pressure episodes (Semenova et al, 2018), and later neurological development at 2 years (Dimitrijević et al, 2016). RMSSD showed also to change according to the incubator temperature, thus revealing how infant's HRV changes due to environmental temperature (Stéphan-Blanchard et al, 2013).…”

Section: The Future Perspectives For the Clinical Use Of Hrv In The Nmentioning

confidence: 98%

“…Despite continuous monitoring to assure high care standards, much of this collected information remains unused. This situation is worth noting since integrating these vital signs could improve the predictive power of the NICU monitors and better help reduce infants' mortality (Sullivan et al, 2018;Joshi et al, 2020;Kumar et al, 2020).…”

Section: A Technological Analysis To Efficiently Monitor and Use Hrv mentioning

confidence: 99%

“…In a recent study, HRV combined with respiratory variability analysis changed significantly during the 24 h before the diagnosis of late-onset sepsis, especially in the last 6 h where real-time monitoring showed signals of cardiorespiratory instability. The reduction in RMSSD, accompanied by a decrease in average acceleration response (AAR, a robust index evaluating the heart capacity to increase its rhythm) and an increase in respiratory variability-related ApEn, predicted late-onset sepsis the most (Joshi et al, 2020).…”

Section: The Clinical Usefulness Of Real-time Hrv To Monitor Newbornsmentioning

confidence: 99%

“…To make HRV a gold standard, however, there is the need to deepen the knowledge about RMSSD or any other metric: more robust studies on larger samples are needed (Stéphan-Blanchard et al, 2013;De Jonckheere and Storme, 2019) as studies still show contradictory findings that merit careful analysis (Wang et al, 2016;Joshi et al, 2020).…”

Section: The Future Perspectives For the Clinical Use Of Hrv In The Nmentioning

confidence: 99%

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Heart Rate Variability in the Perinatal Period: A Critical and Conceptual Review

Chiera¹,

Cerritelli²,

Casini³

et al. 2020

Front. Neurosci.

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Neonatal intensive care units (NICUs) greatly expand the use of technology. There is a need to accurately diagnose discomfort, pain, and complications, such as sepsis, mainly before they occur. While specific treatments are possible, they are often timeconsuming, invasive, or painful, with detrimental effects for the development of the infant. In the last 40 years, heart rate variability (HRV) has emerged as a noninvasive measurement to monitor newborns and infants, but it still is underused. Hence, the present paper aims to review the utility of HRV in neonatology and the instruments available to assess it, showing how HRV could be an innovative tool in the years to come. When continuously monitored, HRV could help assess the baby's overall wellbeing and neurological development to detect stress-/painrelated behaviors or pathological conditions, such as respiratory distress syndrome and hyperbilirubinemia, to address when to perform procedures to reduce the baby's stress/pain and interventions, such as therapeutic hypothermia, and to avoid severe complications, such as sepsis and necrotizing enterocolitis, thus reducing mortality. Based on literature and previous experiences, the first step to efficiently introduce HRV in the NICUs could consist in a monitoring system that uses photoplethysmography, which is low-cost and non-invasive, and displays one or a few metrics with good clinical utility. However, to fully harness HRV clinical potential and to greatly improve neonatal care, the monitoring systems will have to rely on modern bioinformatics (machine learning and artificial intelligence algorithms), which could easily integrate infant's HRV metrics, vital signs, and especially past history, thus elaborating models capable to efficiently monitor and predict the infant's clinical conditions. For this reason, hospitals and institutions will have to establish tight collaborations between the obstetric, neonatal, and pediatric departments: this way, healthcare would truly improve in every stage of the perinatal period (from conception to the first years of life), since information about patients' health would flow freely among different professionals, and high-quality research could be performed integrating the data recorded in those departments.

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Section: The Future Perspectives For the Clinical Use Of Hrv In The Nmentioning

confidence: 98%

Section: A Technological Analysis To Efficiently Monitor and Use Hrv mentioning

confidence: 99%

Section: The Clinical Usefulness Of Real-time Hrv To Monitor Newbornsmentioning

confidence: 99%

Section: The Future Perspectives For the Clinical Use Of Hrv In The Nmentioning

confidence: 99%

See 2 more Smart Citations

Heart Rate Variability in the Perinatal Period: A Critical and Conceptual Review

Chiera¹,

Cerritelli²,

Casini³

et al. 2020

Front. Neurosci.

View full text Add to dashboard Cite

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“…The latter is motivated by the fact that the motor activity evolves along with the age of the newborns [14]. Recently, analysis of motion estimated from ECG has been investigated for the detection of late-onset sepsis [15]. Indeed, it has been shown that sepsis is related to lethargy, which can be observed as the absence of motion [16].…”

Section: Introductionmentioning

confidence: 99%

Preterm Newborn Presence Detection in Incubator and Open Bed Using Deep Transfer Learning

Weber

Cabon

Simon

et al. 2021

IEEE J. Biomed. Health Inform.

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Video-based motion analysis recently appeared to be a promising approach in neonatal intensive care units for monitoring the state of preterm newborns since it is contact-less and noninvasive. However it is important to remove periods when the newborn is absent or an adult is present from the analysis. In this paper, we propose a method for automatic detection of preterm newborn presence in incubator and open bed. We learn a specific model for each bed type as the camera placement differs a lot and the encountered situations are different between both. We break the problem down into two binary classifications based on deep transfer learning that are fused afterwards: newborn presence detection on the one hand and adult presence detection on the other hand. Moreover, we adopt a strategy of decision intervals fusion in order to take advantage of temporal consistency. We test three deep neural network that were pre-trained on ImageNet: VGG16, MobileNetV2 and InceptionV3. Two classifiers are compared: support vector machine and a small neural network. Our experiments are conducted on a database of 120 newborns. The whole method is evaluated on a subset of 25 newborns including 66 days of video recordings. In incubator, we reach a balanced accuracy of 86%. In open bed, the performance is lower because of a much wider variety of situations whereas less data are available.

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Exposures influencing the developing central autonomic nervous system

Schlatterer

Plessis

2020

Birth Defects Research

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Autonomic nervous system function is critical for transition from in-utero to ex-utero life and is associated with neurodevelopmental and neuropsychiatric outcomes later in life. Adverse prenatal and neonatal conditions and exposures can impair or alter ANS development and, as a result, may also impact longterm neurodevelopmental outcomes. The objective of this article is to provide a broad overview of the impact of factors that are known to influence autonomic development during the fetal and early neonatal period, including maternal mood and stress during and after pregnancy, fetal growth restriction, congenital heart disease, toxic exposures, and preterm birth. We touch briefly on the typical development of the ANS, then delve into both in-utero and exutero maternal and fetal factors that may impact developmental trajectory of the ANS and, thus, have implications in transition and in long-term developmental outcomes. While many types of exposures and conditions have been shown to impact development of the autonomic nervous system, there is still much to be learned about the mechanisms underlying these influences. In the future, more advanced neuromonitoring tools will be required to better understand autonomic development and its influence on long-term neurodevelopmental and neuropsychological function, especially during the fetal period.

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Predicting Neonatal Sepsis Using Features of Heart Rate Variability, Respiratory Characteristics, and ECG-Derived Estimates of Infant Motion

Cited by 72 publications

References 40 publications

Heart Rate Variability in the Perinatal Period: A Critical and Conceptual Review

Heart Rate Variability in the Perinatal Period: A Critical and Conceptual Review

Preterm Newborn Presence Detection in Incubator and Open Bed Using Deep Transfer Learning

Exposures influencing the developing central autonomic nervous system

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