A Bradycardia-Based Stress Calculator for the Neonatal Intensive Care Unit: A Multisystem Approach

Abstract

Early life stress in the neonatal intensive care unit (NICU) can predispose premature infants to adverse health outcomes and neurodevelopment delays. Hands-on-care and procedural pain might induce apneas, hypoxic events, and sleep-wake disturbances, which can ultimately impact maturation, but a data-driven method based on physiological fingerprints to quantify early-life stress does not exist. This study aims to provide an automatic stress detector by investigating the relationship between bradycardias, hypoxic events and perinatal stress in NICU patients. EEG, ECG, and SpO 2 were recorded from 136 patients for at least 3 h in three different monitoring groups. In these subjects, the stress burden was assessed using the Leuven Pain Scale. Different subspace linear discriminant analysis models were designed to detect the presence or the absence of stress based on information in each bradycardic spell. The classification shows an area under the curve in the range [0.80–0.96] and a kappa score in the range [0.41–0.80]. The results suggest that stress seems to increase SpO 2 desaturations and EEG regularity as well as the interaction between the cardiovascular and neurological system. It might be possible that stress load enhances the reaction to respiratory abnormalities, which could ultimately impact the neurological and behavioral development.

Entropy Analysis of Heart Rate Variability in Different Sleep Stages

“…Physiological systems are complex, nonlinear, and nonstationary partially due to the mutual coupling between multiple subsystems [15,16]. The cardiovascular control system also has complex dynamical behavior and nonlinear characteristics.…”

Network Physiology of Exercise: Vision and Perspectives

“…In recent years, the Network Physiology framework has been utilized in various fields of basic Physiology and Clinical Medicine, including multiple organ failure and sepsis in critically ill patients (Asada et al, 2016;Moorman et al, 2016), neonatal intensive care (Lavanga et al, 2020;Lucchini et al, 2020), liver disease (Tan et al, 2020), epilepsy and neurological disorders (Lin et al, 2020), diabetes and obesity (Podobnik et al, 2020;Prats-Puig et al, 2020), cancer (Liu et al, 2020), or psychiatry (Bolton et al, 2020), and has the potential for broad applications in the field of Exercise Physiology and Sports Medicine to uncover how the key physiological systems interact pairwise, that is, which links are the major mediators in a given network and how these links adjust their strength with accumulation of fatigue, after a training intervention, or in response to a certain pathological condition (e.g., musculoskeletal injury and neurodegenerative disease).…”

The New Field of Network Physiology: Building the Human Physiolome

“…In fact, in recent years, we have already witnessed the broad impact of introducing novel concepts and methods derived from modern statistical physics and network theory to biology and medicine, shifting the paradigm from reductionism to a new integrative framework essential to address fundamentally new problems in systems biology (Yao et al, 2019;Prats-Puig et al, 2020;Corkey and Deeney, 2020;Rizi et al, 2021;Barajas-Martínez et al, 2020), neuroscience (Castelluzzo et al, 2020;Pa¨eske et al, 2020;Fesce, 2020;Stramaglia et al, 2021), physiology (Podobnik et al, 2020;Zmazek et al, 2021), clinical medicine (Loscalzo and Barabasi, 2011;Delussi et al, 2020;Li et al, 2020;Liu et al, 2020;McNorgan et al, 2020;Tan et al, 2020;Haug et al, 2021;Liu et al, 2021) and even drug discovery (Hopkins, 2008). A central focus of research within this integrative framework is the interplay between structural connectivity and functional dependency, a key problem in neuroscience, brain research (Bullmore and Sporns, 2009;Gallos et al, 2012;Rothkegel and Lehnertz, 2014;Liu et al, 2015a;Bolton et al, 2020;Wang and Liu, 2020) and human physiology (Pereira-Ferrero et al, 2019;Lavanga et al, 2020;Barajas-Martínez et al, 2021;Gao et al, 2018;Balagué et al, 2020;Porta et al, 2017;Lioi et al, 2017;Jiang et al, 2021). As a result, new physical models have been motivated and proposed to investigate the dynamical consequences of adaptive networks…”