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

Mario Lavanga, Bieke Bollen, Katrien Jansen, Els Ortibus, Gunnar Naulaers, Sabine Van Huffel, Alexander Caicedo

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

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 SpO2 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 SpO2 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.

Original languageEnglish (US)
Article number741
JournalFrontiers in Physiology
Volume11
DOIs
StatePublished - Jun 26 2020

All Science Journal Classification (ASJC) codes

  • Physiology
  • Physiology (medical)

Fingerprint

Dive into the research topics of 'A Bradycardia-Based Stress Calculator for the Neonatal Intensive Care Unit: A Multisystem Approach'. Together they form a unique fingerprint.

Cite this