A perinatal stress calculator for the neonatal intensive care unit: an unobtrusive approach.


Journal

Physiological measurement
ISSN: 1361-6579
Titre abrégé: Physiol Meas
Pays: England
ID NLM: 9306921

Informations de publication

Date de publication:
11 08 2020
Historique:
pubmed: 11 6 2020
medline: 21 7 2021
entrez: 11 6 2020
Statut: epublish

Résumé

Early experience of pain and stress in the neonatal intensive care unit is known to have an effect on the neurodevelopment of the infant. However, an automated method to quantify the procedural pain or perinatal stress in premature patients does not exist. In the current study, EEG and ECG data were collected for more than 3 hours from 136 patients in order to quantify stress exposure. Specifically, features extracted from the EEG and heart-rate variability in both quiet and non-quiet sleep segments were used to develop a subspace linear-discriminant analysis stress classifier. The main novelty of the study lies in the absence of intrusive methods or pain elicitation protocols to develop the stress classifier. Three main findings can be reported. First, we developed different stress classifiers for the different age groups and stress intensities, obtaining an area under the curve in the range [0.78-0.93] for non-quiet sleep and [0.77-0.96] for quiet sleep. Second, a dysmature EEG was found in patients under stress. Third, an enhanced cortical connectivity and increased brain-heart communication was correlated with a higher stress load, while the autonomic activity did not seem to be associated to stress exposure. The results shed a light on the pain and stress processing in preterm neonates, suggesting that software tools to investigate dysmature EEG might be helpful to assess stress load in premature patients. These results could be the foundation to assess the impact of stress on infants' development and to tune preventive care.

Identifiants

pubmed: 32521528
doi: 10.1088/1361-6579/ab9b66
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

075012

Auteurs

M Lavanga (M)

Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Kasteelpark Arenberg 10, box 2446, 3001, Leuven, Belgium. Authors contributed equally to this work.

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Classifications MeSH