The Digi-NewB project for preterm infant sepsis risk and maturity analysis

Authors

• Alpo Värri https://orcid.org/0000-0002-3214-3458
• Antti Kallonen Tampere University of Technology
• Elina Helander Tampere University of Technology https://orcid.org/0000-0002-0076-0590
• Andres Ledesma Tampere University of Technology https://orcid.org/0000-0003-1932-573X
• Patrick Pladys Rennes University Hospital https://orcid.org/0000-0002-4305-3765

DOI: https://doi.org/10.23996/fjhw.69152

Keywords:

decision support systems [http://www.yso.fi/onto/yso/p27803], artificial intelligence [http://www.yso.fi/onto/yso/p2616], preterm infant, sepsis risk, infant maturity, health informatics

Abstract

It is known from the literature that the careful analysis of the heart rate variability of a preterm infant can be used as a predictor of sepsis. The Digi-NewB project aims at collecting a database of at least 750 preterm infants including physiological signals, video and clinical observations. These data are used to design a decision support system for the early detection of sepsis and for the evaluation of the infant maturity. The preparation of the data for the exploratory analysis has turned out to be time-consuming. 190 infants have been recorded by March 2018 and of these, the R-R interval analysis of the ECG signals has been completed of 136 infants. The results of the project are still preliminary but seven heart rate variability parameters have been found to be different in preterm and full-term infants with a P value less than 0.01. The video analysis algorithm detecting the presence of personnel or relatives reached 96.8% of sensitivity and 95.1% of specificity.