Artifact rejection and cycle detection in immature breathing: Application to the early detection of neonatal sepsis

• DOI: 10.1016/j.bspc.2014.10.007
• Publication Date: 2014-11-08T02:31:54Z
• Authors: X. Navarro; F. Porée; A. Beuchée; G. Carrault
• Keywords: [SDV.IB] Life Sciences [q-bio]/Bioengineering; automated detection; logistic regression; premature infant; 3. Good health; 03 medical and health sciences; 0302 clinical medicine; respiratory variability signal; artifact removal; [SDV.IB]Life Sciences [q-bio]/Bioengineering; [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing; [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing

Abstract This paper proposes a new framework to obtain quality respiratory variability signals from the raw breathing recorded in neonatal intensive care units (NICUs). It combines three consecutive blocks: an automatic rejection of artifacts, implemented by a logistic regression classifier, a two-step filtering process, and the identification of respiratory cycles, implemented by a peak detection algorithm. By means of a gold standard built from a preterm infants database, the performances of the first and third blocks have been evaluated. While the former obtains a 86% of specificity and sensitivity, the latter attains a respective 97%. The interest of our proposal in the clinical domain is illustrated by a promising application to detect promptly and non-invasively the presence of neonatal sepsis in the NICU.