Adaptive bradycardia assessment in preterm infants. (July 2021)

• Record Type: Journal Article
• Authors: Sbrollini, Agnese; Mancinelli, Martina; Marcantoni, Ilaria; Morettini, Micaela; Carnielli, Virgilio Paolo; Burattini, Laura
• Is Part Of: Biomedical signal processing and control. Volume 68(2021)
• Publication Date: 2021-07
• Subjects: BD bradycardia depth -- BHR baseline heart rate -- BR bradycardia rate -- BW birth weight -- GA gestational age at birth -- HR heart rate -- NB number of bradycardias -- ρ Pearson's correlation coefficient -- tend end time of bradycardia -- ton onset time of bradycardia; Automatic heart-rate monitoring -- Preterm infants -- Heart rate -- Bradycardia -- Birth weight -- Neonatal monitoring; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2021.102816
• Languages: English
• ISSNs: 1746-8094

Abstract

Graphical abstract: Highlights: In preterm infants, bradycardias associate to critical health conditions. Standard bradycardia identification assumes same heart rate in all infants. An adaptive algorithm for bradycardia identification is presented. Adaptive algorithm overcomes standard algorithm for bradycardia identification. Adaptive algorithm is potentially useful in preterm-infant heart-rate monitoring. Abstract: In preterm infants, bradycardias associate to critical health conditions. Standard algorithm for bradycardia identification assumes baseline heart rate (BHR) equal to 150bpm and identifies bradycardias when heart rate (HR) decreases below 100bpm. Since preterm infants show BHR varying from 120bpm to 160bpm, a new adaptive algorithm for real-time bradycardia identification was presented. The adaptive algorithm continuously adjusts BHR by averaging HR over the preceding 10-minute window after eliminating out-of-range HR values, and identifies bradycardias when HR decreases below 67% of BHR. Both standard and adaptive algorithms were evaluated using long-term (20.3–70.3h) electrocardiographic recordings of ten preterm infants ("Preterm Infant Cardio-respiratory Signals" database by Physionet). Bradycardias were characterized in terms of rate (BR, h −1 ) and depth (BD, bpm). Being also indexes of infants' health conditions, gestational age at birth (GA, days), birth weight (BW, kg) and HR were used to evaluate performances of the algorithms. Association between BR and BD vs GA, BW and HR was evaluated by computation of the correlation coefficient ( ρ ). Overall, standard and adaptive algorithms identified 516 and 546 bradycardias, respectively; median BR and BD values were comparable (1.25h −1 and 76bpm vs 1.26h −1 and 70bpm, respectively). However, the adaptive algorithm provided higher BD for HR>150bpm, and vice versa. Significant ( p value<0.05) correlations were found between BR and HR ( ρ =0.69), BR and BW ( ρ =−0.76), and BR and HR ( ρ =0.76) only when using the adaptive algorithm. Thus, the adaptive algorithm is superior to the standard algorithm and represents a potentially clinically useful tool for real-time bradycardia assessment in preterm infants.