New method to measure interbreath intervals in infants for the assessment of apnoea and respiration

Abstract

BackgroundRespiratory disorders, including apnoea, are common in preterm infants due to their immature respiratory control compared with term-born infants. However, our inability to accurately measure respiratory rate in hospitalised infants results in unreported episodes of apnoea and an incomplete picture of respiratory activity.MethodsWe develop, validate and use a novel algorithm to identify interbreath intervals (IBIs) and apnoeas in preterm infants. In 42 preterm infants (1600 hours of recordings), we assess IBIs from the chest electrical impedance pneumograph using an adaptive amplitude threshold for the detection of breaths. The algorithm is refined by comparing its accuracy with clinically observed breaths and pauses in breathing. We develop an automated classifier to differentiate periods of true apnoea from artefactually low amplitude signal. We assess the performance of this algorithm in the detection of morphine-induced respiratory depression. Finally, we use the algorithm to investigate whether retinopathy of prematurity (ROP) screening alters the IBI distribution.ResultsIndividual breaths were detected with a false-positive rate of 13% and a false-negative rate of 12%. The classifier identified true apnoeas with an accuracy of 93%. As expected, morphine caused a significant shift in the IBI distribution towards longer IBIs. Following ROP screening, there was a significant increase in pauses in breathing that lasted more than 10 s (t-statistic=1.82, p=0.023). This was not reflected by changes in the monitor-derived respiratory rate and no episodes of apnoea were recorded in the medical records.ConclusionsWe show that our algorithm offers an improved method for the identification of IBIs and apnoeas in preterm infants. Following ROP screening, increased respiratory instability can occur even in the absence of clinically significant apnoeas. Accurate assessment of infant respiratory activity is essential to inform clinical practice.

The effect of acute respiratory events and respiratory stimulants on EEG-recorded brain activity in neonates: A systematic review

“…Two of the papers were conference abstracts with limited information ( Manas et al, 2014 , Wulbrand and Bentele, 1994 ), one was a case report ( Low et al, 2012 ) which have known study design limitations ( Nissen and Wynn, 2014 ), and three ( Bridgers et al, 1985 , Deuel, 1973 , Fenichel et al, 1980 ) were published 40 to 50 years ago, and so findings may be obsolete with improvements in medical technology. There were diverse definitions for the exposure variable i.e., the definition of apnoea – with wide variation in minimum duration, different EEG outcome measures, methods of analysis (visual versus quantitative assessment) and methods of apnoea detection (which may also be unreliable in infants ( Adjei et al, 2021 )) making cross-study comparison challenging. The heterogeneity of studies also prevented a meta -analysis and power calculations with pooled data for future studies.…”

Doxapram versus placebo in preterm newborns: a study protocol for an international double blinded multicentre randomized controlled trial (DOXA-trial)

“…Poets et al developed an interesting apnoea score to assess the severity of apnoea, but this score is not validated yet and is mostly a reflection of current clinical practice [ 57 ]. The rate of apnoea and hypoxic events can also fluctuate largely within and between patients, and detection can be difficult [ 51 , 58 ]. We therefore chose for a more pragmatic design with less strict inclusion criteria.…”

Apnoea suppresses brain activity in infants

“…Samples that exceeded five times the standard deviation of the signal were removed and linearly interpolated, a process that was iterated 10 times. We then identified the inter- breath intervals (IBI) in each of the three respiratory signals separately using a previously described algorithm validated for the identification of IBI in infants from the impedance pneumograph (Adjei et al, 2021), adapting this algorithm for the respiratory signals recorded here. Briefly, the algorithm first removes artefacts from the respiration signals that could falsely be detected as breathing cycles.…”

“…Samples that exceeded five times the standard deviation of the signal were removed and linearly interpolated, a process that was iterated 10 times. We then identified the interbreath intervals (IBI) in each of the three respiratory signals separately using a previously described algorithm validated for the identification of IBI in infants from the impedance pneumograph (Adjei et al, 2021), adapting this algorithm for the respiratory signals recorded potential pauses, the logistic regression classified 20,280 of them as true. To rule out any effect of short pauses in breathing in the baseline or post-pause period, we focused our analysis on isolated breathing pauses, meaning that any pauses that had another pause in temporal proximity were discarded from the analysis (here, defined as any other pause of at least 5 seconds within -60 to 90 seconds relative to the pause).…”