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

Abstract

Background: Respiratory 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.

Methods: We 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.

Results: Individual 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.

Conclusions: We 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.

Keywords: respiratory measurement.

Figure 1

Schematic of the proposed algorithm for detection of interbreath intervals (IBIs) from the impedance pneumograph (IP) in infants.

Figure 2

Optimising the threshold for breath detection. To optimise the threshold parameters, we investigated the performance of different threshold values (defined as a multiple (α) of the SD of the IP signal across the previous N breaths) to identify individual breaths and pauses in breathing. Figures show the percentage of false positives (orange) and false negatives (purple) for different values of α (with N=15). (A) Values calculated by comparing algorithm-identified breaths with breaths manually annotated at the time of the recording by visual observation (data set 1). (B) Values calculated by comparing algorithm-identified pauses in breathing with pauses (of at least 5 s) manually annotated by two investigators (first hour of recording in 15 infants from data set 2). Error bars indicate mean and SD (across the recordings). Values are jittered on the X-axis so that false positive and false negative bars do not overlap. Grey shading indicates selected threshold parameters; with these parameters (α=0.4, N=15), there was the optimal balance between the percentages of false positives and false negatives in the identification of individual breaths (A). These parameters also achieved a good balance between false positives and negatives in the identification of pauses in breathing (B).

Figure 3

Using support vector machine classification to identify true apnoeas. (A) An example of a pause in breathing lasting longer than 20 s identified as a true apnoea. IP, the electrical impedance pneumograph after filtering to remove cardiac-frequency noise and movement artefact. HR, heart rate in beats per minute. SpO₂, oxygen saturation. RR, respiratory rate in breaths per minute, recorded by the infant’s patient monitor (black) and calculated using our algorithm (blue). Note that the RR does not reach zero on the infant’s patient monitor and so this episode does not lead to a monitor apnoea alarm. Grey shading indicates the period during which no breaths were detected by our algorithm. (B) A potential apnoea initially detected by the algorithm but classified by investigators as a false alarm. (C) The root mean square (RMS) of the IP signal before and during the apnoea (see Methods for further details). Red circles indicate episodes classified by both investigators as true apnoeas, and blue circles are those episodes classified by both investigators as false alarms. (D) Change in oxygen saturation and heart rate for true apnoeas (red) compared with false alarms (blue).

Figure 4

Interbreath intervals are altered by morphine administration and following ROP screening. (A–D) Respiratory rate and interbreath intervals (IBIs) in the 1-hour period prior to morphine administration compared with a 1-hour period after morphine administration (the 1-hour period immediately following ROP screening, approximately 1.3–2.3 hours after morphine administration) in the 15 infants who received morphine in the Poppi clinical trial. (E–H) Respiratory rate and IBIs 1 hour before and after ROP screening in 22 infants. (A, E) Mean respiratory rate from the infants’ patient monitor. (B–D, F–H) Metrics calculated using the novel algorithm proposed in this paper to identify the IBIs. Black lines and points indicate the group mean (A, C, E, F) or median (D, H). (B) IBI distribution in the 1-hour period prior to (black) compared with 1.3–2.3 hours after morphine administration (red). (F) IBI distribution in the 1-hour period before (black) and after (red) ROP screening. Y-axis indicates the probability of an IBI of duration greater than or equal to the X-axis value. Dotted line indicates the mean and shaded area the SD. (*p<0.05, **p<0.01, ***p<0.001, p-values corrected for multiple comparisons). ROP, retinopathy of prematurity.