Neonates at risk for sudden infant death syndrome (SIDS) are hospitalized for cardiorespiratory monitoring however, monitoring is costly and generates large quantities of averaged data that serve as poor predictors of infant risk. In this study we used a traditional autocorrelation function (ACF) testing its suitability as a tool to detect subtle alterations in respiratory patterning in vivo. We applied the ACF to chest wall motion tracings obtained from rat pups in the period corresponding to the mid-to-end of the third trimester of human pregnancy. Pups were drawn from two groups: nicotine-exposed and saline-exposed at each age (i.e., P7, P8, P9, and P10). Respiratory-related motions of the chest wall were recorded in room air and in response to an arousal stimulus (FIO2 14%). The autocorrelation function was used to determine measures of breathing rate and respiratory patterning. Unlike alternative tools such as Poincare plots that depict an averaged difference in a measure breath to breath, the ACF when applied to a digitized chest wall trace yields an instantaneous sample of data points that can be used to compare (data) points at the same time in the next breath or in any subsequent number of breaths. The moment-to-moment evaluation of chest wall motion detected subtle differences in respiratory pattern in rat pups exposed to nicotine in utero and aged matched saline-exposed peers. The ACF can be applied online as well as to existing data sets and requires comparatively short sampling windows (~2min). As shown here, the ACF could be used to identify factors that precipitate or minimize instability and thus, offers a quantitative measure of risk in vulnerable populations.
- Autocorrelation function
- Sudden infant death syndrome
ASJC Scopus subject areas
- Pulmonary and Respiratory Medicine