The respiratory control system is highly dynamic; however, typical measures of respiratory variability are often limited to computing the mean±SE and/or the coefficient of variation (CV), which do not distinguish between short‐term and long‐term variability. To overcome this limitation, some studies have employed Poincaré plot (PP), irregularity score (IS), or approximate entropy (ApEn) analyses; however, these analysis methods have not been implemented and compared on a single data set. To address this issue, we used all of the above analysis methods to quantify burst‐to‐burst variability of basal phrenic nerve discharge (n=300 bursts) recorded from anesthetized or decerebrate adult rat. We found that while all methods provide insight into the overall burst‐to‐burst variability, PP, IS, and ApEn analyses provide novel information about the patterning of the data spread. Moreover, these methods provide novel information about the short‐term vs long‐term variability. We suggest that these additional analysis methods be used to more fully capture patterns of long‐term variability and provide complementary insight into the dynamics underlying the respiratory control system. Supported by HL63175

The respiratory control system is highly dynamic, allowing for breath‐to‐breath adjustments to both physiological and pathological conditions. Ongoing work in our laboratory in the adult in vivo rat focuses on exploring the influence of different classes of anesthetics on various respiratory behaviors, including basal respiratory burst patterns. In the current study, we expand on this work by examining the influence of different classes of anesthetics on burst‐to‐burst variability of basal phrenic nerve discharge. Poincaré plot and coefficient of variation analyses were performed on 300 consecutive phrenic bursts obtained from urethane (U), inactin used alone (IN) or in combination with ketamine (IN‐K) or xylazine (IN‐X), sodium pentobarbital (P), ketamine in combination with xylazine (K‐X), propofol (PRO), and isoflurane (ISO) anesthetized rats; decerebrate rats were also studied. Our analyses suggest that different classes of anesthetics differentially influence the short‐term and long‐term variability of phrenic burst frequency and timing, and that typically used measures of variability may not fully capture long‐term patterns of variability. Supported by HL63175