Real-time cardiorespiratory coherence is blind to changes in respiration during general anesthesia.

Purpose: A novel real-time cardiorespiratory coherence (CRC) algorithm has been developed to monitor nociception during general anesthesia. CRC uses custom designed filters to track and analyze the respiratory sinus arrhythmia (RSA) as it moves in time and frequency. CRC is a form of sensor fusion between heart rate and respiration, estimating the strength of linear coupling between the two signals. The aim of this study was to estimate the effect of changes in respiration rate (RR) and peak airway pressure (PPaw) on CRC. The response of CRC was compared to a prior offline wavelet-based algorithm (WTCRC) as well as traditional univariate heart rate variability (HRV) measures. A nociception index was created for each algorithm, ranging from 0 (no nociception) to 100 (strong nociception).

Methods: Following ethics approval and informed consent, data were collected from 48 children receiving general anesthesia during dental surgery. The times of change in RR and PPaw events were noted in real-time. A total of 43 RR and 35 PPaw change events were analyzed post hoc in pseudo real-time. The nociception index averages were compared between a baseline period and a response period around each event. A Wilcoxon rank-sum test was used to compare changes.

Results: The change in RR changed the CRC nociception index by an average of -2.2 [95% CI from -10 to 4.7] (P > 0.3), and the change in PPaw changed the CRC nociception index by an average of 5.4 [-1.0 to 11] (P > 0.1). The changes were smaller than those of many traditional HRV measures.

Conclusions: Real-time CRC was blind to the changes in respiration, and was less sensitive than many of the traditional HRV measures. A nociception index based on CRC can thus function across a wider range of respiratory conditions than can many traditional univariate HRV measures. The real-time CRC algorithm shows promise for monitoring nociception during general anesthesia.