UC San Diego

Non-invasive assessment of ventilatory control stability or loop gain (which is a key contributor in a number of sleep-related breathing disorders) has proven to be cumbersome. We present a novel multivariate autoregressive model that we hypothesize will enable us to make time-varying measurements of loop gain using nothing more than spontaneous fluctuations in ventilation and CO2. The model is adaptive to changes in the feedback control loop and therefore can account for system non-stationarities (e.g. changes in sleep state) and it is resistant to artifacts by using a signal quality measure. We tested this method by assessing its ability to detect a known increase in loop gain induced by proportional assist ventilation (PAV). Subjects were studied during sleep while breathing on continuous positive airway pressure (CPAP) alone (to stabilize the airway) or on CPAP+PAV. We show that the method tracked the PAV-induced increase in loop gain, demonstrating its time-varying capabilities, and it remained accurate in the face of measurement related artifacts. The model was able to detect a statistically significant increase in loop gain from 0.14±10 on CPAP alone to 0.21±0.13 on CPAP+PAV (p<0.05). Furthermore, our method correctly detected that the PAV-induced increase in loop gain was predominantly driven by an increase in controller gain. Taken together, these data provide compelling evidence for the validity of this technique.