Transition dynamics between two states can help elucidate the behavior of sequential events in physiological signals. By detecting transitions between healthy and pathological states within individual patients, we can help clinicians focus attention on critical transitions, to either preemptively treat adverse events or to detect changes resulting from treatments. We introduce a novel application of singlepoint Bayesian online changepoint detection to predict clinical state transitions, and apply this framework to detecting pathological transitions in preterm infants with episodes of apnea and bradycardia. Bayesian analysis of sequential physiological events provides insights on how to objectively classify clinically important state transitions that can be triggered by external or intrinsic mechanisms.