LAUSR

Objective. Exercise oscillatory ventilation (EOV) is frequently observed in individuals with cardiac disease. Assessment of EOV relies on pattern recognition and this subjectivity and lack of quantification limits the widespread clinical use of EOV as a prognostic marker. Poincaré analysis quantifies the short (SD1) and long-term (SD2) variability of a signal and may provide an alternative means to identify and quantify unstable exercise breathing patterns. This study aimed to determine if Poincaré analysis can distinguish between the breathing patterns of healthy control subjects and individuals being assessed for heart transplantation with and without EOV. Approach. Thirty-nine subjects performed a cardiopulmonary exercise test as part of heart transplant assessment and were subjectively classified into two groups according to the presence of EOV: non-EOV (n = 19) and EOV (n = 20). The control group (n = 24) consisted of healthy adults. Poincaré analysis (SD1 and SD2) was performed for minute ventilation ( V̇ E) and tidal volume (VT) normalized to forced vital capacity ( V̇ En and V̇ Tn ), and breathing frequency (BF) for breath-by-breath data over the 10–15 ml · min−1 · kg−1 V̇ O2 range. Main results. Poincaré analysis showed similar exercise ventilatory responses between the non-EOV and control group. BF was found to discriminate between subjects with stable and unstable ventilation. BF SD1 was significantly higher in the EOV group compared to the non-EOV (7.9 versus 4.6, p < 0.01) and control (7.9 versus 4.2, p < 0.01) groups. The EOV group had significantly greater BF SD2 compared to the non-EOV (5.7 versus 3.5, p < 0.01) and control (5.7 versus 3.5, p < 0.01) groups. Significance. We demonstrated that this novel application of Poincaré analysis can objectively distinguish and quantify unstable from stable breathing patterns during exercise. In subjects being assessed for heart transplantation the presence of EOV is associated with greater BF variability. Poincaré analysis provides an objective measure to identify and quantify EOV. Summary at a glance. As EOV may indicate abnormal ventilatory control, there is a need for an objective measure to identify and quantify unstable from stable ventilation during exercise. We developed a method of quantifying BF variation by the application of Poincaré analysis and demonstrated higher than normal variability of BF in subjects being assessed for heart transplantation who demonstrated EOV.