Breathing motion (respiratory kinematics) can be characterized by the interval and depth of each breath, and by magnitude-synchrony relationships between locations. Such characteristics and their breath-by-breath variability might be useful indicators of respiratory health. To enable breath-by-breath characterization of respiratory kinematics, we developed a method to detect breaths using motion sensors. In 34 volunteers who underwent maximal exercise testing, we used 8 motion sensors to record upper rib, lower rib and abdominal kinematics at 3 exercise stages (rest, lactate threshold and exhaustion). We recorded volumetric air flow signals using clinical exercise laboratory equipment and synchronized them with kinematic signals. Using instantaneous phase landmarks from the analytic representation of kinematic and flow signals, we identified individual breaths and derived respiratory rate (RR) signals at 1Hz. To evaluate the fidelity of kinematics-derived RR, we calculated bias, limits of agreement, and cross-correlation coefficients (CCC) relative to flow-derived RR. To identify coupling between kinematics and flow, we calculated the Shannon entropy of the relative frequency with which flow landmarks were distributed over the phase of the kinematic cycle. We found good agreement in the kinematics-derived and flow-derived RR signals [bias (95% limit of agreement) = 0.1 (± 7) breaths/minute; CCC median (IQR) = 0.80 (0.48 – 0.91)]. In individual signals, kinematics and flow were well-coupled (entropy 0.9-1.4 across sensors), but the relationship varied within (by exercise stage) and between individuals. The final result was that the flow landmarks did not consistently localize to any particular phase of the kinematic signals (entropy 2.2–3.0 across sensors). The Analysis of Respiratory Kinematics method can yield highly resolved respiratory rate signals by separating individual breaths. This method will facilitate characterization of clinically significant breathing motion patterns on a breath-by-breath basis. The relationship between respiratory kinematics and flow is much more complex than expected, varying between and within individuals.
Breathing patterns (respiratory kinematics) contain vital prognostic information. They report on a dimension of physiology that is not captured by conventional vital signs. But for an informative physiomarker to become clinically valuable, it must be measurable with ease, accuracy, and reproducibility. We sought to enable the quantitative characterization of respiratory kinematics at the bedside. Using inertial sensors, we analyzed upper rib, lower rib, and abdominal motion of 108 patients with respiratory symptoms during a hospital encounter (582 two-minute recordings). We measured the average respiratory rate and 33 other signal characteristics that had an explainable correspondence with clinically significant breathing patterns. K-means clustering revealed that the respiratory kinematic information was optimally represented by adding 3 novel measures to the average respiratory rate. We selected measures representing respiratory rate variability, respiratory alternans (rib-predominant breaths alternating with abdomen-predominant ones), and recruitment of accessory muscles (increased upper rib excursion). Latent profile analysis of these measures revealed a phenotype consistent with labored breathing. Poisson regression showed that the rate at which a patient's recordings exhibited the labored breathing phenotype was significantly associated (p<0.01) with the severity of illness (discharge home v/s acute-care hospitalization v/s critical-care hospitalization). Notably, labored breathing was frequently detectable (21%) when the respiratory rate was normal, and it improved discrimination for critical illness. These findings validate the feasibility of respiratory kinematic phenotyping in routine healthcare settings, and demonstrate its clinical value. Further research into respiratory kinematic characteristics may reveal novel pathophysiologic mechanisms, advance the efficacy of predictive analytics, and enhance patient safety.
Rationale: Breathing motion (respiratory kinematics) can be characterized by the interval and depth of each breath, and by magnitude-synchrony relationships between locations. Such characteristics and their breath-by-breath variability might be useful indicators of respiratory health. Objectives: To enable breath-by-breath characterization of respiratory kinematics, we developed a method to detect breaths using motion sensor signals. Methods: In 34 volunteers who underwent maximal exercise testing, we used 8 motion sensors to record upper rib, lower rib and abdominal kinematics at 3 exercise stages (rest, lactate threshold and exhaustion). We recorded volumetric air flow signals using clinical exercise laboratory equipment and synchronized them with kinematic signals. Using instantaneous phase landmarks from the analytic representation of kinematic and flow signals, we identified individual breaths and derived respiratory rate signals at 1Hz. To evaluate the fidelity of kinematics-derived respiratory rate signals, we calculated their cross-correlation with the flow-derived respiratory rate signals. To identify coupling between kinematics and flow, we calculated the Shannon entropy of the relative frequency with which kinematic phase landmarks were distributed over the phase of the flow cycle. Measurements and Main Results: We found good agreement in the kinematics-derived and flow-derived respiratory rate signals, with cross-correlation coefficients as high as 0.94. In some individuals, the kinematics and flow were significantly coupled (Shannon entropy < 2) but the relationship varied within (by exercise stage) and between individuals. The final result was that the phase landmarks from the kinematic signal were uniformly distributed over the phase of the air flow signals (Shannon entropy close to the theoretical maximum of 3.32). Conclusions: The Analysis of Respiratory Kinematics method can yield highly resolved respiratory rate signals by separating individual breaths. This method will facilitate characterization of clinically significant breathing motion patterns on a breath-by-breath basis. The relationship between respiratory kinematics and flow is much more complex than expected, varying between and within individuals.
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