The Analysis of Respiratory Kinematics method using motion sensors showed good agreement with flow-derived respiratory rate signals, achieving cross-correlation coefficients as high as 0.94.
Observational (n=34)
No
Does the Analysis of Respiratory Kinematics method accurately derive respiratory rate signals compared to volumetric air flow in volunteers undergoing exercise testing?
The Analysis of Respiratory Kinematics method can accurately derive highly resolved respiratory rate signals from motion sensors during exercise, though the relationship between kinematics and flow is complex and variable.
Effect estimate: cross-correlation coefficients as high as 0.94
Abstract 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.
Ashe et al. (Sun,) conducted a observational in Healthy volunteers (n=34). Analysis of Respiratory Kinematics (ARK) using motion sensors vs. Volumetric air flow signals was evaluated on Cross-correlation between kinematics-derived and flow-derived respiratory rate signals (cross-correlation coefficients as high as 0.94). The Analysis of Respiratory Kinematics method using motion sensors showed good agreement with flow-derived respiratory rate signals, achieving cross-correlation coefficients as high as 0.94.
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