OBJECTIVE: While many studies rely on flow and pressure measurements in small animal models of respiratory disease, such measurements can however be inaccurate and difficult to obtain. Thus, the goal of this study was to design and implement an easy-to-manufacture and accurate sensor capable of monitoring flow. APPROACH: We designed and 3D printed a flowmeter and utilized parametric (resistance and inertance) and nonparametric (polynomial and Volterra series) system identification to characterize the device. The sensor was tested in a closed system for apparent flow using the common mode rejection ratio (CMRR). The sensor properly measured tidal volumes and respiratory rates in spontaneously breathing mice. The device was used to evaluate a ventilator's ability to deliver a prescribed volume before and after lung injury. MAIN RESULTS: The parametric and polynomial models provided a reasonable prediction of the independently measured flow (Adjusted coefficient of determination [Formula: see text] = 0.9591 and 0.9147 respectively), but the Volterra series of the 1st, 2nd, and 3rd order with a memory of six time points provided better fits ([Formula: see text] = 0.9775, 0.9787, and 0.9954, respectively). At and below the mouse breathing frequency (1-5 Hz), CMRR was higher than 40 dB. Following lung injury, the sensor revealed a significant drop in delivered tidal volume. SIGNIFICANCE: We demonstrate that the application of nonparametric nonlinear Volterra series modeling in combination with 3D printing technology allows the inexpensive and rapid fabrication of an accurate flow sensor for continuously measuring small flows in various physiological conditions.
OBJECTIVE: While many studies rely on flow and pressure measurements in small animal models of respiratory disease, such measurements can however be inaccurate and difficult to obtain. Thus, the goal of this study was to design and implement an easy-to-manufacture and accurate sensor capable of monitoring flow. APPROACH: We designed and 3D printed a flowmeter and utilized parametric (resistance and inertance) and nonparametric (polynomial and Volterra series) system identification to characterize the device. The sensor was tested in a closed system for apparent flow using the common mode rejection ratio (CMRR). The sensor properly measured tidal volumes and respiratory rates in spontaneously breathing mice. The device was used to evaluate a ventilator's ability to deliver a prescribed volume before and after lung injury. MAIN RESULTS: The parametric and polynomial models provided a reasonable prediction of the independently measured flow (Adjusted coefficient of determination [Formula: see text] = 0.9591 and 0.9147 respectively), but the Volterra series of the 1st, 2nd, and 3rd order with a memory of six time points provided better fits ([Formula: see text] = 0.9775, 0.9787, and 0.9954, respectively). At and below the mouse breathing frequency (1-5 Hz), CMRR was higher than 40 dB. Following lung injury, the sensor revealed a significant drop in delivered tidal volume. SIGNIFICANCE: We demonstrate that the application of nonparametric nonlinear Volterra series modeling in combination with 3D printing technology allows the inexpensive and rapid fabrication of an accurate flow sensor for continuously measuring small flows in various physiological conditions.
Authors: Toby K McGovern; Annette Robichaud; Liah Fereydoonzad; Thomas F Schuessler; James G Martin Journal: J Vis Exp Date: 2013-05-15 Impact factor: 1.355
Authors: Samer Bou Jawde; Allan J Walkey; Arnab Majumdar; George T O'Connor; Bradford J Smith; Jason H T Bates; Kenneth R Lutchen; Béla Suki Journal: Sci Rep Date: 2020-04-21 Impact factor: 4.379