RespiCo: A novel, flexible, and stand-alone electronic respiratory coaching device

Open-source software for respiratory rate estimation using single-lead electrocardiograms

Respiratory rate (RR) is a critical vital sign used to assess pulmonary function. Currently, RR estimating instrumentation is specialized and bulky, therefore unsuitable for remote health monitoring. Previously, RR was estimated using proprietary software that extract surface electrocardiogram (ECG) waveform features obtained at several thoracic locations. However, developing a non-proprietary method that uses minimal ECG leads, generally available from mobile cardiac monitors is highly desirable. Here, we introduce an open-source and well-documented Python-based algorithm that estimates RR requiring only single-stream ECG signals. The algorithm was first developed using ECGs from awake, spontaneously breathing adult human subjects. The algorithm-estimated RRs exhibited close linear correlation to the subjects' true RR values demonstrating an R2 of 0.9092 and root mean square error of 2.2 bpm. The algorithm robustness was then tested using ECGs generated by the ischemic hearts of anesthetized, mechanically ventilated sheep. Although the ECG waveforms during ischemia exhibited severe morphologic changes, the algorithm-determined RRs exhibited high fidelity with a resolution of 1 bpm, an absolute error of 0.07 ± 0.07 bpm, and a relative error of 0.67 ± 0.64%. This optimized Python-based RR estimation technique will likely be widely adapted for remote lung function assessment in patients with cardiopulmonary disease.

LungElast-an open-source, flexible, low-cost, microprocessor-controlled mouse lung elastometer

The study of mouse lung mechanics provides essential insights into the physiological mechanisms of pulmonary disease. Consequently, investigators assemble custom systems comprising infusion-withdrawal syringe pumps and analog pressure sensors to investigate the lung function of these animals. But these systems are expensive and require ongoing regulation, making them challenging to use. Here I introduce LungElast, an open-source, inexpensive, and self-contained instrument that can experimentally determine lung elasticity and volumes even in immature mice. It is assembled using custom 3D printed parts and readily available or easily constructed components. In this device, a microprocessor-controlled stepper motor automatically regulates lung volume by precisely driving a syringe piston whose position is determined using time-of-flight LIDAR technology. The airway pressures associated with the lung volumes are determined using compact sensor-on-chip technology, retrieved in a digital format, and stored by the microcontroller. The instrument software is modular, which eases device testing, calibration, and use. Data are also provided here that specify the accuracy and precision of the elastometer's sensors and volume delivery and demonstrate its use with lung models and mouse pups. This instrument has excellent potential for research and educational work.