Distinguished Professor José Príncipe was recently awarded an NSF grant in support of development of a low-cost smart multi-patient ventilator system. The proposal, “RAPID: Inexpensive, rapidly manufacturable respiratory monitor to provide safe emergency ventilation during COVID19 pandemic,” provides a portable respiratory monitoring system with several key advantages:
- Safe, remote, monitoring for ventilated patients in isolation rooms—minimizing use of PPE and exposure of clinicians to the virus
- Safe and effective use of non-traditional (emergency) ventilators, assembled from inexpensive, off-the-shelf components
- Safe and effective ventilation of multiple patients with a single FDA-cleared ventilator
- Novel sensor-level signal processing, custom algorithms, and machine learning will enable the collection of data and will provide decision support in a rapidly changing environment
Príncipe’s team has developed a working prototype of the respiratory monitor, which uses a low-cost smart sensor at the patient airway and a standard Android tablet outside the room. The ventilator sharing concept has also been prototyped, but is not yet fully developed. Both devices can be rapidly cleared via FDA Emergency Use Authorization when completed. Importantly, the system alleviates all of the safety problems normally associated with ventilating multiple patients on a single ventilator.
A Long-Term Collaboration
Working with Príncipe on the technology is ECE alumnus (MS ’88 PhD ’98) Dr. Neil Euliano, president and founder of Convergent Engineering, a company which focuses on applying advanced technology to the design of medical devices. The team is leveraging Convergent’s experience and respiratory technology to create the ultra-low cost system. The collaboration between Euliano and Príncipe began decades ago—Príncipe was Euliano’s PhD advisor. Over the past 25 years, they have worked together to innovate medical devices such as electronic pills for medication compliance monitoring, maternal-fetal monitoring using advanced digital signal processing, and ventilators/respiratory monitoring using machine intelligence.