Optimizing Patient-Ventilator Synchrony Utilizing Radar-Based Respiratory Features for Monitoring COVID-19 Patients

During this COVID-19 pandemic time, an unprecedented number of patients with severe respiratory illness require intensive care units (ICUs) under mechanical ventilation (MV) for sustaining life. Patient-ventilator asynchrony (PVA) is very common, and it occurs due to the mismatch between the normal variability of the patients' breathing patterns and ventilator parameters. Asynchronies during invasive ventilation are causing the patients discomfort, fatigue, anxiety, neurovascular nerve damage, and mortality. However, currently, the only way to detect the asynchrony is through visual inspections by the healthcare professionals and adjust manually. In this article, we propose an opinion on the conceptual framework of a system composed of radio frequency (RF)-based noncontact life-sensing technology that can extract different respiratory features unobtrusively and continuously and can reduce the patient-ventilator asynchrony. After extracting respiratory features of patients from the radar data, it can provide optimally and continuously supplemental oxygen by adjusting the function of the existing mechanical ventilator. This will reduce the sufferings and mortalities, as well as less stress for emergency nurses and doctors to handle patients more effectively.