Artificial intelligence driven monitoring and prediction of true sleep health on CPAP machines to improve patient care and reduce the harm of obstructive sleep apnea

In this project we aim to evaluate the potential to effectively predict and prevent the occurrence of obstructive sleep apnea during sleep therapy using multiple sensor inputs in conventional CPAP machines. This will be based on experimental tests during both non-REM and REM sleep and standard polysomnography (PSG) measurements. This project is expected to assess the effectiveness of proactive management and treatment of OSA using real-time monitoring of air pressure and airflow and deep learning predictive models while bringing into account the sleep stages detected by EEG signals.

Monitoring and Analysis of COVID-19 Acute Respiratory Distress Syndrome on Ventilators

Front-line clinicians have reported that different respiratory stiffness results in different COVID-19 patient conditions on ventilators. In this project, we test and improve NovaResp’s monitoring hardware and analyze the collected data for development of algorithms with focus on respiratory stiffness of patients with COVID-19. The resulting algorithms and monitoring device could lead to determine whether patients need to be intubated, and when under ventilation, what ventilatory settings need to be applied for better patient outcome.

Development of a simulation model for prediction of performance of a novel positive airway pressure (PAP) machine for treatment of sleep apnea

The gold standard of treatment for patients with sleep apnea are Positive-airway-pressure (PAP) machines. PAPs provide a one-size-fits-all solution of providing the same therapy in terms of airflow to every patient and every breath. This causes frustration and discomfort for patients, therefore patients don’t purchase PAPs or purchase and don’t use them; leading to 4 times higher chances of stroke and 3 times higher chances of heart attacks as well as huge costs on the healthcare system.