Patient-Ventilator Asynchrony Detection via Similarity Search Methods

Abstract

Patient-ventilator asynchrony (PVA) is a common cause of ventilation-related medical complications and are traditionally only able to be reliably identified by trained clinicians. The need for constant monitoring and limited access to trained experts are major challenges in managing PVA, both of which can potentially be solved by automating the detection process. In this research, we propose a new data-driven approach to PVA detection using several similarity and randomness measures, including how unusual a time window is in the series and randomness of the time window. We found that all these similarity or randomness measures can be estimated with variants of the highly efficient Matrix Pro..