Network Data Acquisition and Monitoring System for Intensive Care Mechanical Ventilation Treatment

• Published in: IEEE Access
• Main Author: Ng Q.A.; Chiew Y.S.; Wang X.; Tan C.P.; Nor M.B.M.; Damanhuri N.S.; Chase J.G.
• Format: Article
• Language: English
• Published: Institute of Electrical and Electronics Engineers Inc. 2021
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112701660&doi=10.1109%2fACCESS.2021.3092194&partnerID=40&md5=230aff00c41cc8c857b775996fcdadda

The rise of model-based and machine learning methods have created increasingly realistic opportunities to implement personalized, patient-specific mechanical ventilation (MV) in the ICU. These methods require monitoring of real-time patient ventilation waveform data (VWD) during MV treatment. However, there are relatively few non-invasive and/or non-proprietary systems to monitor and record patient-specific lung condition in real-time. In this paper, we present a CARE network data acquisition and monitoring system (CARENet) to automate data collection and to remotely monitor patient-specific lung condition and ventilation parameters. The automated system acquires VWD from a mechanical ventilator using a data acquisition device (DAQ), stores data in network-attached storage (NAS), and processes VWDs via a data management platform (DMP) web application. The web application enables real-time and retrospective model-based monitoring and analysis of clinical MV data. In particular, CARENet provides detailed breath-by-breath patient-specific respiratory mechanics, as well as the overall trends assessing changes in patient condition. Validation testing with a retrospective data set illustrated how breath-to-breath and time-varying patient-ventilator interaction during MV can be monitored, and, in turn, can be used to guide MV treatment. The network data acquisition system design presented is low-cost, open, and enables continuous, automated, scalable, real-time collection and analysis of waveform data, to help improve decision making, care, and outcomes in MV. © 2013 IEEE.