Network data acquisition and monitoring system for intensive care mechanical ventilation treatment

• Main Authors: Qing, Arn Ng, Chiew, Yeong Shiong, Xin, Wang, Chee, Pin Tan, Mat Nor, Mohd Basri, Damanhuri, Nor Salwa, Chase, Geoffrey
• Format: Article
• Language: English; English
• Published: Institute of Electrical and Electronics Engineers Inc. 2021
• Subjects: RC82 Medical Emergencies, Critical Care, Intensive Care, First Aid; TJ181 Mechanical movements
• Online Access: http://irep.iium.edu.my/90571/7/90571_Network%20data%20Acquisition%20and%20Monitoring%20System.pdf; http://irep.iium.edu.my/90571/13/90571_Network%20Data%20Acquisition%20and%20Monitoring%20System%20for%20Intensive%20Care%20Mechanical%20Ventilation%20Treatment_Scopus.pdf; http://irep.iium.edu.my/90571/; https://ieeeaccess.ieee.org/

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.