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

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. Howeve...

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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
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spelling my.iium.irep.905712021-08-27T00:25:15Z http://irep.iium.edu.my/90571/ Network data acquisition and monitoring system for intensive care mechanical ventilation treatment Qing, Arn Ng Chiew, Yeong Shiong Xin, Wang Chee, Pin Tan Mat Nor, Mohd Basri Damanhuri, Nor Salwa Chase, Geoffrey RC82 Medical Emergencies, Critical Care, Intensive Care, First Aid TJ181 Mechanical movements 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. Institute of Electrical and Electronics Engineers Inc. 2021-06-24 Article PeerReviewed application/pdf en http://irep.iium.edu.my/90571/7/90571_Network%20data%20Acquisition%20and%20Monitoring%20System.pdf application/pdf en http://irep.iium.edu.my/90571/13/90571_Network%20Data%20Acquisition%20and%20Monitoring%20System%20for%20Intensive%20Care%20Mechanical%20Ventilation%20Treatment_Scopus.pdf Qing, Arn Ng and Chiew, Yeong Shiong and Xin, Wang and Chee, Pin Tan and Mat Nor, Mohd Basri and Damanhuri, Nor Salwa and Chase, Geoffrey (2021) Network data acquisition and monitoring system for intensive care mechanical ventilation treatment. IEEE Access, 9. pp. 91859-91873. ISSN 2169-3536 E-ISSN 2169-3536 https://ieeeaccess.ieee.org/ 10.1109/ACCESS.2021.3092194
institution Universiti Islam Antarabangsa Malaysia
building IIUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider International Islamic University Malaysia
content_source IIUM Repository (IREP)
url_provider http://irep.iium.edu.my/
language English
English
topic RC82 Medical Emergencies, Critical Care, Intensive Care, First Aid
TJ181 Mechanical movements
spellingShingle RC82 Medical Emergencies, Critical Care, Intensive Care, First Aid
TJ181 Mechanical movements
Qing, Arn Ng
Chiew, Yeong Shiong
Xin, Wang
Chee, Pin Tan
Mat Nor, Mohd Basri
Damanhuri, Nor Salwa
Chase, Geoffrey
Network data acquisition and monitoring system for intensive care mechanical ventilation treatment
description 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.
format Article
author Qing, Arn Ng
Chiew, Yeong Shiong
Xin, Wang
Chee, Pin Tan
Mat Nor, Mohd Basri
Damanhuri, Nor Salwa
Chase, Geoffrey
author_facet Qing, Arn Ng
Chiew, Yeong Shiong
Xin, Wang
Chee, Pin Tan
Mat Nor, Mohd Basri
Damanhuri, Nor Salwa
Chase, Geoffrey
author_sort Qing, Arn Ng
title Network data acquisition and monitoring system for intensive care mechanical ventilation treatment
title_short Network data acquisition and monitoring system for intensive care mechanical ventilation treatment
title_full Network data acquisition and monitoring system for intensive care mechanical ventilation treatment
title_fullStr Network data acquisition and monitoring system for intensive care mechanical ventilation treatment
title_full_unstemmed Network data acquisition and monitoring system for intensive care mechanical ventilation treatment
title_sort network data acquisition and monitoring system for intensive care mechanical ventilation treatment
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2021
url 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/
_version_ 1709667141654937600
score 13.211869

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