TY - JOUR
T1 - CAREDAQ
T2 - Data acquisition device for mechanical ventilation waveform monitoring
AU - Arn Ng, Qing
AU - Yew Shuen Ang, Christopher
AU - Shiong Chiew, Yeong
AU - Wang, Xin
AU - Pin Tan, Chee
AU - Basri Mat Nor, Mohd
AU - Salwa Damanhuri, Nor
AU - Geoffrey Chase, J.
N1 - Funding Information:
The authors would like to thank the Ministry of Energy, Science, Technology, Environment and Climate Change (MESTECC) research grant (IF0219I1060), the MedTech Centre of Research Expertise, University of Canterbury, New Zealand and Monash University Malaysia Advance Engineering Platform (AEP) for supporting of this research.
Funding Information:
This study received a research grant (IF0219I1060) from the Ministry of Energy, Science, Technology, Environment and Climate Change (MESTECC).
Publisher Copyright:
© 2022
PY - 2022/10
Y1 - 2022/10
N2 - Mechanical ventilation (MV) provides respiratory support for critically ill patients in the intensive care unit (ICU). Waveform data output by the ventilator provides valuable physiological and diagnostic information. However, existing systems do not provide full access to this information nor allow for real-time, non-invasive data collection. Therefore, large amounts of data are lost and analysis is limited to short samples of breathing cycles. This study presents a data acquisition device for acquiring and monitoring patient ventilation waveform data. Acquired data can be exported to other systems, allowing users to further analyse data and develop further clinically useful parameters. These parameters, together with other ventilatory information, can help personalise and guide MV treatment. The device is designed to be easily replicable, low-cost, and scalable according to the number of patient beds. Validation was carried out by assessing system performance and stability over prolonged periods of 7 days of continuous use. The device provides a platform for future integration of machine-learning or model-based modules, potentially allowing real-time, proactive, patient-specific MV guidance and decision support to improve the quality and productivity of care and outcomes.
AB - Mechanical ventilation (MV) provides respiratory support for critically ill patients in the intensive care unit (ICU). Waveform data output by the ventilator provides valuable physiological and diagnostic information. However, existing systems do not provide full access to this information nor allow for real-time, non-invasive data collection. Therefore, large amounts of data are lost and analysis is limited to short samples of breathing cycles. This study presents a data acquisition device for acquiring and monitoring patient ventilation waveform data. Acquired data can be exported to other systems, allowing users to further analyse data and develop further clinically useful parameters. These parameters, together with other ventilatory information, can help personalise and guide MV treatment. The device is designed to be easily replicable, low-cost, and scalable according to the number of patient beds. Validation was carried out by assessing system performance and stability over prolonged periods of 7 days of continuous use. The device provides a platform for future integration of machine-learning or model-based modules, potentially allowing real-time, proactive, patient-specific MV guidance and decision support to improve the quality and productivity of care and outcomes.
KW - Data acquisition device
KW - Mechanical ventilation
KW - Ventilator waveform data
UR - http://www.scopus.com/inward/record.url?scp=85137635088&partnerID=8YFLogxK
U2 - 10.1016/j.ohx.2022.e00358
DO - 10.1016/j.ohx.2022.e00358
M3 - Article
C2 - 36117541
AN - SCOPUS:85137635088
SN - 2468-0672
VL - 12
JO - HardwareX
JF - HardwareX
M1 - e00358
ER -