CAREDAQ: Data acquisition device for mechanical ventilation waveform monitoring

[Display omitted] 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 inf...

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Published inHardwareX Vol. 12; p. e00358
Main Authors Arn Ng, Qing, Yew Shuen Ang, Christopher, Shiong Chiew, Yeong, Wang, Xin, Pin Tan, Chee, Basri Mat Nor, Mohd, Salwa Damanhuri, Nor, Geoffrey Chase, J.
Format Journal Article
LanguageEnglish
Published England Elsevier Ltd 01.10.2022
Elsevier
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Online AccessGet full text
ISSN2468-0672
2468-0672
DOI10.1016/j.ohx.2022.e00358

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Summary:[Display omitted] 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.
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ISSN:2468-0672
2468-0672
DOI:10.1016/j.ohx.2022.e00358