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dc.contributor.authorDenai, Mouloud
dc.contributor.authorMahfouf, M.
dc.contributor.authorMohamed-Samuri, S.
dc.contributor.authorPanoutsos, G.
dc.contributor.authorMills, G.H.
dc.contributor.authorBrown, B.H.
dc.identifier.citationDenai , M , Mahfouf , M , Mohamed-Samuri , S , Panoutsos , G , Mills , G H & Brown , B H 2010 , ' Absolute Electrical Impedance Tomography (aEIT) Guided Ventilation Therapy in Critical Care Patients : Simulations and Future Trends ' , IEEE Transactions on Information Technology in Biomedicine , vol. 14 , no. 3 , pp. 641-649 .
dc.identifier.otherPURE: 2915550
dc.identifier.otherPURE UUID: dfafefb2-8f31-4234-abff-0a2b1f44509b
dc.identifier.otherScopus: 77953144283
dc.description.abstractThoracic electrical impedance tomography (EIT) is a noninvasive, radiation-free monitoring technique whose aim is to reconstruct a cross-sectional image of the internal spatial distribution of conductivity from electrical measurements made by injecting small alternating currents via an electrode array placed on the surface of the thorax. The purpose of this paper is to discuss the fundamentals of EIT and demonstrate the principles of mechanical ventilation, lung recruitment, and EIT imaging on a comprehensive physiological model, which combines a model of respiratory mechanics, a model of the human lung absolute resistivity as a function of air content, and a 2-D finite-element mesh of the thorax to simulate EIT image reconstruction during mechanical ventilation. The overall model gives a good understanding of respiratory physiology and EIT monitoring techniques in mechanically ventilated patients. The model proposed here was able to reproduce consistent images of ventilation distribution in simulated acutely injured and collapsed lung conditions. A new advisory system architecture integrating a previously developed data-driven physiological model for continuous and noninvasive predictions of blood gas parameters with the regional lung function data/information generated from absolute EIT (aEIT) is proposed for monitoring and ventilator therapy management of critical care patientsen
dc.relation.ispartofIEEE Transactions on Information Technology in Biomedicine
dc.titleAbsolute Electrical Impedance Tomography (aEIT) Guided Ventilation Therapy in Critical Care Patients : Simulations and Future Trendsen
dc.contributor.institutionSchool of Engineering and Computer Science
dc.contributor.institutionDepartment of Engineering and Technology
dc.contributor.institutionCentre for Engineering Research
dc.contributor.institutionCommunications and Intelligent Systems
dc.description.statusPeer reviewed
dc.relation.schoolSchool of Engineering and Computer Science
rioxxterms.typeJournal Article/Review

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