University of Hertfordshire

From the same journal

By the same authors

Standard

Absolute Electrical Impedance Tomography (aEIT) Guided Ventilation Therapy in Critical Care Patients : Simulations and Future Trends. / Denai, Mouloud; Mahfouf, M.; Mohamed-Samuri, S.; Panoutsos, G.; Mills, G.H.; Brown, B.H.

In: IEEE Transactions on Information Technology in Biomedicine , Vol. 14, No. 3, 2010, p. 641-649.

Research output: Contribution to journalArticlepeer-review

Harvard

APA

Vancouver

Author

Bibtex

@article{dfafefb28f314234abff0a2b1f44509b,
title = "Absolute Electrical Impedance Tomography (aEIT) Guided Ventilation Therapy in Critical Care Patients: Simulations and Future Trends",
abstract = "Thoracic 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 patients",
author = "Mouloud Denai and M. Mahfouf and S. Mohamed-Samuri and G. Panoutsos and G.H. Mills and B.H. Brown",
year = "2010",
doi = "10.1109/TITB.2009.2036010",
language = "English",
volume = "14",
pages = "641--649",
journal = "IEEE Transactions on Information Technology in Biomedicine ",
issn = "1089-7771",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "3",

}

RIS

TY - JOUR

T1 - Absolute Electrical Impedance Tomography (aEIT) Guided Ventilation Therapy in Critical Care Patients

T2 - Simulations and Future Trends

AU - Denai, Mouloud

AU - Mahfouf, M.

AU - Mohamed-Samuri, S.

AU - Panoutsos, G.

AU - Mills, G.H.

AU - Brown, B.H.

PY - 2010

Y1 - 2010

N2 - Thoracic 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 patients

AB - Thoracic 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 patients

U2 - 10.1109/TITB.2009.2036010

DO - 10.1109/TITB.2009.2036010

M3 - Article

VL - 14

SP - 641

EP - 649

JO - IEEE Transactions on Information Technology in Biomedicine

JF - IEEE Transactions on Information Technology in Biomedicine

SN - 1089-7771

IS - 3

ER -