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S. Mohamad-Samuri 1 , M. Mahfouf 1 , M. Denaï 2 , J.J. Ross 3 and G.H. Mills 3

S. Mohamad-Samuri 1 , M. Mahfouf 1 , M. Denaï 2 , J.J. Ross 3 and G.H. Mills 3 1 Dept of Automatic Control and Systems Eng, University of Sheffield, Sheffield, UK 2 School of Science and Eng, Teesside University, Middlesbrough, UK

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S. Mohamad-Samuri 1 , M. Mahfouf 1 , M. Denaï 2 , J.J. Ross 3 and G.H. Mills 3

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  1. S. Mohamad-Samuri1, M. Mahfouf1, M. Denaï2, J.J. Ross3 and G.H. Mills3 1Dept of Automatic Control and Systems Eng, University of Sheffield, Sheffield, UK 2 School of Science and Eng, Teesside University, Middlesbrough, UK 3Dept of Critical Care and Anaesthesia, Northern General Hospital, Sheffield, UK ABSOLUTE EIT COUPLED TO A BLOOD GAS PHYSIOLOGICAL MODEL FOR THE ASSESSMENT OF LUNG VENTILATION IN CRITICAL CARE PATIENTS

  2. Outline • Absolute Electrical Impedance • Tomography (aEIT) of the lungs: Overview • Clinical trial of aEIT • Modelling of Mean End Expiratory lung • Volumes (MEEV): Neuro-Fuzzy Approach • Overview of the SOPAVent • Coupling aEIT and SOPAVent • Conclusion and future work ESCTAIC 6-9 October 2010, Amsterdam Netherland

  3. aEIT of the Lungs: Overview • Hardware Research prototype Commercial EIT System ESCTAIC 6-9 October 2010, Amsterdam Netherland

  4. aEIT of the Lungs: Overview • Steps to determine the absolute lung resistivity Collection of impedance measurements from the subject Simulate reference image data using 3D thoracic model The modelled data are compared with the real measurements over a pre-determined region of interest The value of lung resistivity which minimizes the mean difference between these data sets is returned as the value of the absolute lung resistivity ESCTAIC 6-9 October 2010, Amsterdam Netherland 4

  5. aEIT of the Lungs: Overview • Absolute lung resistivity flow chart current injection and EIT data measurement Real EIT data Patient Model predicted EIT data Y Match ? N Absolute lung resistivity 3D finite difference model adjusted to the real EIT data ESCTAIC 6-9 October 2010, Amsterdam Netherland

  6. Clinical trial of aEIT • Objective To validate the ability of the Mk3.5 aEIT system to reflect ventilator settings (PEEP)-induced changes on the lung absolute volume and resistivity in ITU patients • Methods ESCTAIC 6-9 October 2010, Amsterdam Netherland

  7. Clinical trial of aEIT • Demographic information of the patients • An example of patient’s ventilator settings, MEEV and MVT ESCTAIC 6-9 October 2010, Amsterdam Netherland 7

  8. Clinical trial of aEIT • Lung absolute resistivity and air volume measured by aEIT at different PEEP levels on an ITU patient Absolute resistivity Ω.m PEEP=12 cmH₂O 12 cmH₂O 10 cmH₂O 10 cmH₂O 10 cmH₂O Absolute lung air volumes (litres) Day 1 Day 2 Day 3 Day 4 ESCTAIC 6-9 October 2010, Amsterdam Netherland 8

  9. ANFIS modelling of MEEV • What is ANFIS? • Stands for Adaptive Neural-Fuzzy Inference Systems [1] • Hybrid system that operates on both linguistic descriptions • of the variables and the numeric values • Neural-Fuzzy model incorporate human expertiseas well • as adaptitself through repeated learning [1] Jang, J. S. R. (1993). "ANFIS: adaptive-network-based fuzzy inference system." Systems, Man and Cybernetics, IEEE Transactions on 23(3): 665-685. ESCTAIC 6-9 October 2010, Amsterdam Netherland 9

  10. ANFIS modelling of MEEV • ANFIS architecture • ANFIS consists of a set of TSK-type fuzzy IF-THEN rules • A typical fuzzy rule in Sugeno fuzzy model has the following form: • IF x is A and y is B THEN z = ƒ(x,y) • Where A and B are fuzzy setsin the antecedent, while z= ƒ(x,y)is a • crisp function in the consequent ESCTAIC 6-9 October 2010, Amsterdam Netherland 10

  11. ANFIS modelling of MEEV • ANFIS model structure input input mf rule output mf output PIP RR PEEP MEEV Pinsp PaO2/FiO2 PaCO2 ANFIS Structure 6 inputs, 1 output 4 membership functions for each input 5 fuzzy rules example of Gaussian MF ESCTAIC 6-9 October 2010, Amsterdam Netherland 11

  12. ANFIS modelling of MEEV • Results ANFIS architecture has demonstrated a good performance in modelling the MEEV ESCTAIC 6-9 October 2010, Amsterdam Netherland 12

  13. Overview of SOPAVent • What is SOPAVent? • Simulation of Patients under Artificial Ventilation • The model represents the exchange of O2and CO2 in the lungs and • tissues together with their transport through the circulatory system • based on respiratory physiology and mass balance equations • The model uses a compartmental structure, where the circulatory • system is represented by lumped arterial, tissue, venous and • pulmonary compartments. ESCTAIC 6-9 October 2010, Amsterdam Netherland 13

  14. Overview of SOPAVent • The lung is sub-divided into three compartments: • an ideal alveolus compartment, • where all gas exchange takes place • with a perfusion-diffusion ratio of unity. • b) a dead space compartment • representing lung areas that are • ventilated but not perfused • c) a shunt compartment that is a fraction of cardiac output, representing both anatomical shunts and lung areas that are perfused but not ventilated. ESCTAIC 6-9 October 2010, Amsterdam Netherland 14

  15. Overview of SOPAVent • What are the inputs and outputs of the model? • The inputs of the model are the ventilator settings (FiO2, PEEP, PIP, RR, • Tinsp) and the outputs are the arterial pressures PaO2 and PaCO2 • The model parameters are patient-specific and the model can therefore • be matched to each patient provided the parameters are known. ESCTAIC 6-9 October 2010, Amsterdam Netherland 15

  16. Coupling aEIT and SOPAVent • Objective To simulate the effect of reducing PEEP to changes of MEEV (predicted from ANFIS model), PaO2 and PaCO2 (predicted from SOPAVent model) • Method • Loading patients’ specific data (ex: ventilator parameters etc) • The models were run for 300 seconds. PEEP was set at the initial value of • 12 cmH₂O and gradually decreased to 11cmH₂O, 10cmH₂O, 9 cmH₂O and • 8 cmH₂O, while all other ventilator settings remain constant • Changes in MEEV, PaO2 and PaCO2 were observed and recorded ESCTAIC 6-9 October 2010, Amsterdam Netherland 16

  17. Coupling aEIT and SOPAVent • Results 12 11.9 11 10 9 10.31 10.09 8 9.78 9.53 PEEP=12 11 10 9 8 5.68 4.76 4.33 4.30 4.70 4.64 4.29 4.58 4.13 4.10 ESCTAIC 6-9 October 2010, Amsterdam Netherland 17

  18. Conclusion • aEIT is capable of tracking local changes in pulmonary air contents and thus • can be used to continuously guide the appropriate setting of mechanical • ventilation in critical care patients • Mean end-expiratory lung volume (MEEV) calculated from aEIT is a feature • parameter that reveals volume of air present in the lungs at the end of • patients’ expiration • Both models are capable of providing information on patients’ lung • behaviour in response to ventilation therapy • More ventilated patients EIT data are needed to further improve the accuracy of • MEEV prediction ESCTAIC 6-9 October 2010, Amsterdam Netherland 18

  19. Future work By using information from both aEIT and SOPAVent models should lead to a better understanding of phenomena surrounding ventilated patients in order to support decision-making and guide ventilator therapy. SOPAVent: Data-driven physiological model of patient’s blood gases Decision support system Sheffield aEIT MK 3.5 system ESCTAIC 6-9 October 2010, Amsterdam Netherland 19

  20. THANK YOU ESCTAIC 6-9 October 2010, Amsterdam Netherland 20

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