1. 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 3 Dept 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. Overview of the SOPAVent Overview of the SOPAVent Absolute Electrical Impedance Absolute Electrical Impedance Tomography (aEIT) of the lungs: Overview Tomography (aEIT) of the lungs: Overview Clinical trial of aEIT Clinical trial of aEIT Coupling aEIT and SOPAVent Coupling aEIT and SOPAVent Conclusion and future work Conclusion and future work Modelling of Mean End Expiratory lung Modelling of Mean End Expiratory lung Volumes (MEEV): Neuro-Fuzzy Approach Volumes (MEEV): Neuro-Fuzzy Approach 2 ESCTAIC 6-9 October 2010, Amsterdam Netherland Outline

3. Commercial EIT System Research prototype HardwareHardware Drive pattern Adjacent No. of electrodes 8 Frequencies 30:2 kHz – 1.6 MHz TechnologyDigital Date2000 3 ESCTAIC 6-9 October 2010, Amsterdam Netherland aEIT of the Lungs: Overview

4. 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 Collection of impedance measurements from the subject Simulate reference image data using 3D thoracic model aEIT of the Lungs: Overview 4 ESCTAIC 6-9 October 2010, Amsterdam Netherland Steps to determine the absolute lung resistivitySteps to determine the absolute lung resistivity

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

6. 6 ESCTAIC 6-9 October 2010, Amsterdam Netherland Clinical trial of aEIT 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 ObjectiveObjective MethodsMethodsEquipment Sheffield EIT MK 3.5, with 8 Sheffield EIT MK 3.5, with 8 Skintact Premier ECG Electrodes No. of Subjects Eight (8) ITU patients PositionSupine Extracted aEIT measurement Mean End Expiratory Lung Volume (MEEV)

7. GenderHeight (cm)Chest Circumference (cm) Elipse ratio Mean + S.D7 males, 1 female 169.8 + 6.4194.6 + 4.101.38 + 0.09 Clinical trial of aEIT Demographic information of the patientsDemographic information of the patients Day Ventilation mode Ventilator settingsEIT Outputs ΔASB PEEP (cmH 2 O) Pinsp (cmH 2 O) FiO 2 (%) V T (litre) MV (litre) MEEV (litre) MV T (litre) 1BIPAP01230550.6511.66.210.77 2BIPAP12 30400.7213.35.310.62 2BIPAP121022400.6610.44.720.8 3BIPAP10 20501.119.53.591.12 4CPAP31020450.9213.64.360.82 An example of patient’s ventilator settings, MEEV and MVTAn example of patient’s ventilator settings, MEEV and MVT 7 ESCTAIC 6-9 October 2010, Amsterdam Netherland

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

9. 9 ANFIS modelling of MEEV What is ANFIS?What is ANFIS? Adaptive Neural-Fuzzy Inference Systems [1]  Stands for Adaptive Neural-Fuzzy Inference Systems [1] linguistic descriptions  Hybrid system that operates on both linguistic descriptions numeric values of the variables and the numeric values human expertise  Neural-Fuzzy model incorporate human expertise as well repeated learning as adapt itself 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.

10. ANFIS modelling of MEEV ANFIS architectureANFIS architecture TSK-type fuzzy IF-THEN  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) ABfuzzy sets antecedentz ƒ(x,y) Where A and B are fuzzy sets in the antecedent, while z = ƒ(x,y) is a crisp function consequent crisp function in the consequent 10 ESCTAIC 6-9 October 2010, Amsterdam Netherland

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

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

13. Overview of SOPAVent What is SOPAVent?What is SOPAVent? Simulation of Patients under Artificial Ventilation  Simulation of Patients under Artificial Ventilation representsexchange of O 2 CO 2 lungs  The model represents the exchange of O 2 and CO 2 in the lungs and tissuescirculatory system tissues together with their transport through the circulatory system respiratory physiologymass balance equations based on respiratory physiology and mass balance equations compartmental structurecirculatory  The model uses a compartmental structure, where the circulatory system lumped arterial, tissue, venous and system is represented by lumped arterial, tissue, venous and pulmonary compartments. pulmonary compartments. 13 ESCTAIC 6-9 October 2010, Amsterdam Netherland

14. Overview of SOPAVent  The lung is sub-divided into three compartments: a)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. 14 ESCTAIC 6-9 October 2010, Amsterdam Netherland

15. 15 ESCTAIC 6-9 October 2010, Amsterdam Netherland Overview of SOPAVent inputs(FiO 2, PEEP, PIP, RR,  The inputs of the model are the ventilator settings (FiO 2, PEEP, PIP, RR, T insp ) outputsthe arterial pressures PaO2 and PaCO2 T insp ) and the outputs are the arterial pressures PaO2 and PaCO2 What are the inputs and outputs of the model? What are the inputs and outputs of the model?  The model parameters are patient-specific and the model can therefore be matched to each patient provided the parameters are known.

16. 16 ESCTAIC 6-9 October 2010, Amsterdam Netherland 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 cmH ₂ O and gradually decreased to 11cmH ₂ O, 10cmH ₂ O, 9 cmH ₂ O and 8 cmH ₂ O, while all other ventilator settings remain constant  Changes in MEEV, PaO2 and PaCO2 were observed and recorded

17. 17 ESCTAIC 6-9 October 2010, Amsterdam Netherland Coupling aEIT and SOPAVent ResultsResults PEEP=12 11 10 9 8 12 11 10 9 8 5.68 4.76 4.70 4.64 4.58 11.9 10.31 10.09 9.78 9.53 4.29 4.33 4.30 4.134.10

18. 18 ESCTAIC 6-9 October 2010, Amsterdam Netherland Conclusion  More ventilated patients EIT data are needed to further improve the accuracy of MEEV prediction  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  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

19. 19 ESCTAIC 6-9 October 2010, Amsterdam Netherland Future work SOPAVent: Data-driven physiological model of patient’s blood gases Sheffield aEIT MK 3.5 system Decision support system 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.

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