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,

S. Mohamad-Samuri1, M. Mahfouf1, M. Denaï2, J.J. Ross3 and G.H. Mills3

1 Dept of Automatic Control and Systems Eng, University of Sheffield, Sheffield, UK2 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 ABSOLUTE EIT COUPLED TO A BLOOD GAS PHYSIOLOGICAL MODEL FOR THE GAS PHYSIOLOGICAL MODEL FOR THE

ASSESSMENT OF LUNG VENTILATION IN ASSESSMENT OF LUNG VENTILATION IN CRITICAL CARE PATIENTSCRITICAL CARE PATIENTS

• Overview of the SOPAVentOverview of the SOPAVent

• Absolute Electrical Impedance Absolute Electrical Impedance Tomography (aEIT) of the lungs: OverviewTomography (aEIT) of the lungs: Overview

• Clinical trial of aEIT Clinical trial of aEIT

• Coupling aEIT and SOPAVent Coupling aEIT and SOPAVent

• Conclusion and future workConclusion and future work

• Modelling of Mean End Expiratory lung Modelling of Mean End Expiratory lung Volumes (MEEV): Neuro-Fuzzy ApproachVolumes (MEEV): Neuro-Fuzzy Approach

2ESCTAIC 6-9 October 2010, Amsterdam Netherland

OutlineOutline

Commercial EIT SystemCommercial EIT System

Research prototypeResearch prototype

• Hardware Hardware

Drive patternDrive pattern AdjacentAdjacent

No. of electrodesNo. of electrodes 88

FrequenciesFrequencies 30:2 kHz – 1.6 MHz30:2 kHz – 1.6 MHz

TechnologyTechnology DigitalDigital

DateDate 20002000


aEIT of the Lungs: OverviewaEIT of the Lungs: Overview



• Steps to determine the absolute lung resistivity Steps to determine the absolute lung resistivity

5

Real EIT Real EIT datadata

Model predicted EIT data Match ?Match ?

3D finite difference model adjusted to the real EIT data

NN

YY

Absolute lung resistivity

current injection current injection and EIT data and EIT data

measurementmeasurement

Patient


ESCTAIC 6-9 October 2010, Amsterdam Netherland

• Absolute lung resistivity flow chart Absolute lung resistivity flow chart


Clinical trial of aEITClinical 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

• MethodsMethods

Gender Height (cm) Chest Circumference (cm)

Elipse ratio

Mean + S.D 7 males, 1 female

169.8 + 6.41 94.6 + 4.10 1.38 + 0.09


• Demographic information of the patientsDemographic information of the patients

DayVentilation

mode

Ventilator settings EIT Outputs

ΔASBPEEP

(cmH2O)

Pinsp

(cmH2O)

FiO2

(%)

VT

(litre)

MV

(litre)

MEEV

(litre)

MVT

(litre)1 BIPAP 0 12 30 55 0.65 11.6 6.21 0.772 BIPAP 12 12 30 40 0.72 13.3 5.31 0.622 BIPAP 12 10 22 40 0.66 10.4 4.72 0.83 BIPAP 10 10 20 50 1.11 9.5 3.59 1.124 CPAP 3 10 20 45 0.92 13.6 4.36 0.82

• An example of patient’s ventilator settings, MEEV and MVT An example of patient’s ventilator settings, MEEV and MVT


Abs

olu

te r

esis

tivity

Ω

.m

PEEP=12 cmH₂O

12 cmH₂O 10 cmH₂O 10 cmH₂O 10 cmH₂O

Abs

olu

te lu

ng

air

volu

mes

(lit

res)

Day 1 Day 2 Day 3 Day 4


• Lung absolute resistivity and air volume measured by aEIT Lung absolute resistivity and air volume measured by aEIT at different PEEP levels on an ITU patientat different PEEP levels on an ITU patient



ANFIS modelling of MEEVANFIS modelling of MEEV

• What is ANFIS?What is ANFIS?

Stands for Adaptive Neural-Fuzzy Inference Systems Adaptive Neural-Fuzzy Inference Systems [1][1]

Hybrid system that operates on both linguistic descriptions linguistic descriptions of the variables and the numeric values numeric values

Neural-Fuzzy model incorporate human expertisehuman expertise as well as adapt itself through repeated learningrepeated 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.


• ANFIS architectureANFIS architecture

ANFIS consists of a set of TSK-type fuzzy IF-THEN 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)IF x is A and y is B THEN z = ƒ(x,y)

Where AA and BB are fuzzy setsfuzzy sets in the antecedentantecedent, while zz = ƒ(x,y)ƒ(x,y) is a crisp function crisp function in the consequentconsequent


4 4.1 4.2 4.3 4.4 4.5 4.6 4.7 4.8

0

0.2

0.4

0.6

0.8

1

PaC02

Degre

e o

f m

em

bers

hip

11 12 13 14 15 16 17 18

0

0.2

0.4

0.6

0.8

1

RR

Degre

e o

f m

em

bers

hip



• ANFIS model structureANFIS model structure

PIP

RR

PEEP

Pinsp

PaO2/FiO2

PaCO2

MEEV

input input mf rule output mf output

example of Gaussian MFANFIS Structure

6 inputs, 1 output

4 membership functions for each input

5 fuzzy rules



• ResultsResults

ANFIS architecture has demonstrated a good performance in modelling the MEEV

1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6

5

5.2

5.4

5.6

5.8

6

6.2

6.4

Data

ME

EV

(lit

res)

Model training results

Actual outputANFIS predicted output

0 1 2 3 4 5 6 7 80

2

4

6

8

10

real

pred

ictio

n

correlation between actual and predicted output

0% Error Line

Model Predictions

+/- 10% Error Line

1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6-1

-0.5

0

0.5

1

MAE= 0.0050004

error between actual and predicted output

data

erro

s

Overview of SOPAVentOverview of SOPAVent

• What is SOPAVent?What is SOPAVent?

Simulation of Patients under Artificial VentilationSimulation of Patients under Artificial Ventilation

The model representsrepresents the exchange of Oexchange of O22 and COCO22 in the lungs lungs and

tissuestissues together with their transport through the circulatory system circulatory system based on respiratory physiologyrespiratory physiology and mass balance equationsmass balance equations

The model uses a compartmental structurecompartmental structure, where the circulatory circulatory system system is represented by lumped arterial, tissue, venous and lumped arterial, tissue, venous and pulmonary compartments. pulmonary compartments.



The lung is sub-divided into three compartments:

a)an ideal alveolus compartment, where all gas exchange takes placewith 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.


CO2 O2

CO2 O2

Shunted Blood

Ventilator

Pulmonary Capillary Bed

Dead space Ideal Alveoli

Arterial Pool

Tissue Capillary Bed

Metabolised Tissue

Venous Pool

Expired Gas



The inputsinputs of the model are the ventilator settings (FiO(FiO22, PEEP, PIP, RR, , PEEP, PIP, RR,

TTinspinsp) ) and the outputsoutputs are the arterial pressures PaO2 and PaCO2the 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.


Coupling aEIT and SOPAVent Coupling aEIT and SOPAVent

• ObjectiveObjective

To simulate the effect of reducing PEEP to changes of MEEV (predicted from ANFIS model), PaO2 and PaCO2 (predicted from SOPAVent model)

• MethodMethod

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


Coupling aEIT and SOPAVent Coupling aEIT and SOPAVent

• ResultsResults

PEEP=12 11 10 9 8PEEP=12 11 10 9 8

0 50 100 150 200 250 300

6

8

10

12

time (sec)

PE

EP

(cm

H2O

)

PEEP

0 50 100 150 200 250 3004

4.5

5

5.5

6

time(sec)

ME

EV

(litr

es)

MEEV

12

1110

98

5.68

4.76 4.70 4.64 4.58

0 50 100 150 200 250 3008

9

10

11

12

13

time (sec)

PaO

2(m

mH

g)

PaO2

0 50 100 150 200 250 3004

4.2

4.4

4.6

4.8

5

time(sec)

PaC

O2(

mm

Hg)

PaCO2

11.9

10.31 10.09 9.78 9.53

4.294.33 4.30

4.13 4.10


ConclusionConclusion

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


Future workFuture work

SOPAVent: Data-drivenphysiological 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.

THANK YOUTHANK YOU


Documents

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,