S. Mohamad-Samuri 1 , M. Mahfouf 1 , M. Denaï 2 , J.J. Ross 3 and G.H. Mills 3
description
Transcript of S. Mohamad-Samuri 1 , M. Mahfouf 1 , M. Denaï 2 , J.J. Ross 3 and G.H. Mills 3
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
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OutlineOutline
Commercial EIT SystemCommercial EIT System
Research prototypeResearch prototype
• Hardware Hardware
Drive patternDrive pattern AdjacentAdjacentNo. of electrodesNo. of electrodes 88FrequenciesFrequencies 30:2 kHz – 1.6 MHz30:2 kHz – 1.6 MHzTechnologyTechnology DigitalDigitalDateDate 20002000
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aEIT of the Lungs: OverviewaEIT of the Lungs: Overview
aEIT of the Lungs: OverviewaEIT of the Lungs: Overview
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• 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
aEIT of the Lungs: OverviewaEIT of the Lungs: Overview
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• Absolute lung resistivity flow chart Absolute lung resistivity flow chart
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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
Clinical trial of aEITClinical trial of aEIT• 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
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Abs
olut
e re
sist
ivity
Ω
.m
PEEP=12 cmH₂O
12 cmH₂O 10 cmH₂O 10 cmH₂O 10 cmH₂O
Abs
olut
e lu
ng a
ir vo
lum
es (l
itres
)
Day 1 Day 2 Day 3 Day 4
Clinical trial of aEITClinical trial of aEIT• 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
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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 modelling of MEEVANFIS modelling of MEEV• 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
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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
Deg
ree
of m
embe
rshi
p
11 12 13 14 15 16 17 18
0
0.2
0.4
0.6
0.8
1
RR
Deg
ree
of m
embe
rshi
p
ANFIS modelling of MEEVANFIS modelling of MEEV
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• 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
ANFIS modelling of MEEVANFIS modelling of MEEV
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• 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
(litr
es)
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 LineModel 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.
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Overview of SOPAVentOverview of SOPAVent
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.
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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
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Overview of SOPAVentOverview of SOPAVent
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.
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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
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Coupling aEIT and SOPAVent Coupling aEIT and SOPAVent • ResultsResults
PEEP=12 11 10 9 8PEEP=12 11 10 9 80 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
(litre
s)
MEEV
1211
109
8
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
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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
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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 guideventilator therapy.
THANK YOUTHANK YOU
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