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Identification of Time Varying Cardiac Disease Identification of Time Varying Cardiac Disease State Using a Minimal Cardiac Model with Reflex State Using a Minimal Cardiac Model with Reflex
Actions Actions
14th IFAC SYMPOSIUM ON SYSTEM IDENTIFICATION, SYSID-2006
C. E. Hann1, S. Andreassen2, B. W. Smith2, G. M. Shaw3, J. G. Chase1, P. L. Jensen4
1Department of Mechanical Engineering, University of Canterbury, Christchurch, New Zealand2Centre for Model-based Medical Decision Support, Aalborg University, Aalborg, Denmark
3 Department of Intensive Care Medicine, Christchurch Hospital, Christchurch, New Zealand4 Department of Cardiology, Aalborg Hospital, Denmark
• Cardiac disturbances difficult to diagnose
- Limited data
- Reflex actions
• Minimal Cardiac Model
- Interactions of simple models
- primary parameters
- common ICU measurements
• Increased resistance in pulmonary artery – pulmonary embolism, atherosclerotic heart disease
• Require fast parameter ID
Diagnosis and TreatmentDiagnosis and Treatment
Heart ModelHeart Model
D.E.’s and PV diagramD.E.’s and PV diagram
2
2232
2
1
1121
1
21
L
RQPPQ
L
RQPPQ
QQV
2
0
)375.0(80
)(02
)(
),1())(1()()(
t
VVdes
ete
ePteVVEteP
Reflex actionsReflex actions
• Vaso-constriction - contract veins
• Venous constriction – increase venous dead space
• Increased HR
• Increased ventricular contractility
Varying HR as a function of aoP
Disease StatesDisease States
• Pericardial Tamponade- build up of fluid in pericardium- dead space volume V0,pcd by 10 ml / 10 heart beats
• Pulmonary Embolism - Rpul 20% each time
• Cardiogenic shock- e.g. blocked coronary artery- not enough oxygen to myocardium- Ees,lvf, P0,lvf
• Septic shock- blood poisoning- reduce systemic resistance
• Hypovolemic shock – severe drop in total blood volume
Healthy Human BaselineHealthy Human Baseline
Output Value
Volume in left ventricle 111.7/45.7 ml
Volume in right ventricle 112.2/46.1 ml
Cardiac output 5.3 L/min
Max Plv 119.2 mmHg
Max Prv 26.2 mmHg
Pressure in aorta 116.6/79.1 mmHg
Pressure in pulmonary artery 25.7/7.8 mmHg
Avg pressure in pulmonary vein 2.0 mmHg
Avg pressure in vena cava 2.0 mmHg
• Healthy human
Model Simulation ResultsModel Simulation Results
• Pericardial tamponade Ppu – 7.9 mmHgCO – 4.1 L/minMAP – 88.0 mmHg
• Pulmonary Embolism
• All other disease states similarly capture physiological trends and magnitudes
Add Noise to IdentifyAdd Noise to Identify
• Add 10% uniform distributed noise to outputs to identify
• Apply integral-based optimization
Integral Method - ConceptIntegral Method - Concept
Discretised solution analogous to measured data
• Work backwards and find a,b,c
• Current method – solve D. E. numerically or analytically
8.0,2.0,5.0
1)0(,)sin(
cba
xctbaxx• (simple example with analytical solution )
R
PPq 21
12 13 14 15 16 17 18 191.4
1.45
1.5
1.55
1.6
1.65
1.7
1.75
1.8
1.85
time
x
))sincos(
)(()1(
1)(
22
322
ccatbatab
acaabcaeaa
tx at
- Find best least squares fit of x(t) to the data
- Non-linear, non-convex optimization, computationally intense
• integral method
– reformulate in terms of integrals
– linear, convex optimization, minimal computation
Integral Method - ConceptIntegral Method - Concept
0t• Integrate both sides from to t ( ) ,)sin( ctbaxx 4
0t
t
t
t
t
t
t
t
t
t
t
t
t
ttcttbdtxatxtx
dtcdttbdtxatxtx
dtctbaxdtx
0
0 0 0
00
)())cos()(cos()()(
1)sin()()(
))sin((
000
0
• Choose 10 values of t, between and form 10 equations in 3 unknowns a,b,c
40t 6
10,,1),()()())cos(1( 000
itxtxttctbdtxa iitt i
Integral Method - ConceptIntegral Method - Concept
)()(
)()(
)cos()cos(
)cos()cos(
010
01
010100
0110
10
0
1
0
txtx
txtx
c
b
a
ttttdtx
ttttdtx
tt
tt
• Linear least squares (unique solution)
Method Starting point CPU time (seconds) Solution
Integral - 0.003 [-0.5002, -0.2000, 0.8003]
Non-linear [-1, 1, 1] 4.6 [-0.52, -0.20, 0.83]
Non-linear [1, 1, 1] 20.8 [0.75, 0.32, -0.91]
• Integral method is at least 1000-10,000 times faster depending on starting point
• Thus very suitable for clinical application
Identifying Disease State using Identifying Disease State using All Variables - SimulatedAll Variables - Simulated
Change True value
(ml)
Optimized Value
Error (%)
First 180 176 2.22
Second 160 158 1.25
Third 140 138 1.43
Fourth 120 117 2.50
Fifth 100 100 0
• Capture disease states, assume Ppa, Pao, Vlv_max, Vlv_min, chamber flows.• Pericardial tamponade (determining V0,pcd)
• Pulmonary Embolism (determining Rpul)
Change True value
(mmHg s ml-1)
Optimized Value Error (%)
First 0.1862 0.1907 2.41
Second 0.2173 0.2050 5.67
Third 0.2483 0.2694 8.50
Fourth 0.2794 0.2721 2.60
Fifth 0.3104 0.2962 4.59
• Cardiogenic shock (determining [Ees,lvf, P0,lvf] (mmHg ml-1, mmHg)
Change True values Optimized Value
Error (%)
First [2.59,0.16] [2.61,0.15] [0.89,5.49]
Second [2.30,0.19] [2.30,0.18] [0.34,4.39]
Third [2.02,0.23] [2.02,0.21] [0.43,8.03]
Fourth [1.73,0.26] [1.70,0.24] [1.48,9.85]
Fifth [1.44,0.30] [1.43,0.27] [0.47,9.39]
Change True value
(mmHg s ml-1)
Optimized Value
Error (%)
First 1.0236 1.0278 0.41
Second 0.9582 0.9714 1.37
Third 0.8929 0.8596 3.73
Fourth 0.8276 0.8316 0.49
Fifth 0.7622 0.7993 4.86
• Septic Shock (determining Rsys)
Identifying Disease State using Identifying Disease State using All Variables - SimulatedAll Variables - Simulated
• Hypovolemic Shock (determining stressed blood volume)
Change True value
(ml)
Optimized Value
Error (%)
First 1299.9 1206.5 7.18
Second 1177.3 1103.7 6.26
Third 1063.1 953.8 10.28 (hmm!)
Fourth 967.8 1018.9 5.28
Fifth 928.5 853.4 8.10
Identifying Disease State using Identifying Disease State using All Variables - SimulatedAll Variables - Simulated
Preliminary Animal Model Preliminary Animal Model ResultsResults
• Pulmonary embolism induced in pig (collaborators lab in Belgium)
• Identifying changes in pulmonary resistance, Rpul
Left Ventricle
• 8 heartbeats• Essential dynamics captured• Remaining issues with sensor locations etc• Aortic stenosis as well?
ConclusionsConclusions
• Minimal cardiac model simulate time varying disease states
• Accurately captured physiological trends and magnitudes
capture wide range of dynamics
• Integral based parameter ID method
- errors from 0-10%, with 10% noise
- identifiable using common measurements
• Rapid feedback to medical staff
AcknowledgementsAcknowledgements
Questions ???
Engineers and Docs
Dr Geoff Chase Dr Geoff Shaw
The Danes
Steen Andreassen Dr Bram Smith
The honorary Danes