Optimal PEEP selection in Mechanical Ventilation using EIT · Optimal PEEP selection in Mechanical...

Transcript of Optimal PEEP selection in Mechanical Ventilation using EIT · Optimal PEEP selection in Mechanical...

Optimal PEEP selection in Mechanical Ventilation usingEIT

Ravi B. Bhanabhai

Carleton University - 2009/11 M.A.Sc

January 20, 2012

Ravi B. Bhanabhai (Carleton U) Safe Keeping Ventilation Patients 01/20/2012 1 / 28
Outline

1 IntroductionThe ProblemHow to solve the problem?

2 ContributionsIP CalculationFuzzy Logic System

3 ResultsSigmoid vs. LinearLinear vs. VisualOptimal PEEP

4 References

Introduction

Introduction

This is a presentation outlining the work done within RaviBhanabhai M.A.Sc thesis.

Purpose: Investigate the use of Electrical Impedance Tomography(EIT) within mechanical ventilation.

Mathematical Tools:1 Linear and Non-Linear curve fitting techniques2 Fuzzy Logic.

Contribtions:1 Summarize scholarly papers on ALI.2 Inflection Point (IP) location on EIT and pressure data.3 Creation of Fuzzy Logic System using IP.

Novel Aspects:1 Use of short recruitment maneuever ( 2min)2 Regional Inflection Points used3 Use of Inflection Points within an automated classification system

Introduction

Introduction






Introduction

Introduction






Introduction

Introduction






Introduction The Problem

ALI & VILI

Respiratory Failure

OxygenationFailure

(hypoxemia)

VentilatoryFailure

(hypercapnia)

+ more oxygenrelated conditions

Acute Lung Injury(ALI)

Ventilator InduceLung Injury

(VILI)* Cyclic opening and closing* overdistensionPEEP

Introduction How to solve the problem?

Respiratory Function Models

Pao =V

C+ V R + V I Pmus (1)

Interested in VC only.

To remove other components this thesis data did two things:1 Slow Constant Flow2 Antheysia



Pao =V






Pao =V





Pressure-Volume Curves

Used to help guide ventilation strategies by locating points ofcompliance change.

Points are Lower Inflection Point (LIP) and Upper Inflection Point(UIP)

0 5 10 15 20 25 30 352

0

2

4

6

Pressure (mbar)

EIT

Con

duct

ivity

Linear Fit Inflation

OriginalSigmoid FittedInflection Points

0 5 10 15 20 25 30 352

0

2

4

6

Pressure (mbar)

EIT

Con

duct

ivity

Linear Fit Deflation


(a) Linear Fit of PI data

20 10 0 10 20 300

0.2

0.4

0.6

0.8

1Time Alignment

global EITPressure

(b) Pressure andEIT data overlap



Used to help guide ventilation strategies by locating points ofcompliance change.Points are Lower Inflection Point (LIP) and Upper Inflection Point(UIP)

0 5 10 15 20 25 30 352

0

2

4

6

Pressure (mbar)

EIT

Con

duct

ivity



0 5 10 15 20 25 30 352

0

2

4

6

Pressure (mbar)

EIT

Con

duct

ivity



(c) Linear Fit of PI data

20 10 0 10 20 300

0.2

0.4

0.6

0.8

1Time Alignment

global EITPressure

(d) Pressure andEIT data overlap



Used to help guide ventilation strategies by locating points ofcompliance change.Points are Lower Inflection Point (LIP) and Upper Inflection Point(UIP)

0 5 10 15 20 25 30 352

0

2

4

6

Pressure (mbar)

EIT

Con

duct

ivity



0 5 10 15 20 25 30 352

0

2

4

6

Pressure (mbar)

EIT

Con

duct

ivity



(e) Linear Fit of PI data

20 10 0 10 20 300

0.2

0.4

0.6

0.8

1Time Alignment

global EITPressure

(f) Pressure andEIT data overlap


Data used

Data used:

26 patients

low constant flow maneuver (4 L/min)

start 0 mbar 35 mbar / 2L

20 40 60 80 100 120 140 160 180 2000

5

10

15

20P

ress

ure

[mba

r]

Pressure Maneuver

20 40 60 80 100 120 140 160 180 2000

500

1000

1500

Vol

ume

[ml]

Volume Maneuver

20 40 60 80 100 120 140 160 180 200

20

0

20

40

Flo

w [L

/min

]

Flow Maneuver

Time [sec]


Electrical Impedance Tomography (EIT)

EIT is real-time impedance tomography, it can be used to accuratlymeasure air distrubtion within the thorax.

(g) Start of Inflation (h) Max Pressure (i) End of Deflation

Figure: Example reconstruction using the GREIT methods of a healthy lungpatient (patient 7).


Electrical Impedance Tomography (EIT)

EIT is real-time impedance tomography, it can be used to accuratlymeasure air distrubtion within the thorax.

(a) Start of Inflation (b) Max Pressure (c) End of Deflation

Figure: Example reconstruction using the GREIT methods of a healthy lungpatient (patient 7).

Contributions

Contributions

Automated IP calculation

Rule-base Fuzzy Logic Classifier

Contributions

Contributions

Automated IP calculation

Rule-base Fuzzy Logic Classifier

Contributions IP Calculation

IP Calculation

Three Types of IP location methods were used:

1 Sigmoid method

2 Visual heuristics

3 3-piece linear spline method

Multiple methods were implemented for comparison reasons.


IP Calculation


1 Sigmoid method

2 Visual heuristics




IP Calculation


1 Sigmoid method

2 Visual heuristics




IP Calculation


1 Sigmoid method

2 Visual heuristics




IP Calculation


1 Sigmoid method

2 Visual heuristics




Sigmoid Method

0 5 10 15 20 25 30 350

200

400

600

800

1000

1200

pressure mbar

volu

me

m

l

Sigmoid Method

a= 12ml

b= 1173 ml

Plip =c - 2d

=11.1Plip =c +2d

=22.9

c= 17 cm H20


Visual Heuristics

Clinicians used this method to locate Inflection Points from global PVcurves.

Multiple methods exist:

1 Find location where PV curve has linear compliance2 Pressure where rapid increase in compliance occurs3 Place two line. 1) Along low compliance. 2) Along High compliance.

Locate Intersection.

This thesis:1 5 participants2 Fit in linear manner to get closest to all the data points


Visual Heuristics


Multiple methods exist:1 Find location where PV curve has linear compliance

2 Pressure where rapid increase in compliance occurs3 Place two line. 1) Along low compliance. 2) Along High compliance.




Visual Heuristics


Multiple methods exist:1 Find location where PV curve has linear compliance2 Pressure where rapid increase in compliance occurs

3 Place two line. 1) Along low compliance. 2) Along High compliance.Locate Intersection.



Visual Heuristics


Multiple methods exist:1 Find location where PV curve has linear compliance2 Pressure where rapid increase in compliance occurs3 Place two line. 1) Along low compliance. 2) Along High compliance.




Visual Heuristics


Multiple methods exist:1 Find location where PV curve has linear compliance2 Pressure where rapid increase in compliance occurs3 Place two line. 1) Along low compliance. 2) Along High compliance.




Visual Heuristic

0 5 10 15 20 25 30 350.4

0.2

0

0.2

0.4

0.6

0.8

1

Pressure (mbar)

EIT

Con

duct

ivity

Trial3/ 4 Deflation

One chance only, be carefull

0 5 10 15 20 25 30 350.1

0

0.1

0.2

0.3

0.4

0.5

0.6

Pressure (mbar)

EIT

Con

duct

ivity

Trial1/ 4 Deflation

One chance only, be carefull


3-piece Linear Spline Method

Similar to visual methods

Fits to 3 lines with Inflection Points being located at intersection

This Thesis: No Constraints on fitting other then minimization ofcriteria

0 5 10 15 20 25 30 352

0

2

4

6

Pressure (mbar)

EIT

Con

duct

ivity



0 5 10 15 20 25 30 352

0

2

4

6

Pressure (mbar)

EIT

Con

duct

ivity








0 5 10 15 20 25 30 352

0

2

4

6

Pressure (mbar)

EIT

Con

duct

ivity



0 5 10 15 20 25 30 352

0

2

4

6

Pressure (mbar)

EIT

Con

duct

ivity








0 5 10 15 20 25 30 352

0

2

4

6

Pressure (mbar)

EIT

Con

duct

ivity



0 5 10 15 20 25 30 352

0

2

4

6

Pressure (mbar)

EIT

Con

duct

ivity








0 5 10 15 20 25 30 352

0

2

4

6

Pressure (mbar)

EIT

Con

duct

ivity



0 5 10 15 20 25 30 352

0

2

4

6

Pressure (mbar)

EIT

Con

duct

ivity



Contributions Fuzzy Logic System

Introduction

The Fuzzy System is designed into 4 sections:

1 Location of IP

2 Fuzzification

3 Premise Calculation (Application of IF-THEN)

4 Defuzzification and Optimization


Introduction


1 Location of IP

2 Fuzzification




Introduction


1 Location of IP

2 Fuzzification




Introduction


1 Location of IP

2 Fuzzification




Introduction

0 5 10 15 20 25 30 35 401

0

1

2

3

Pressure (mbar)

EITConductivity



0 5 10 15 20 25 30 35 401

0

1

2

3

Pressure (mbar)

EITConductivity



0 5 10 15 20 25 30 35 400.5

0

0.5

1

1.5

2

Pressure (mbar)

EITConductivity



0 5 10 15 20 25 30 35 401

0

1

2

Pressure (mbar)

EITConductivity



5 10 15 20 25 30 350

0.2

0.4

0.6

0.8

1

Pressure

Mem

bershipValue

Pressure based Membership Graph Inflation

BelowIn BetweenAbove

5 10 15 20 25 30 350

0.2

0.4

0.6

0.8

1

Pressure

Mem

bershipValue

Pressure based Membership Graph Deflation


5 10 15 20 25 30 350

0.2

0.4

0.6

0.8

1

Pressure

Mem

bershipValue

Pressure based Membership Graph Inflation


5 10 15 20 25 30 350

0.2

0.4

0.6

0.8

1

Pressure

Mem

bershipValue

Pressure based Membership Graph Deflation


0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6

0

0.2

0.4

0.6

0.8

1

EIT

Mem

bershipValue

LIP UIP

EIT based Membership Graph Inflation


0.5 0 0.5 1 1.5

0

0.2

0.4

0.6

0.8

1

EIT

Mem

bershipValue

LIP UIP

EIT based Membership Graph Deflation


0 0.5 1 1.5 2

0

0.2

0.4

0.6

0.8

1

EIT

Mem

bershipValue

LIP UIP

EIT based Membership Graph Inflation


0 0.5 1 1.5 2

0

0.2

0.4

0.6

0.8

1

EIT

Mem

bershipValue

LIP UIP

EIT based Membership Graph Deflation


5 10 15 20 25 30 35

0

0.2

0.4

0.6

0.8

1

Pressure

Magnitude

Pressure based Optimal Pressure Graph Inflation

Good StatesBad States

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6

0

0.2

0.4

0.6

0.8

1

EIT

Magnitude

EIT based Optimal Pressure Graph Inflation


5 10 15 20 25 30 35

0

0.2

0.4

0.6

0.8

1

Pressure

Magnitude

Pressure based Optimal Pressure Graph Deflation


0.5 0 0.5 1 1.5

0

0.2

0.4

0.6

0.8

1

EIT

Magnitude

EIT based Optimal Pressure Graph Deflation


5 10 15 20 25 30 35

0

0.2

0.4

0.6

0.8

1

Pressure

Magnitude

Pressure based Optimal Pressure Graph Inflation


0 0.5 1 1.5 2

0

0.2

0.4

0.6

0.8

1

EIT

Magnitude

EIT based Optimal Pressure Graph Inflation


5 10 15 20 25 30 35

0

0.2

0.4

0.6

0.8

1

Pressure

Magnitude

Pressure based Optimal Pressure Graph Deflation


0 0.5 1 1.5 2

0

0.2

0.4

0.6

0.8

1

EIT

Magnitude

EIT based Optimal Pressure Graph Deflation


Average over all pixelsCalculate Optimal PEEP

IF P ressure/E I T THEN StateBelow CollpasedI n Between NormalAbove Overdistended

Inflection Points Fuzzification Premise Calculation OptimizationRule Base

0 50 100 150 200 250 300 350 400 4500

50

100

150

200

250

300Good vs Bad states Pressure based system

Pressure Index

Mantiu

de

Optimal PEEP Optimal PEEP

0 50 100 150 200 250 300 350 4000

50

100

150

200

250

300Good vs Bad states Pressure based system

Pressure Index

Mantiu

de

Optimal PEEP

0 50 100 150 200 250 300 350 400 4500

50

100

150

200

250

300Good vs Bad states EIT based system

Pressure Index

Mantiu

de

Optimal PEEP

0 50 100 150 200 250 300 350 4000

50

100

150

200

250

300

350Good vs Bad states EIT based system

Pressure Index

Mantiu

de

Optimal PEEP

Pressure - Inflation

Pressure - Deflation

EIT - Inflation

EIT - Deflation

Inference and Defuzzification


IP and Fuzzification

IP were taken from the 3-piece linear optimization portion and used in thecreation of the fuzzification graphs.

Membership Input 1 (mbar) Input 2 (mbar) Input 3 (mbar) Input 4 (mbar)

Below min(p) min(p) -2+LIP LIPIn Between -2+LIP LIP UIP 2+UIPAbove UIP 2+UIP max(p) max(p)

Table: Details on creating the trapezoidal based fuzzy membership functions

0

0.25

0.5

0.75

1

a b c d

Figure: Trapezoidal Fuzzy Membership graph







0

0.25

0.5

0.75

1

a b c d








0

0.25

0.5

0.75

1

a b c d




The Inference is conducted using the Rule base. With key relations toprevious papers:

1 Pressure below the LIP is considered collapsed

2 Pressure above the UIP is considered overdistended

Defuzzification was done by breaking the output states into twoclassifications:

1 Good = Normal states

2 Bad = Collpased + Overdistended states

Upon averaging over lung region MAX value between the difference ofGood and Bad states is performed to locate the PEEP. Fuzzy Logic Schematic

Results Sigmoid vs. Linear

Sigmoid vs. Linear

0

5

10

15

20x 103

LIPInf UIPInf LIPDef UIPDef

Linear Method

% n

ot fo

und

0

0.2

0.4

0.6

0.8

1


Sigmoid Method

% n

ot fo

und

Figure: How frequent each sigmoid and linear method are not able to find IP.

Mean Std Median

LIP - Inflation 1.47 3.02 1.50UIP - Inflation -6.80 2.54 -6.82LIP - Deflation 4.07 1.84 4.07UIP - Deflation -2.37 2.24 -2.78

Table: Difference between Sigmoid and Linear Method

Results Sigmoid vs. Linear

Sigmoid vs. Linear

0

5

10

15

20x 103


Linear Method

% n

ot fo

und

0

0.2

0.4

0.6

0.8

1


Sigmoid Method

% n

ot fo

und

Figure: How frequent each sigmoid and linear method are not able to find IP.

Mean Std Median

LIP - Inflation 1.47 3.02 1.50UIP - Inflation -6.80 2.54 -6.82LIP - Deflation 4.07 1.84 4.07UIP - Deflation -2.37 2.24 -2.78

Table: Difference between Sigmoid and Linear Method

Results Linear vs. Visual

Linear vs. Visual Heuristics

Difference = Linear IP Visual Heuristic IP

0.6247mbar for LIP 0.4662mbar for UIPbest average = 0.016mbar worst average = 1.507mbarProvides insight to accuracy of linear method



Difference = Linear IP Visual Heuristic IP0.6247mbar for LIP 0.4662mbar for UIP

best average = 0.016mbar worst average = 1.507mbarProvides insight to accuracy of linear method



Difference = Linear IP Visual Heuristic IP0.6247mbar for LIP 0.4662mbar for UIPbest average = 0.016mbar worst average = 1.507mbar

Provides insight to accuracy of linear method



Difference = Linear IP Visual Heuristic IP0.6247mbar for LIP 0.4662mbar for UIPbest average = 0.016mbar worst average = 1.507mbarProvides insight to accuracy of linear method

Results Optimal PEEP

Hetergenaity

Figure: Progressive change of lung state with pressure

Results Optimal PEEP

LIP+2 vs FLS

0 5 10 15 20 25 30 350.5

0

0.5

1

1.5

2

Pressure [mbar]

EIT

Linear Fit graph with LIP+2 mbar PEEP

LIP+2cm = 9.4837 mbar

0 5 10 15 20 25 30 35

1

0.5

0

0.5

1

Pressure [mbar]

Mem

bers

hip

Optimal Selection graph with Optimal PEEP

FLS = 17.2 mbar

PILinear FitLower IPUpper IP

BadGoodDifference

(a) Patient 12

10 0 10 20 30 400.5

0

0.5

1

1.5

Pressure [mbar]

EIT


LIP+2cm = 2.1 mbar

0 10 20 30 40

1

0.5

0

0.5

1

Pressure [mbar]

Mem

bers

hip


FLS = 16.8 mbar


BadGoodDifference

(b) Patient 16

0 5 10 15 201

0

1

2

3

4

5

Pressure [mbar]

EIT




0 5 10 15 20

1

0.5

0

0.5

1

Pressure [mbar]

Mem

bers

hip


FLS = 8.6 mbar

BadGoodDifference

(c) Patient 8

0 5 10 15 20 251

0

1

2

3

4

Pressure [mbar]

EIT



0 5 10 15 20 25

1

0.5

0

0.5

1

Pressure [mbar]

Mem

bers

hip


FLS = 10.3 mbar


BadGoodDifference

(d) Patient 17

Figure: Global PI curve with LIP, UIP, and the LIP+2 mbar pressure and theFuzzy optimal selection with according FLS based pressure.

References I

Adler, A., Amato, M., Arnold, J., Bayford, R., Bodenstein, M., Bohm,S., Brown, B., Frerichs, I., Stenqvist, O., Weiler, N., and Wolf, G.(2012).Whither lung EIT: where are we, where do we want to go, and whatdo we need to get there?Submitted for publication to Journal of Applied Physiology.

EIDORS (2011).EIDORS: Electrical impedance tomography and diffuse opticaltomography reconstruction software.http://eidors3d.sourceforge.net/.

Graham, B. M. (2007).Enhancements in Electrical Impedance Tomography (EIT) ImageReconstruction for 3D Lung Imaging.PhD thesis, Carleton University.

http://eidors3d.sourceforge.net/
References

References II

Grychtol, B., Wolf, G. K., Adler, A., and Arnold, J. H. (2010).Towards lung EIT image segmentation: automatic classification oflung tissue state from analysis of EIT monitored recruitmentmanoeuvres.Physiological Measurement, 31(8):S31.

Grychtol, B., Wolf, G. K., and Arnold, J. H. (2009).Differences in regional pulmonary pressure impedance curves beforeand after lung injury assessed with a novel algorithm.Physiological Measurement, 30(6):S137.

Holder, D. (2004).Electrical Impedance Tomography: Methods, History, Applications.Institute of Physics Publishing.

References

References III

Luepschen, H., Meier, T., Grossherr, M., Leibecke, T., Karsten, J.,and Leonhardt, S. (2007).Protective ventilation using electrical impedance tomography.Physiological Measurement, 28(7):S247.

Mendel, J. M. (2001).Uncertain rule-based fuzzy logic system: introduction and newdirections.Prentice-Hall PTR, 1st edition.

Pulletz, S., Adler, A., Kott, M., Elke, B., Hawelczyk, B., Schadler, D.,Zick, G., Weiler, N., and Frerichs, I. (2011).Regional lung opening and closing pressures in patients with acutelung injury.Journal of Critical Care, 0000(0000):0000.

References

References IV

Victorino, J. A., Borges, J. B., Okamoto, V. N., Matos, G. F. J.,Tucci, M. R., Caramez, M. P. R., Tanaka, H., Sipmann, F. S., Santos,D. C. B., Barbas, C. S. V., Carvalho, C. R. R., and Amato, M. B. P.(2004).Imbalances in regional lung ventilation: A validation study onelectrical impedance tomography.Am. J. Respir. Crit. Care Med., 169(7):791800.

Wolf, G. K., Grychtol, B., Frerichs, I., Zurakowski, D., and Arnold,J. H. (2010).Regional lung volume changes during high-frequency oscillatoryventilation.11(5):610615.

References

References V

Wu, D. and Mendel, J. M. (2011).Linguistic summarization using IFTHEN rules and interval type-2fuzzy sets.Fuzzy Systems, IEEE Transactions on, 19(1):136151.

References

ending

Fuzzy Logic Schematic


IntroductionThe ProblemHow to solve the problem?

ContributionsIP CalculationFuzzy Logic System

ResultsSigmoid vs. LinearLinear vs. VisualOptimal PEEP

References

Optimal PEEP selection in Mechanical Ventilation using EIT · Optimal PEEP selection in Mechanical...

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Transcript of Optimal PEEP selection in Mechanical Ventilation using EIT · Optimal PEEP selection in Mechanical...