robert s. macleod, yesim serinagaoglu*, bulent yilmaz, and dana h. brooks*
DESCRIPTION
Epicardial Mapping from Venous Catheter Measurements, Body Surface Potential Maps, and an Electrocardiographic Inverse Solution. Robert S. MacLeod, Yesim Serinagaoglu*, Bulent Yilmaz, and Dana H. Brooks*. NEH Cardiovascular Research and Training Institute (CVRTI), U of Utah - PowerPoint PPT PresentationTRANSCRIPT
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Epicardial Mapping from Venous Catheter Measurements,
Body Surface Potential Maps, and an Electrocardiographic Inverse Solution
Epicardial Mapping from Venous Catheter Measurements,
Body Surface Potential Maps, and an Electrocardiographic Inverse Solution
Robert S. MacLeod, Yesim Serinagaoglu*, Bulent Yilmaz,
and Dana H. Brooks*
NEH Cardiovascular Research and Training Institute (CVRTI), U of Utah*Communications and Digital Signal Processing (CDSP), Northeastern U
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Combining Information SourcesCombining Information Sources
Forward
Inverse
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Venous Catheter Based Mapping
Venous Catheter Based Mapping
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Statistical EstimationStatistical Estimation
EstimationMatrix
Training Data
CovarianceMatrix
* =
Sparse Test DataEstimate
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Estimated Activation MapsEstimated Activation MapsOriginal Mixed
RV-onlyLV-only
• Training set composition
• Lead selection
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Augmented Inverse ProblemAugmented Inverse Problem
Forward
Inverse
Torso geometry
Body-Surface Potentials
Sparse Epicardial Potentials
Inverse Solution
Epicardial Map
+
+
+
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Subtraction ApproachSubtraction ApproachKnownUnknown
Inverse(Tikhonov)
1) Subtract known epicardial potentials
2) Solve reduced inverse problem
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Epicardial EstimationEpicardial Estimation
t1 t2 ….. ….tN
. . .
Estimation
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Combined EstimationCombined Estimation
Estimation
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Bayesian ApproachBayesian Approach
Inverse(MAP)
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Hybrid ApproachHybrid Approach
Inverse(MAP)Estimate
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Tank/Heart GeometryTank/Heart Geometry
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Test Lead SetsTest Lead Sets
Anterior
Posterior
42 leads 21 leads 10 leads
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Simulation StudySimulation Study
• 490 lead measured sock data
• Surrogate catheter potentials– 42 sites– + Gaussian noise
• Torso potentials– Calculated noise-free using forward model– + Gaussian noise
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Leave-One-Experiment Out Protocol
Leave-One-Experiment Out Protocol
Training Data
Dec 13, 2000
Test Data
Dec 18, 2000
Dec 13, 2000
LV Paced Beats
Mixed Paced Beats
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LV Pacing (LV-23 ms) LV Pacing (LV-23 ms) Orig Subt MAP
Est Epi Est All Hybrid MAP
31: LV-MEpiP-23 ms
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LV Pacing (LV-38 ms)LV Pacing (LV-38 ms)Orig Subt MAP
Est Epi Est All Hybrid MAP
31: LV-MEpiP-38 ms
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LV Pacing (LV-47 ms)LV Pacing (LV-47 ms)Orig Subt MAP
Est Epi Est All Hybrid MAP
31: LV-MEpiP-47 ms
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LV Pacing (Mixed-23 ms)LV Pacing (Mixed-23 ms)Orig Subt MAP
Est Epi Est All Hybrid MAP
31: LV-RV-MEpiP-23 ms
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LV Pacing (Mixed-38 ms)LV Pacing (Mixed-38 ms)Orig Subt MAP
Est Epi Est All Hybrid MAP
31: LV-RV-MEpiP-38 ms
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LV Pacing (Mixed-47 ms)LV Pacing (Mixed-47 ms)Orig Subt MAP
Est Epi Est All Hybrid MAP
31: LV-RV-MEpiP-47 ms
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Relative Error (31-LV)Relative Error (31-LV)
0 10 20 30 40 50 60 70 80 90 1000
0.2
0.4
0.6
0.8
1
1.2
1.4R
elat
ive
Err
or
Time [ms]
MAPEstim-Epi
Estim-AllMAP-HybridSubtr
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Relative Error (31-Mixed)Relative Error (31-Mixed)
0 10 20 30 40 50 60 70 80 90 1000
0.2
0.4
0.6
0.8
1
1.2
1.4MAPEstim-EpiEstim-AllMAP-HybridSubtr
Rel
ativ
e E
rro
r
Time [ms]
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Estimation FindingsEstimation Findings
• Estimation alone: noisy, unstable results
• Estimation + inverse: smoothing improves stability
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Inverse Solution FindingsInverse Solution Findings
• All solutions better than simple Tikhonov
• MAP usually improved with addition of catheter measurements (Hybrid MAP)
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Role of Statistics (Training)Role of Statistics (Training)
• Generally helps
• But can add artifacts, e.g., spurious breakthroughs or wavefronts
• Torso potentials can reduce artifacts
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ConclusionConclusion
More (bigger) is better…..
… but not always