1 Patient-Speciﬁc Heart Model Towards Atrial Fibrillation

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Patient-Speciﬁc Heart Model Towards Atrial

Fibrillation

Jiyue He, Arkady Pertsov, Sanjay Dixit, Katie Walsh, Eric Toolan, Rahul Mangharam

Abstract—Atrial ﬁbrillation is a heart rhythm disorder that

affects tens of millions people worldwide. The most effective

treatment is catheter ablation. This involves irreversible heat-

ing of abnormal cardiac tissue facilitated by electroanatomical

mapping. However, it is difﬁcult to consistently identify the

triggers and sources that may initiate or perpetuate atrial

ﬁbrillation due to its chaotic behavior. We developed a patient-

speciﬁc computational heart model that can accurately reproduce

the activation patterns to help in localizing these triggers and

sources. Our model has high spatial resolution, with whole-

atrium temporal synchronous activity, and has patient-speciﬁc

accurate electrophysiological activation patterns. A total of 15

patients data were processed: 8 in sinus rhythm, 6 in atrial

ﬂutter and 1 in atrial tachycardia. For resolution, the average

simulation geometry voxel is a cube of 2.47 mm length. For

synchrony, the model takes in about 1,500 local electrogram

recordings, optimally ﬁts parameters to the individual’s atrium

geometry and then generates whole-atrium activation patterns.

For accuracy, the average local activation time error is 5.47 ms

for sinus rhythm, 10.97 ms for ﬂutter and tachycardia; and the

average correlation is 0.95 for sinus rhythm, 0.81 for ﬂutter and

tachycardia. This promising result demonstrates our model is

an effective building block in capturing more complex rhythms

such as atrial ﬁbrillation to guide physicians for effective ablation

therapy.

Index Terms—medical cyber-physical systems, patient-speciﬁc,

computational heart model, cardiac electrophysiology modeling,

electroanatomical mapping, Mitchell-Schaeffer model

I. INTRODUCTION

Atrial Fibrillation (AF) is a heart rhythm disorder where

the normal beating in the two atria is irregular, and blood

does not ﬂow well to the ventricles. While the underlying

mechanism of AF is not clearly understood, it largely is due

to the spatial variation in the conduction properties of the atrial

myocardium. This causes the single wavefront propagating

across the atria to being split into multiple wavefronts resulting

in chaotic depolarization of the heart tissue and irregularly fast

rhythm. AF increases the risk of stroke and heart failure. If

left untreated, it will become worse [1].

One of the most effective treatments for AF is catheter

ablation. This involves irreversible radiofrequency heating of

the sources that initiate or perpetuate AF. The common current

ablation protocol involves standard lesion locations for all

patients. For example, in the Pulmonary Vein Isolation (PVI)

Jiyue He, Rahul Mangharam: Department of Electrical and Sys-

tems Engineering, University of Pennsylvania, Philadelphia, PA, USA.

jiyuehe@seas.upenn.edu. Arkady Pertsov: Department of Pharmacology, Up-

state Medical University, Syracuse, USA. Sanjay Dixit, Katie Walsh: Depart-

ment of Cardiac Electrophysiology, Hospital of the University of Pennsylva-

nia, Philadelphia, PA, USA. Eric Toolan: Biosense Webster, Lansdowne, PA,

USA. May 19, 2021.

approach, ablation lesions encircle the pulmonary veins to

prevent abnormal activations originated in the veins travel into

the atria.

Another common protocol involves PVI with additional ab-

lation of non-pulmonary vein triggers and putative arrhythmia

substrate [2]. While paroxysmal-AF (i.e. spontaneous onset

and termination) can be eliminated in 70-75% of patients with

a single ablation procedure, persistent-AF can be eliminated

in only about 50% of patients with a single procedure [3].

The reason is there are triggers other than pulmonary veins

that cause ﬁbrillation for the persistent-AF. Electroanatomical

mapping captures the heart geometry and tissue conductivity

which are essential in identifying those trigger [4].

The central challenge is to capture high-resolution synchro-

nized electrograms across the entire atrium and analyze these

data to identify potential triggers as ablation candidates to ter-

minate AF. We developed a high spatial resolution, temporally

synchronous and patient-speciﬁc atrium model which captures

electrophysiologic and anatomic parameters unique to each

patient.

Figure 1 shows the overall process of constructing the

heart model. First, a high resolution electroanatomical map

is exported from the Carto3 System, which contains atrium

3D triangular mesh, electrograms, and electrode locations.

The mesh is processed to remove geometry defects, such as

deep concave holes, intersecting triangular faces, and non-

referenced vertices. The mitral valve and 4 pulmonary veins

are cut out. Then a 3D Cartesian grid is generated and wrapped

around the mesh for computing simulation. Then, these are put

into the electrophysiological heart model, and patient-speciﬁc

parameters are ﬁtted via an optimization process. As a result

of ﬁtting parameters at every locations on the mesh, the heart

model becomes whole-atrium synchronous. Lastly, the heart

model is validated over 15 patients data by comparing Local

Activation Time (LAT) between patient data and simulation

data.

This model demonstrates the ability to accurately produce

the activation patterns for an individual patient that can better

identify AF sources.

II. RELATED WORK

Several research groups had developed patient speciﬁc high

resolution computational heart models [5]. Cabrera-Lozoya et

al developed a model constructed from 3D Delayed-Enhanced

Magnetic Resonance Imaging (DE-MRI) to simulate patient-

speciﬁc post-infarction Ventricular Tachycardia (VT) abnor-

mal electrograms. From DE-MRI data, they segmented the

arXiv:2210.12825v1 [physics.med-ph] 23 Oct 2022

Fig. 1. The overall process. Patient data consists of electrode locations, electrograms, and atrium 3D triangular mesh. Mesh is reﬁned and pulmonary veins

and mitral valve are cut out. Then, a Cartesian grid is created wrapping around the mesh. These data are input into the electrophysiological heart model,

where parameters are ﬁtted via an optimization process. Lastly, the heart model is validated through patient data.

ventricle into healthy myocardium, scar, and border zone.

Then, three different sets of parameters were assigned to

these three regions. They showed that their model can gen-

erate distinguishable electrograms for healthy region and scar

region [6]. Boyle et al developed a model for personalized

ablation guidance of AF. They obtained atrium geometry

and ﬁbrosis distribution from Late Gadolinium Enhancement

Magnetic Resonance Imaging (LGE-MRI). Healthy regions

and ﬁbrosis regions are assigned with different parameters.

Then virtual pacing are performed in simulation to identify

all possible ablation targets [7]. Lim et al developed a model

to do simulation-guided catheter ablation of AF. Their model

combined data from Computed Tomography (CT) images and

electroanatomical maps to capture anatomy, ﬁber orientation,

ﬁbrosis, and electrophysiology. Different set of conduction

parameters are assigned to ﬁbrotic and non-ﬁbrotic tissue [8].

Corrado et al developed a model that had locally ﬁtted param-

eters. They applied an S1-S2 electrical stimulation protocol

from the coronary sinus and the high right atrium and recorded

endocardium electrograms in the left atrium. The parameter

ﬁtting was done via a grid-search method that evaluated all

the combinations of parameters within a range [9] [10].

The major differences of our heart model are:

•Our model identiﬁes tissue conduction and diffusion

values locally at the voxel level, while other heart models

assign two or three sets of ﬁxed parameters to generic

healthy or scar regions.

•Our model ﬁts the local parameters using self-activated

electrograms rather then manual pacing-induced ones.

•Our model does not use MRI or CT because they do not

provide electrical activation data. Instead, we use elec-

troanatomical mapping data which provides endocardium

electrogram.

•Our model is computationally light and will eventually

be able to run during the clinical procedure for real-time

ablation guidance.

It takes about 1 to 2 hours for Carto3 System to export 1

patient data. Our heart model program is implemented using

Matlab (MathWorks), and runs on a laptop with 1 Intel Core i7

CPU. It takes about 2 minutes to read in a patient data to the

computer. On average, it takes about 50 seconds to personalize

one heart model. If our heart model was integrated into the

Carto3 System, the step of export/import data would not be

needed, which means the entire computation could be ﬁnished

in 50 seconds. Further more, it could speed up hundreds times

if GPU computing was implemented.

III. HIGH SPATIAL RESOLUTION

A. Data Collection

At the start of the catheter ablation procedure, the physician

captures an electroanatomical map with a roving mapping

catheter from the Carto3 System at the Hospital of the Uni-

versity of Pennsylvania. As show in Figure 2, it associates

the 3D atrium triangular mesh with electrical activity at every

location. The mesh mapping ﬁll threshold was 5 mm, and

electrogram recording ﬁlters were set at 2 to 240 Hz for

unipolar electrograms, 16-500 Hz for bipolar electrograms,

and 0.5-200 Hz for surface electrogram. Each map has about

1,500 electrogram recordings spread across the endocardium.

Each electrogram records 2.5 seconds unipolar and bipolar

signals at 1 kHz. Bad electrograms will introduce noise to the

model, thus needed to be excluded:

•Electrode distance to the nearest mesh vertex is greater

than 8 mm. This is an empirical threshold we decided to

Fig. 2. (a) Pentaray catheter is a star-shape catheter that has 20 electrodes. (b) Physicians will hold the catheter in a location for 2.5 seconds to obtain one

segment of recording. (c) Then move the catheter to another location. (d) After 10 minutes, about 1,500 electrograms at different locations will be recorded.

use. The electrode was most likely not in contact with

atrium tissue beyond this threshold.

•Maximum voltage is less than 0.45 mV. These electrodes

are either too far way from tissue or in contact with scar

tissue [11]. Neither will provide clean electrogram.

•Complex and fractionated electrogram as shown in Figure

3. Fractionated electrograms consist of multiple high

frequency components with low amplitudes and long

duration, makes it difﬁcult to ﬁnd the accurate activation

time.

Fig. 3. (a) Electrogram is too complex and fractionated, making it difﬁcult

to ﬁnd the activation time. (b) Good electrogram. The activation time is easy

to identify.

The amount of raw electrogram recordings captured and ﬁl-

tered are shown in Table I. The percentage of good electrogram

recordings is low for ﬂutter and tachycardia maps because they

contain more fractionated electrograms.

B. Triangular Mesh to Cartesian Grid

To achieve accurate activation wave propagation, we cut out

the mitral valve and pulmonary veins. The cutting decision is

based on atrium anatomy [12]. The mesh is then reﬁned to

make each of the triangles the same size and shape. Mesh

editing is done using a software called MeshLab [13], and the

two most helpful functionalities are 1) Simpliﬁcation: quadric

edge collapse decimation, can be used to smooth mesh imper-

fections; and 2) Remeshing: isotropic explicit remeshing, can

be used to make triangles uniform in size and shape. For our

heart model to simulate atrium action potential propagation, it

requires a calculation of the second derivative of space. This

is difﬁcult to perform on triangular mesh because the vertices

TABLE I

NUMBER OF ELECTRODE RECORDINGS

ID Rhythm # Electrode # Used % Used

1 Sinus Rhythm 976 557 57.1

2 Sinus Rhythm 3263 1361 41.7

3 Sinus Rhythm 3156 1788 56.7

4 Sinus Rhythm 1488 663 44.6

5 Sinus Rhythm 2477 1655 66.8

6 Sinus Rhythm 2905 2079 71.6

7 Sinus Rhythm 1744 861 49.4

8 Sinus Rhythm 1801 1106 61.4

Average 2226 1259 56.1

9 Flutter 278 73 26.3

10 Flutter 958 326 34.0

11 Tachycardia 822 197 24.0

12 Flutter 484 215 44.4

13 Flutter 1227 198 16.1

14 Flutter 714 130 18.2

15 Flutter 1469 425 28.9

Average 850 223 27.4

are not regularly positioned. In contrast, a Cartesian grid is

regular. In Figure 4, the black line represents a cross section

of the atrium and the blue dots are Cartesian grid voxels.

First, a Cartesian grid that encloses the entire atrium is created

as shown in (a). Then the voxels that are beyond a distance

threshold to the atrium mesh are removed as shown in (b).

The distance threshold is equal to 1.5 times the average inter

vertex distance. (c) (d) show the same process implemented

on a patient’s atrium mesh.

Patient electrograms are processed and the results are as-

signed to the nearest atrium mesh vertices. From atrium mesh

vertices, values will be projected onto the nearest Cartesian

grid voxels for simulation. Then the simulated results will

be projected back to the nearest vertices of the atrium mesh.

Denote dvoxel the distance in between two neighboring Carte-

sian grid voxels, dvertex the average distance in between two

neighboring atrium mesh vertices. It is important that this

equation to be satisﬁed: dvoxel <1/2×dvertex. Figure 5

explains why. The triangular mesh is the atrium, and the

larger dots are the Cartesian grid voxels. (a) If voxel spacing

is too large, when projecting values from vertices to voxels,

multiple vertices may be projected to the same voxel, causing

values to be overwritten and information lost. Here the two

red vertices are projected to the same green voxel. (b) If voxel

spacing is small enough, each red vertex will be projected to

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1Patient-SpecicHeartModelTowardsAtrialFibrillationJiyueHe,ArkadyPertsov,SanjayDixit,KatieWalsh,EricToolan,RahulMangharamAbstractAtrialbrillationisaheartrhythmdisorderthataffectstensofmillionspeopleworldwide.Themosteffectivetreatmentiscatheterablation.Thisinvolvesirreversibleheat-ingofabnormalcard...

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