Noninvasive Personalization of a Cardiac Electrophysiology Model from Body Surface Potential Mapping

Sophie Giffard-Roisin; Thomas Jackson; Lauren Fovargue; Jack Lee; Herve Delingette; Reza Razavi; Nicholas Ayache; Maxime Sermesant

doi:10.1109/TBME.2016.2629849

Noninvasive Personalization of a Cardiac Electrophysiology Model from Body Surface Potential Mapping

Sophie Giffard-Roisin, Thomas Jackson, Lauren Fovargue, Jack Lee, Herve Delingette, Reza Razavi, Nicholas Ayache, Maxime Sermesant

Inria French Institute for Research in Computer Science and Automation

Research output: Contribution to journal › Article › peer-review

54 Citations (Scopus)

108 Downloads (Pure)

Abstract

Goal: We use noninvasive data (body surface potential mapping, BSPM) to personalize the main parameters of a cardiac electrophysiological (EP) model for predicting the response to different pacing conditions. Methods: First, an efficient forward model is proposed, coupling the Mitchell-Schaeffer transmembrane potential model with a current dipole formulation. Then, we estimate the main parameters of the cardiac model: Activation onset location and tissue conductivity. A large patient-specific database of simulated BSPM is generated, from which specific features are extracted to train a machine learning algorithm. The activation onset location is computed from a Kernel Ridge Regression and a second regression calibrates the global ventricular conductivity. Results: The evaluation of the results is done both on a benchmark dataset of a patient with premature ventricular contraction (PVC) and on five nonischaemic implanted cardiac resynchonization therapy (CRT) patients with a total of 21 different pacing conditions. Good personalization results were found in terms of the activation onset location for the PVC (mean distance error, MDE = 20.3 mm), for the pacing sites (MDE = 21.7 mm) and for the CRT patients (MDE = 24.6 mm). We tested the predictive power of the personalized model for biventricular pacing and showed that we could predict the new electrical activity patterns with a good accuracy in terms of BSPM signals. Conclusion: We have personalized the cardiac EP model and predicted new patient-specific pacing conditions. Significance: This is an encouraging first step towards a noninvasive preoperative prediction of the response to different pacing conditions to assist clinicians for CRT patient selection and therapy planning.

Original language	English
Article number	7745951
Pages (from-to)	2206-2218
Number of pages	13
Journal	IEEE Transactions on Biomedical Engineering
Volume	64
Issue number	9
Early online date	29 Nov 2016
DOIs	https://doi.org/10.1109/TBME.2016.2629849
Publication status	Published - 1 Sept 2017

Keywords

Cardiac electrophysiology
ECG imaging
inverse problem of ECG
parameter estimation
personalization

Access to Document

10.1109/TBME.2016.2629849

Non-Invasive Personalisation of_GIFFARD-ROISIN_Accepted14November2016Publishedonline29November2016_GREEN AAMAccepted author manuscript, 10.5 MB

https://hal.inria.fr/hal-01397393

Cite this

@article{248f5ede822a49e0aa41be20ba392efc,

title = "Noninvasive Personalization of a Cardiac Electrophysiology Model from Body Surface Potential Mapping",

abstract = "Goal: We use noninvasive data (body surface potential mapping, BSPM) to personalize the main parameters of a cardiac electrophysiological (EP) model for predicting the response to different pacing conditions. Methods: First, an efficient forward model is proposed, coupling the Mitchell-Schaeffer transmembrane potential model with a current dipole formulation. Then, we estimate the main parameters of the cardiac model: Activation onset location and tissue conductivity. A large patient-specific database of simulated BSPM is generated, from which specific features are extracted to train a machine learning algorithm. The activation onset location is computed from a Kernel Ridge Regression and a second regression calibrates the global ventricular conductivity. Results: The evaluation of the results is done both on a benchmark dataset of a patient with premature ventricular contraction (PVC) and on five nonischaemic implanted cardiac resynchonization therapy (CRT) patients with a total of 21 different pacing conditions. Good personalization results were found in terms of the activation onset location for the PVC (mean distance error, MDE = 20.3 mm), for the pacing sites (MDE = 21.7 mm) and for the CRT patients (MDE = 24.6 mm). We tested the predictive power of the personalized model for biventricular pacing and showed that we could predict the new electrical activity patterns with a good accuracy in terms of BSPM signals. Conclusion: We have personalized the cardiac EP model and predicted new patient-specific pacing conditions. Significance: This is an encouraging first step towards a noninvasive preoperative prediction of the response to different pacing conditions to assist clinicians for CRT patient selection and therapy planning.",

keywords = "Cardiac electrophysiology, ECG imaging, inverse problem of ECG, parameter estimation, personalization",

author = "Sophie Giffard-Roisin and Thomas Jackson and Lauren Fovargue and Jack Lee and Herve Delingette and Reza Razavi and Nicholas Ayache and Maxime Sermesant",

year = "2017",

month = sep,

day = "1",

doi = "10.1109/TBME.2016.2629849",

language = "English",

volume = "64",

pages = "2206--2218",

journal = "IEEE Transactions on Biomedical Engineering",

issn = "0018-9294",

publisher = "IEEE Computer Society",

number = "9",

}

TY - JOUR

T1 - Noninvasive Personalization of a Cardiac Electrophysiology Model from Body Surface Potential Mapping

AU - Giffard-Roisin, Sophie

AU - Jackson, Thomas

AU - Fovargue, Lauren

AU - Lee, Jack

AU - Delingette, Herve

AU - Razavi, Reza

AU - Ayache, Nicholas

AU - Sermesant, Maxime

PY - 2017/9/1

Y1 - 2017/9/1

N2 - Goal: We use noninvasive data (body surface potential mapping, BSPM) to personalize the main parameters of a cardiac electrophysiological (EP) model for predicting the response to different pacing conditions. Methods: First, an efficient forward model is proposed, coupling the Mitchell-Schaeffer transmembrane potential model with a current dipole formulation. Then, we estimate the main parameters of the cardiac model: Activation onset location and tissue conductivity. A large patient-specific database of simulated BSPM is generated, from which specific features are extracted to train a machine learning algorithm. The activation onset location is computed from a Kernel Ridge Regression and a second regression calibrates the global ventricular conductivity. Results: The evaluation of the results is done both on a benchmark dataset of a patient with premature ventricular contraction (PVC) and on five nonischaemic implanted cardiac resynchonization therapy (CRT) patients with a total of 21 different pacing conditions. Good personalization results were found in terms of the activation onset location for the PVC (mean distance error, MDE = 20.3 mm), for the pacing sites (MDE = 21.7 mm) and for the CRT patients (MDE = 24.6 mm). We tested the predictive power of the personalized model for biventricular pacing and showed that we could predict the new electrical activity patterns with a good accuracy in terms of BSPM signals. Conclusion: We have personalized the cardiac EP model and predicted new patient-specific pacing conditions. Significance: This is an encouraging first step towards a noninvasive preoperative prediction of the response to different pacing conditions to assist clinicians for CRT patient selection and therapy planning.

AB - Goal: We use noninvasive data (body surface potential mapping, BSPM) to personalize the main parameters of a cardiac electrophysiological (EP) model for predicting the response to different pacing conditions. Methods: First, an efficient forward model is proposed, coupling the Mitchell-Schaeffer transmembrane potential model with a current dipole formulation. Then, we estimate the main parameters of the cardiac model: Activation onset location and tissue conductivity. A large patient-specific database of simulated BSPM is generated, from which specific features are extracted to train a machine learning algorithm. The activation onset location is computed from a Kernel Ridge Regression and a second regression calibrates the global ventricular conductivity. Results: The evaluation of the results is done both on a benchmark dataset of a patient with premature ventricular contraction (PVC) and on five nonischaemic implanted cardiac resynchonization therapy (CRT) patients with a total of 21 different pacing conditions. Good personalization results were found in terms of the activation onset location for the PVC (mean distance error, MDE = 20.3 mm), for the pacing sites (MDE = 21.7 mm) and for the CRT patients (MDE = 24.6 mm). We tested the predictive power of the personalized model for biventricular pacing and showed that we could predict the new electrical activity patterns with a good accuracy in terms of BSPM signals. Conclusion: We have personalized the cardiac EP model and predicted new patient-specific pacing conditions. Significance: This is an encouraging first step towards a noninvasive preoperative prediction of the response to different pacing conditions to assist clinicians for CRT patient selection and therapy planning.

KW - Cardiac electrophysiology

KW - ECG imaging

KW - inverse problem of ECG

KW - parameter estimation

KW - personalization

UR - http://www.scopus.com/inward/record.url?scp=85029844083&partnerID=8YFLogxK

U2 - 10.1109/TBME.2016.2629849

DO - 10.1109/TBME.2016.2629849

M3 - Article

C2 - 28113292

SN - 0018-9294

VL - 64

SP - 2206

EP - 2218

JO - IEEE Transactions on Biomedical Engineering

JF - IEEE Transactions on Biomedical Engineering

IS - 9

M1 - 7745951

ER -

Noninvasive Personalization of a Cardiac Electrophysiology Model from Body Surface Potential Mapping

Abstract

Keywords

Access to Document

Other files and links

Fingerprint

Cite this