Patient Specific Simulation of Body Surface ECG using the Finite Element Method

Okada, Junichi; Sasaki, Teruyoshi; Washio, Takumi; Yamashita, Hiroshi; Kariya, Taro; Imai, Yasushi; Nakagawa, Machiko; Kadooka, Yoshimasa; Nagai, Ryozo; Hisada, Toshiaki; Sugiura, Seiryo

doi:10.1111/pace.12057

Cited by 28 publications

(34 citation statements)

References 32 publications

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“…Our activation sequences have been shown to replicate the main diagnostic manifestations of left anterior fascicular, left posterior fascicular, left bundle branch and right bundle branch block conditions. Previous computational studies have also shown agreement in diseased QRS biomarkers associated to left bundle branch block, using either a similar modelling approach to ours for the human activation sequence, 18 or more detailed anatomical representations of the human Purkinje network 16 . Sahli et al 17 also addressed the modelling of right bundle branch block using detailed Purkinje trees, however, without probing its impact on the precordial leads, which are the derivations used in its clinical diagnosis.…”

Section: Discussionmentioning

confidence: 62%

“…These vary in generality, from specifying activation times analytically by means of a parameterized sequence, 15 to the creation of idealized Purkinje networks including explicit Purkinje-muscle junctions 16 , 17 . They also differ in computational complexity, from the semi-automatic generation of activation profiles, 10 , 18 to more iterative and interactive parametrization processes to converge on a personalized activation sequence 16 . The activation sequences utilized here encapsulate the minimal information needed to investigate the implication on the QRS complex of anatomical variability in the human trifascicular conduction system.…”

Section: Discussionmentioning

confidence: 99%

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Human ventricular activation sequence and the simulation of the electrocardiographic QRS complex and its variability in healthy and intraventricular block conditions

et al. 2016

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AimsTo investigate how variability in activation sequence and passive conduction properties translates into clinical variability in QRS biomarkers, and gain novel physiological knowledge on the information contained in the human QRS complex.Methods and resultsMultiscale bidomain simulations using a detailed heart-torso human anatomical model are performed to investigate the impact of activation sequence characteristics on clinical QRS biomarkers. Activation sequences are built and validated against experimentally-derived ex vivo and in vivo human activation data. R-peak amplitude exhibits the largest variability in terms of QRS morphology, due to its simultaneous modulation by activation sequence speed, myocardial intracellular and extracellular conductivities, and propagation through the human torso. QRS width, however, is regulated by endocardial activation speed and intracellular myocardial conductivities, whereas QR intervals are only affected by the endocardial activation profile. Variability in the apico-basal location of activation sites on the anterior and posterior left ventricular wall is associated with S-wave progression in limb and precordial leads, respectively, and occasional notched QRS complexes in precordial derivations. Variability in the number of early activation sites successfully reproduces pathological abnormalities of the human conduction system in the QRS complex.ConclusionVariability in activation sequence and passive conduction properties captures and explains a large part of the clinical variability observed in the human QRS complex. Our physiological insights allow for a deeper interpretation of human QRS biomarkers in terms of QRS morphology and location of early endocardial activation sites. This might be used to attain a better patient-specific knowledge of activation sequence from routine body-surface electrocardiograms.

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Section: Discussionmentioning

confidence: 62%

Section: Discussionmentioning

confidence: 99%

Human ventricular activation sequence and the simulation of the electrocardiographic QRS complex and its variability in healthy and intraventricular block conditions

et al. 2016

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“…We have developed a multi-scale, multi-physics three-dimensional (3D) heart simulator in which propagation of excitation, contraction and relaxation, development of pressure, and blood flow are reproduced based on molecular models of the cardiac excitation-contraction process [13][14][15][16][17]. We also succeeded in simulating the patientspecific body surface ECG [18]. Applying these technologies, we created a tailor-made simulation model of the heart to determine if the effects of CRT could be predicted in a canine model of heart failure with LBBB [19].…”

Section: Introductionmentioning

confidence: 99%

Multi-scale, tailor-made heart simulation can predict the effect of cardiac resynchronization therapy

Okada

Washio

Nakagawa

et al. 2017

Journal of Molecular and Cellular Cardiology

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“…Despite being computationally intensive these models have become increasingly realistic and informative. They have begun to be more widely used due to the greater availability and increase in computing power with models of the heart even being constructed for individual patients to aid diagnosis [17]. Simulations of the heart can address its electrical or mechanical properties and current models incorporate either or both of these aspects [18–20].…”

Section: Introductionmentioning

confidence: 99%

3D Finite Element Electrical Model of Larval Zebrafish ECG Signals

Crowcombe¹,

Dhillon²,

Hurst³

et al. 2016

PLoS ONE

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Assessment of heart function in zebrafish larvae using electrocardiography (ECG) is a potentially useful tool in developing cardiac treatments and the assessment of drug therapies. In order to better understand how a measured ECG waveform is related to the structure of the heart, its position within the larva and the position of the electrodes, a 3D model of a 3 days post fertilisation (dpf) larval zebrafish was developed to simulate cardiac electrical activity and investigate the voltage distribution throughout the body. The geometry consisted of two main components; the zebrafish body was modelled as a homogeneous volume, while the heart was split into five distinct regions (sinoatrial region, atrial wall, atrioventricular band, ventricular wall and heart chambers). Similarly, the electrical model consisted of two parts with the body described by Laplace’s equation and the heart using a bidomain ionic model based upon the Fitzhugh-Nagumo equations. Each region of the heart was differentiated by action potential (AP) parameters and activation wave conduction velocities, which were fitted and scaled based on previously published experimental results. ECG measurements in vivo at different electrode recording positions were then compared to the model results. The model was able to simulate action potentials, wave propagation and all the major features (P wave, R wave, T wave) of the ECG, as well as polarity of the peaks observed at each position. This model was based upon our current understanding of the structure of the normal zebrafish larval heart. Further development would enable us to incorporate features associated with the diseased heart and hence assist in the interpretation of larval zebrafish ECGs in these conditions.

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Patient Specific Simulation of Body Surface ECG using the Finite Element Method

Abstract: These results not only help us understand the cellular basis of the body surface ECG, but also open the possibility of heart simulation for clinical applications.

Cited by 28 publications

References 32 publications

Human ventricular activation sequence and the simulation of the electrocardiographic QRS complex and its variability in healthy and intraventricular block conditions

Human ventricular activation sequence and the simulation of the electrocardiographic QRS complex and its variability in healthy and intraventricular block conditions

Multi-scale, tailor-made heart simulation can predict the effect of cardiac resynchronization therapy

3D Finite Element Electrical Model of Larval Zebrafish ECG Signals

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