155 research outputs found
Electron-conformational transformations in nanoscopic RyR channels govern both the heart's contraction and beating
We show that a simple biophysically based electron-conformational model of
RyR channel is able to explain and describe on equal footing the oscillatory
regime of the heart's cell release unit both in sinoatrial node (pacemaker)
cells under normal physiological conditions and in ventricular myocytes under
Ca SR overload.Comment: 6 pages, 3 figure
Classification Model of Heart Transplant Outcomes Based on Features of Left Ventricular Functional Geometry
The function of the transplanted heart can be significantly affected by acute allograft rejection, chronic rejection, high blood pressure. These factors may induce cardiac remodelling with further adverse consequences for the patients. Dynamic change in the configuration of the left ventricle (LV) from end diastole to end systole (LV functional geometry) is an important factor of the heart pump function. The objective of this study is to evaluate the time dependent changes in parameters of LV functional geometry in the transplanted heart and to assess relations between the changes and adverse outcomes of the heart transplantation (HT). We used linear discriminant analysis (LDA) to build classification models based on either the standard echocardiographic parameters of LV systolic function and global longitudinal strain (GLS) or LV function geometry indexes. The training set for model building included data from patients with different degrees of systolic dysfunction. Using the models, we retrospectively classified data from 31 patients after orthotropic HT. In contrast to the LDA models based on the standard echocardiographic characteristics and GLS, the model based on the LV functional geometry data showed high accuracy in predicting allograft rejection and development of the heart failure in the HT patients. Β© 2018 Creative Commons Attribution
The activated charcoal adsorption of phenol
The problems of treatment of the waste water were considered in this work. The main method of the activated charcoal adsorption of phenol was investigated. The adsorption properties of the adsorbent were also considered. Along with it Langmuir's and Friendlich's adsorption isotherm has been studied and analyzed
Role of myocardial properties and pacing lead location on ECG in personalized paced heart models
Personalised cardiac models were built from the computed tomography imaging data for two patients with implanted cardiac resynchronisation therapy devices. The cardiac models comprised a biventricular model of myocardial electrophysiology coupled with a model of the torso to simulate the body surface potential map. The models were verified against electrocardiogams (ECG) recorded in the patients from 240 leads on the body surface under left ventricular pacing. The simulated ECG demonstrated a significant sensitivity to the myocardial anisotropy and location of the pacing electrode tip in the models. An apicobasal cellular heterogeneity was shown to be less significant for the ECG pattern at the paced-ventricle activation than that showed earlier by Keller and co-authors (2012) for the normal activation sequence. Β© 2017 IEEE Computer Society. All rights reserved.This study was supported by the RAS Presidium Programme I.33Ξ , and Government of the Russian Federation (agreement 02.A03.21.0006). We used the computational clusters of Ural Federal University and βURANβ of Institute of Mathematics and Mechanics (Ekaterinburg)
Integrative Modeling of Electrical Properties of Pacemaker Cardiac Cells
This work represents modeling of electrical properties of pacemaker (sinus) cardiac cells. Special attention is paid to electrical potential arising from transmembrane current of Na{+}, K{+} and Ca{2+} ions. This potential is calculated using the NaCaX model. In this respect, molar concentration of ions in the intercellular space which is calculated on the basis of the GENTEX model is essential. Combined use of two different models allows referring this approach to integrative modeling
Effects of Lead Position, Cardiac Rhythm Variation and Drug-induced QT Prolongation on Performance of Machine Learning Methods for ECG Processing
Machine learning shows great performance in various problems of electrocardiography (ECG) signal analysis. However, collecting a dataset for biomedical engineering is a very difficult task. Any dataset for ECG processing contains from 100 to 10,000 times fewer cases than datasets for image or text analysis. This issue is especially important because of physiological phenomena that can significantly change the morphology of heartbeats in ECG signals. In this preliminary study, we analyze the effects of lead choice from the standard ECG recordings, variation of ECG during 24-hours, and the effects of QT-prolongation agents on the performance of machine learning methods for ECG processing. We choose the problem of subject identification for analysis, because this problem may be solved for almost any available dataset of ECG data. In a discussion, we compare our findings with observations from other works that use machine learning for ECG processing with different problem statements. Our results show the importance of training dataset enrichment with ECG signals acquired in specific physiological conditions for obtaining good performance of ECG processing for real applications. Β© 2020 IEEE.The reported study was supported by RFBR research project No. 19-37-50079 and supported by the IIF UrB RAS theme βAAAA-A18-118020590031-8, RF Government Act #211 of March 16, 2013, the Program of the Presidium RAS
Electron-Conformational Transformations in Nanoscopic RyR Channels Governing Both the Heart's Contraction and Beating
We show that a simple biophysically based electron-conformational model of RyR channel can explain and describe on equal footing the oscillatory regime of the heart's cell release unit both in sinoatrial node (pacemaker) cells under normal physiological conditions and in ventricular myocytes under Ca2+ sarcoplasmic reticulum overload. Β© 2011 Pleiades Publishing, Ltd.This work was supported by the Ural Branch, Russian Academy of Sciences, project no. 09 M 14 2001
Classification Model of Heart Transplant Outcomes Based on Features of Left Ventricular Functional Geometry
ΠΠ½Π΅Π²ΡΠΈΠ·ΠΌΡ ΠΏΠΎΡΠ΅ΡΠ½ΡΡ Π°ΡΡΠ΅ΡΠΈΠΉ
Renal artery aneurysms are a rare condition and are usually found when other abdominal organ diseases are being searched. Among the causes of renal artery aneurysms, atherosclerosis and fibromuscular dysplasia predominate. However, they can also be observed in congenital Ehlers-Danlos syndrome, neurofibromatosis, arteritis, and due to traumatic effects. Most patients have an asymptomatic course of the disease. Literature data suggest slow growth of aneurysms, and their progression is associated with arterial hypertension, absence of wall calcification and pregnancy in young women. The aim of surgical treatment is to prevent aneurysm rupture, eliminate the risk of renal parenchyma embolism and correct arterial hypertension. Most authors believe that surgical treatment is indicated for asymptomatic course of the disease when the aneurysm is over 20 mm in diameter, aneurysm growth is over 5 mm within a year, arterial hypertension resistant to drug therapy, renal artery dissection and aneurysm presence in women of childbearing age. There are a number of surgical and endovascular techniques to restore renal blood flow. Both open and endovascular interventions are used for renal artery trunk surgery. For aneurysms of the renal artery branches, aortorenal shunting by autovenous or internal iliac artery as well as extracorporeal surgeries are more often used. The use of endografts is most appropriate for localization of aneurysms in the renal artery trunk, while embolization with microspirals and glue is most effective for saccular aneurysms. The embolization technique can cause embolization of the renal parenchyma itself as a potential complication, which aggravates arterial hypertension. The authors present the literature and their own data on various techniques to restore the renal blood flow. Up to 80-90% of the operated kidneys can be saved in the long term. Reconstructive surgery reduces the level of arterial pressure and reduces the number of antihypertensive drugs used and the need for renal replacement therapy.ΠΠ½Π΅Π²ΡΠΈΠ·ΠΌΡ ΠΏΠΎΡΠ΅ΡΠ½ΡΡ
Π°ΡΡΠ΅ΡΠΈΠΉ ΠΎΡΠ½ΠΎΡΡΡΡΡ ΠΊ ΡΠ΅Π΄ΠΊΠΈΠΌ Π·Π°Π±ΠΎΠ»Π΅Π²Π°Π½ΠΈΡΠΌ ΠΈ, ΠΊΠ°ΠΊ ΠΏΡΠ°Π²ΠΈΠ»ΠΎ, ΠΎΠ±Π½Π°ΡΡΠΆΠΈΠ²Π°ΡΡΡΡ ΠΏΡΠΈ ΠΏΠΎΠΈΡΠΊΠ΅ Π΄ΡΡΠ³ΠΈΡ
Π·Π°Π±ΠΎΠ»Π΅Π²Π°Π½ΠΈΠΉ ΠΎΡΠ³Π°Π½ΠΎΠ² Π±ΡΡΡΠ½ΠΎΠΉ ΠΏΠΎΠ»ΠΎΡΡΠΈ. Π‘ΡΠ΅Π΄ΠΈ ΠΏΡΠΈΡΠΈΠ½ Π²ΠΎΠ·Π½ΠΈΠΊΠ½ΠΎΠ²Π΅Π½ΠΈΡ Π°Π½Π΅Π²ΡΠΈΠ·ΠΌ ΠΏΠΎΡΠ΅ΡΠ½ΡΡ
Π°ΡΡΠ΅ΡΠΈΠΉ ΠΏΡΠ΅ΠΎΠ±Π»Π°Π΄Π°ΡΡ Π°ΡΠ΅ΡΠΎΡΠΊΠ»Π΅ΡΠΎΠ· ΠΈ ΡΠΈΠ±ΡΠΎΠ·Π½ΠΎ-ΠΌΡΡΠ΅ΡΠ½Π°Ρ Π΄ΠΈΡΠΏΠ»Π°Π·ΠΈΡ. ΠΠ΄Π½Π°ΠΊΠΎ ΠΎΠ½ΠΈ ΠΌΠΎΠ³ΡΡ Π½Π°Π±Π»ΡΠ΄Π°ΡΡΡΡ ΠΈ ΠΏΡΠΈ Π²ΡΠΎΠΆΠ΄Π΅Π½Π½ΠΎΠΌ ΡΠΈΠ½Π΄ΡΠΎΠΌΠ΅ ΠΠ»Π΅ΡΡΠ° β ΠΠ°Π½Π»ΠΎΡΠ°, Π½Π΅ΠΉΡΠΎΡΠΈΠ±ΡΠΎΠΌΠ°ΡΠΎΠ·Π΅, Π°ΡΡΠ΅ΡΠΈΠΈΡΠ°Ρ
ΠΈ Π²ΡΠ»Π΅Π΄ΡΡΠ²ΠΈΠ΅ ΡΡΠ°Π²ΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΈΡ
Π²ΠΎΠ·Π΄Π΅ΠΉΡΡΠ²ΠΈΠΉ. ΠΠΎΠ»ΡΡΠΈΠ½ΡΡΠ²ΠΎ ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠ² ΠΈΠΌΠ΅ΡΡ Π°ΡΠΈΠΌΠΏΡΠΎΠΌΠ½ΠΎΠ΅ ΡΠ΅ΡΠ΅Π½ΠΈΠ΅ Π·Π°Π±ΠΎΠ»Π΅Π²Π°Π½ΠΈΡ. ΠΠΈΡΠ΅ΡΠ°ΡΡΡΠ½ΡΠ΅ Π΄Π°Π½Π½ΡΠ΅ ΡΠ²ΠΈΠ΄Π΅ΡΠ΅Π»ΡΡΡΠ²ΡΡΡ ΠΎ ΠΌΠ΅Π΄Π»Π΅Π½Π½ΠΎΠΌ ΡΠΎΡΡΠ΅ Π°Π½Π΅Π²ΡΠΈΠ·ΠΌ, Π° ΠΈΡ
ΠΏΡΠΎΠ³ΡΠ΅ΡΡΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅ ΡΠ²ΡΠ·ΡΠ²Π°ΡΡ Ρ Π°ΡΡΠ΅ΡΠΈΠ°Π»ΡΠ½ΠΎΠΉ Π³ΠΈΠΏΠ΅ΡΡΠ΅Π½Π·ΠΈΠ΅ΠΉ, ΠΎΡΡΡΡΡΡΠ²ΠΈΠ΅ΠΌ ΠΊΠ°Π»ΡΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ ΡΡΠ΅Π½ΠΊΠΈ ΠΈ Π±Π΅ΡΠ΅ΠΌΠ΅Π½Π½ΠΎΡΡΡΡ Ρ ΠΌΠΎΠ»ΠΎΠ΄ΡΡ
ΠΆΠ΅Π½ΡΠΈΠ½. Π¦Π΅Π»ΡΡ Ρ
ΠΈΡΡΡΠ³ΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ Π»Π΅ΡΠ΅Π½ΠΈΡ ΡΠ²Π»ΡΡΡΡΡ ΠΏΡΠΎΡΠΈΠ»Π°ΠΊΡΠΈΠΊΠ° ΡΠ°Π·ΡΡΠ²Π° Π°Π½Π΅Π²ΡΠΈΠ·ΠΌΡ, ΡΡΡΡΠ°Π½Π΅Π½ΠΈΠ΅ ΡΠΈΡΠΊΠ° ΡΠΌΠ±ΠΎΠ»ΠΈΠΈ ΠΏΠΎΡΠ΅ΡΠ½ΠΎΠΉ ΠΏΠ°ΡΠ΅Π½Ρ
ΠΈΠΌΡ ΠΈ ΠΊΠΎΡΡΠ΅ΠΊΡΠΈΡ Π°ΡΡΠ΅ΡΠΈΠ°Π»ΡΠ½ΠΎΠΉ Π³ΠΈΠΏΠ΅ΡΡΠ΅Π½Π·ΠΈΠΈ. ΠΠΎΠ»ΡΡΠΈΠ½ΡΡΠ²ΠΎ Π°Π²ΡΠΎΡΠΎΠ² ΡΡΠΈΡΠ°ΡΡ, ΡΡΠΎ ΠΏΡΠΈ Π°ΡΠΈΠΌΠΏΡΠΎΠΌΠ½ΠΎΠΌ ΡΠ΅ΡΠ΅Π½ΠΈΠΈ Π·Π°Π±ΠΎΠ»Π΅Π²Π°Π½ΠΈΡ ΠΎΠΏΠ΅ΡΠ°ΡΠΈΠ²Π½ΠΎΠ΅ Π»Π΅ΡΠ΅Π½ΠΈΠ΅ ΠΏΠΎΠΊΠ°Π·Π°Π½ΠΎ ΠΏΡΠΈ Π΄ΠΈΠ°ΠΌΠ΅ΡΡΠ΅ Π°Π½Π΅Π²ΡΠΈΠ·ΠΌΡ Π±ΠΎΠ»Π΅Π΅ 20 ΠΌΠΌ, ΡΠΎΡΡΠ΅ Π°Π½Π΅Π²ΡΠΈΠ·ΠΌΡ Π±ΠΎΠ»Π΅Π΅ 5 ΠΌΠΌ Π² ΡΠ΅ΡΠ΅Π½ΠΈΠ΅ Π³ΠΎΠ΄Π°, Π°ΡΡΠ΅ΡΠΈΠ°Π»ΡΠ½ΠΎΠΉ Π³ΠΈΠΏΠ΅ΡΡΠ΅Π½Π·ΠΈΠΈ, ΡΠ΅Π·ΠΈΡΡΠ΅Π½ΡΠ½ΠΎΠΉ ΠΊ ΠΌΠ΅Π΄ΠΈΠΊΠ°ΠΌΠ΅Π½ΡΠΎΠ·Π½ΠΎΠΉ ΡΠ΅ΡΠ°ΠΏΠΈΠΈ, Π΄ΠΈΡΡΠ΅ΠΊΡΠΈΠΈ ΠΏΠΎΡΠ΅ΡΠ½ΠΎΠΉ Π°ΡΡΠ΅ΡΠΈΠΈ ΠΈ Π½Π°Π»ΠΈΡΠΈΠΈ Π°Π½Π΅Π²ΡΠΈΠ·ΠΌΡ Ρ ΠΆΠ΅Π½ΡΠΈΠ½ Π΄Π΅ΡΠΎΡΠΎΠ΄Π½ΠΎΠ³ΠΎ Π²ΠΎΠ·ΡΠ°ΡΡΠ°. Π‘ΡΡΠ΅ΡΡΠ²ΡΠ΅Ρ ΡΡΠ΄ Ρ
ΠΈΡΡΡΠ³ΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΈ ΡΠ½Π΄ΠΎΠ²Π°ΡΠΊΡΠ»ΡΡΠ½ΡΡ
ΠΌΠ΅ΡΠΎΠ΄ΠΈΠΊ, ΠΏΠΎΠ·Π²ΠΎΠ»ΡΡΡΠΈΡ
Π²ΠΎΡΡΡΠ°Π½ΠΎΠ²ΠΈΡΡ ΠΏΠΎΡΠ΅ΡΠ½ΡΠΉ ΠΊΡΠΎΠ²ΠΎΡΠΎΠΊ. ΠΠ»Ρ Ρ
ΠΈΡΡΡΠ³ΠΈΠΈ ΡΡΠ²ΠΎΠ»Π° ΠΏΠΎΡΠ΅ΡΠ½ΠΎΠΉ Π°ΡΡΠ΅ΡΠΈΠΈ ΠΈΡΠΏΠΎΠ»ΡΠ·ΡΠ΅ΡΡΡ ΠΌΠ΅ΡΠΎΠ΄ΠΈΠΊΠ° ΠΊΠ°ΠΊ ΠΎΡΠΊΡΡΡΡΡ
, ΡΠ°ΠΊ ΠΈ ΡΠ½Π΄ΠΎΠ²Π°ΡΠΊΡΠ»ΡΡΠ½ΡΡ
Π²ΠΌΠ΅ΡΠ°ΡΠ΅Π»ΡΡΡΠ². ΠΡΠΈ Π°Π½Π΅Π²ΡΠΈΠ·ΠΌΠ°Ρ
Π²Π΅ΡΠ²Π΅ΠΉ ΠΏΠΎΡΠ΅ΡΠ½ΠΎΠΉ Π°ΡΡΠ΅ΡΠΈΠΈ ΡΠ°ΡΠ΅ ΠΈΡΠΏΠΎΠ»ΡΠ·ΡΠ΅ΡΡΡ Π°ΠΎΡΡΠΎΡΠ΅Π½Π°Π»ΡΠ½ΠΎΠ΅ ΡΡΠ½ΡΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅ Π°ΡΡΠΎΠ²Π΅Π½ΠΎΠΉ ΠΈΠ»ΠΈ Π²Π½ΡΡΡΠ΅Π½Π½Π΅ΠΉ ΠΏΠΎΠ΄Π²Π·Π΄ΠΎΡΠ½ΠΎΠΉ Π°ΡΡΠ΅ΡΠΈΠ΅ΠΉ, Π° ΡΠ°ΠΊΠΆΠ΅ ΡΠΊΡΡΡΠ°ΠΊΠΎΡΠΏΠΎΡΠ°Π»ΡΠ½ΡΠ΅ ΠΎΠΏΠ΅ΡΠ°ΡΠΈΠΈ. ΠΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ ΡΠ½Π΄ΠΎΠ³ΡΠ°ΡΡΠΎΠ² Π½Π°ΠΈΠ±ΠΎΠ»Π΅Π΅ ΡΠ΅Π»Π΅ΡΠΎΠΎΠ±ΡΠ°Π·Π½ΠΎ ΠΏΡΠΈ Π»ΠΎΠΊΠ°Π»ΠΈΠ·Π°ΡΠΈΠΈ Π°Π½Π΅Π²ΡΠΈΠ·ΠΌ Π² ΡΡΠ²ΠΎΠ»Π΅ ΠΏΠΎΡΠ΅ΡΠ½ΠΎΠΉ Π°ΡΡΠ΅ΡΠΈΠΈ, Π² ΡΠΎ Π²ΡΠ΅ΠΌΡ ΠΊΠ°ΠΊ ΡΠΌΠ±ΠΎΠ»ΠΈΠ·Π°ΡΠΈΡ ΠΌΠΈΠΊΡΠΎΡΠΏΠΈΡΠ°Π»ΡΠΌΠΈ ΠΈ ΠΊΠ»Π΅Π΅ΠΌ Π½Π°ΠΈΠ±ΠΎΠ»Π΅Π΅ ΡΡΡΠ΅ΠΊΡΠΈΠ²Π½Π° ΠΏΡΠΈ ΠΌΠ΅ΡΠΎΡΡΠ°ΡΡΡ
Π°Π½Π΅Π²ΡΠΈΠ·ΠΌΠ°Ρ
. ΠΠ΅ΡΠΎΠ΄ΠΈΠΊΠ° ΡΠΌΠ±ΠΎΠ»ΠΈΠ·Π°ΡΠΈΠΈ Π² ΠΊΠ°ΡΠ΅ΡΡΠ²Π΅ ΠΏΠΎΡΠ΅Π½ΡΠΈΠ°Π»ΡΠ½ΡΡ
ΠΎΡΠ»ΠΎΠΆΠ½Π΅Π½ΠΈΠΉ ΠΌΠΎΠΆΠ΅Ρ Π²ΡΠ·ΡΠ²Π°ΡΡ ΡΠΌΠ±ΠΎΠ»ΠΈΡ ΡΠ°ΠΌΠΎΠΉ ΠΏΠΎΡΠ΅ΡΠ½ΠΎΠΉ ΠΏΠ°ΡΠ΅Π½Ρ
ΠΈΠΌΡ, ΡΡΠΎ ΡΡΡΠ³ΡΠ±Π»ΡΠ΅Ρ Π°ΡΡΠ΅ΡΠΈΠ°Π»ΡΠ½ΡΡ Π³ΠΈΠΏΠ΅ΡΡΠ΅Π½Π·ΠΈΡ. Π ΡΡΠ°ΡΡΠ΅ Π°Π²ΡΠΎΡΡ ΠΏΡΠΈΠ²ΠΎΠ΄ΡΡ Π»ΠΈΡΠ΅ΡΠ°ΡΡΡΠ½ΡΠ΅ ΠΈ ΡΠΎΠ±ΡΡΠ²Π΅Π½Π½ΡΠ΅ Π΄Π°Π½Π½ΡΠ΅ ΠΎ ΡΠ°Π·Π»ΠΈΡΠ½ΡΡ
ΠΌΠ΅ΡΠΎΠ΄ΠΈΠΊΠ°Ρ
Π²ΠΎΡΡΡΠ°Π½ΠΎΠ²Π»Π΅Π½ΠΈΡ ΠΏΠΎΡΠ΅ΡΠ½ΠΎΠ³ΠΎ ΠΊΡΠΎΠ²ΠΎΡΠΎΠΊΠ°. Π ΠΎΡΠ΄Π°Π»Π΅Π½Π½ΡΠ΅ ΡΡΠΎΠΊΠΈ ΡΠ΄Π°Π΅ΡΡΡ ΡΠΎΡ
ΡΠ°Π½ΠΈΡΡ Π΄ΠΎ 80β90% ΠΎΠΏΠ΅ΡΠΈΡΠΎΠ²Π°Π½Π½ΡΡ
ΠΏΠΎΡΠ΅ΠΊ. Π Π΅ΠΊΠΎΠ½ΡΡΡΡΠΊΡΠΈΠ²Π½ΡΠ΅ ΠΎΠΏΠ΅ΡΠ°ΡΠΈΠΈ ΡΠ½ΠΈΠΆΠ°ΡΡ ΡΡΠΎΠ²Π΅Π½Ρ Π°ΡΡΠ΅ΡΠΈΠ°Π»ΡΠ½ΠΎΠ³ΠΎ Π΄Π°Π²Π»Π΅Π½ΠΈΡ ΠΈ ΡΠΌΠ΅Π½ΡΡΠ°ΡΡ ΠΊΠΎΠ»ΠΈΡΠ΅ΡΡΠ²ΠΎ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½Π½ΡΡ
Π°Π½ΡΠΈΠ³ΠΈΠΏΠ΅ΡΡΠ΅Π½Π·ΠΈΠ²Π½ΡΡ
ΠΏΡΠ΅ΠΏΠ°ΡΠ°ΡΠΎΠ² ΠΈ Π½Π΅ΠΎΠ±Ρ
ΠΎΠ΄ΠΈΠΌΠΎΡΡΡ ΠΏΠΎΡΠ΅ΡΠ½ΠΎΠΉ Π·Π°ΠΌΠ΅ΡΡΠΈΡΠ΅Π»ΡΠ½ΠΎΠΉ ΡΠ΅ΡΠ°ΠΏΠΈΠΈ
Meshless electrophysiological modeling of cardiac resynchronization therapyβbenchmark analysis with finite-element methods in experimental data
Computational models of cardiac electrophysiology are promising tools for reducing the rates of non-response patients suitable for cardiac resynchronization therapy (CRT) by optimizing electrode placement. The majority of computational models in the literature are mesh-based, primarily using the finite element method (FEM). The generation of patient-specific cardiac meshes has traditionally been a tedious task requiring manual intervention and hindering the modeling of a large number of cases. Meshless models can be a valid alternative due to their mesh quality independence. The organization of challenges such as the CRT-EPiggy19, providing unique experimental data as open access, enables benchmarking analysis of different cardiac computational modeling solutions with quantitative metrics. We present a benchmark analysis of a meshless-based method with finite-element methods for the prediction of cardiac electrical patterns in CRT, based on a subset of the CRT-EPiggy19 dataset. A data assimilation strategy was designed to personalize the most relevant parameters of the electrophysiological simulations and identify the optimal CRT lead configuration. The simulation results obtained with the meshless model were equivalent to FEM, with the most relevant aspect for accurate CRT predictions being the parameter personalization strategy (e.g., regional conduction velocity distribution, including the Purkinje system and CRT lead distribution). Β© 2022 by the authors. Licensee MDPI, Basel, Switzerland
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