Computing in cardiology最新文献

{"title":"The Effect of Segmentation Variability in Forward ECG Simulation.","authors":"Beata Ondrusova,&nbsp;Machteld Boonstra,&nbsp;Jana Svehlikova,&nbsp;Dana Brooks,&nbsp;Peter van Dam,&nbsp;Ali Salman Rababah,&nbsp;Akil Narayan,&nbsp;Rob MacLeod,&nbsp;Nejib Zemzemi,&nbsp;Jess Tate","doi":"10.22489/cinc.2022.325","DOIUrl":"10.22489/cinc.2022.325","url":null,"abstract":"<p><p>Segmentation of patient-specific anatomical models is one of the first steps in Electrocardiographic imaging (ECGI). However, the effect of segmentation variability on ECGI remains unexplored. In this study, we assess the effect of heart segmentation variability on ECG simulation. We generated a statistical shape model from segmentations of the same patient and generated 262 cardiac geometries to run in an ECG forward computation of body surface potentials (BSPs) using an equivalent dipole layer cardiac source model and 5 ventricular stimulation protocols. Variability between simulated BSPs for all models and protocols was assessed using Pearson's correlation coefficient (CC). Compared to the BSPs of the mean cardiac shape model, the lowest variability (average CC = 0.98 ± 0.03) was found for apical pacing whereas the highest variability (average CC = 0.90 ± 0.23) was found for right ventricular free wall pacing. Furthermore, low amplitude BSPs show a larger variation in QRS morphology compared to high amplitude signals. The results indicate that the uncertainty in cardiac shape has a significant impact on ECGI.</p>","PeriodicalId":72683,"journal":{"name":"Computing in cardiology","volume":"49 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10552847/pdf/nihms-1884572.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41157877","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Toward a Quasi-dynamic Pulsed Field Electroporation Numerical Model for Cardiac Ablation: Predicting Tissue Conductance Changes and Ablation Lesion Patterns.","authors":"Richard Simon,&nbsp;Nishaki K Mehta,&nbsp;Kuldeep B Shah,&nbsp;David E Haines,&nbsp;Cristian A Linte","doi":"10.22489/CinC.2022.233","DOIUrl":"https://doi.org/10.22489/CinC.2022.233","url":null,"abstract":"<p><p>Pulsed field ablation (PFA) has the potential to evolve into an efficient alternative to traditional RF ablation for atrial fibrillation treatment. However, achieving irreversible tissue electroporation is critical to suppressing arrhythmic pathways, raising the need for accurate lesion characterization. To understand the physics behind the tissue response PFA, we propose a quasi-dynamic model that quantifies tissue conductance at end-electroporation and identifies regions that have undergone fully irreversible electroporation (IRE). The model uses several parameters and numerically solves the electrical field diffusion into the tissue by iteratively updating the tissue conductance until equilibrium at end-electroporation. The model yields a steady-state tissue conductance map used to identify the irreversible lesion. We conducted numerical experiments mimicking a lasso catheter featuring nine 3-mm electrodes spaced circumferentially at 3.75 mm and fired sequentially using a 1500 V and 3000 V pulse amplitude. The IRE lesion region has a surface area and volume of 780 mm² and 1411 mm³, respectively, at 1500 V, and 1178 mm² and 2760 mm³, respectively, at 3000 V. Lesion discontinuity was observed at 5.0 mm depth with 1500 V, and 7.2 mm depth with 3000 V. This quasi-dynamic model yields tissue conductance maps, predicts irreversible lesion and lesion penumbra at end-electroporation, and confirms larger lesions with higher pulse amplitudes.</p>","PeriodicalId":72683,"journal":{"name":"Computing in cardiology","volume":"2022 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10134894/pdf/nihms-1892746.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9391928","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effect of Segmentation Uncertainty on the ECGI Inverse Problem Solution and Source Localization.","authors":"Narimane Gassa, Machteld Boonstra, Beata Ondrusova, Jana Svehlikova, Dana Brooks, Akil Narayan, Ali Salman Rababah, Peter van Dam, Rob MacLeod, Jess Tate, Nejib Zemzemi","doi":"10.22489/cinc.2022.275","DOIUrl":"10.22489/cinc.2022.275","url":null,"abstract":"<p><p>Electrocardiographic Imaging (ECGI) is a promising tool to non-invasively map the electrical activity of the heart using body surface potentials (BSPs) and the patient specific anatomical data. One of the first steps of ECGI is the segmentation of the heart and torso geometries. In the clinical practice, the segmentation procedure is not fully-automated yet and is in consequence operator-dependent. We expect that the inter-operator variation in cardiac segmentation would influence the ECGI solution. This effect remains however non quantified. In the present work, we study the effect of segmentation variability on the ECGI estimation of the cardiac activity with 262 shape models generated from fifteen different segmentations. Therefore, we designed two test cases: with and without shape model uncertainty. Moreover, we used four cases for ectopic ventricular excitation and compared the ECGI results in terms of reconstructed activation times and excitation origins. The preliminary results indicate that a small variation of the activation maps can be observed with a model uncertainty but no significant effect on the source localization is observed.</p>","PeriodicalId":72683,"journal":{"name":"Computing in cardiology","volume":"49 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10544807/pdf/nihms-1884573.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41156365","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantifying Distributions of Parameters for Cardiac Action Potential Models Using the Hamiltonian Monte Carlo Method.","authors":"Alejandro Nieto Ramos,&nbsp;Conner J Herndon,&nbsp;Flavio H Fenton,&nbsp;Elizabeth M Cherry","doi":"10.23919/cinc53138.2021.9662836","DOIUrl":"https://doi.org/10.23919/cinc53138.2021.9662836","url":null,"abstract":"<p><strong>Aims: </strong>Cardiac action potential (AP) models are typically given with a single set of parameter values; however, this approach does not consider variability and uncertainty across individuals and experimental conditions. As an alternative to single-value parameter fitting, we sought to use a Bayesian approach, the Hamiltonian Monte Carlo (HMC) algorithm, to find distributions of physiological parameter values for cardiac AP models across a range of cycle lengths (CLs) and dynamics.</p><p><strong>Methods: </strong>To assess HMC's accuracy for cardiac data, we applied it to synthetic APs from the Mitchell-Shaeffer (MS) and Fenton-Karma (FK) models with added noise over a range of physiological CLs, some of which included alternans. To show the applicability of HMC to experimental data, we calculated parameter distributions for both models using micro-electrode recordings of zebrafish APs from a range of CLs.</p><p><strong>Results: </strong>For synthetic APs generated from three CLs using the MS (FK) models, HMC produced unimodal quasi-symmetric distributions for all five (13) parameters. APs generated by setting all parameters in the MS (FK) model to the modes of their corresponding marginal distributions yielded errors in voltage traces below 5.0% (0.6%). We also obtained distributions for the MS (FK) model parameters using zebrafish data to construct the first minimal model of the zebrafish AP, with voltage trace errors below 4.8% (3.4%).</p><p><strong>Conclusion: </strong>We have shown that HMC can identify not only a single set of parameter values but also viable distributions for cardiac AP model parameters using synthetic and experimental data. Because HMC generates samples from the parameter distributions based on input data, it can produce families of parameterizations that can be used in population-based modeling approaches without the need for rejecting a large number of randomly generated candidate parameterizations. HMC also has the potential to provide quantitative measures of spatial/individual variability and uncertainty.</p>","PeriodicalId":72683,"journal":{"name":"Computing in cardiology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9228588/pdf/nihms-1815728.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40401124","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Not all Long-QTs Are The Same, Proarrhytmic Quantification with Action Potential Triangulation and Alternans.","authors":"Ilija Uzelac,&nbsp;Shahriar Iravanian,&nbsp;Elizabeth M Cherry,&nbsp;Flavio H Fenton","doi":"10.23919/cinc53138.2021.9662721","DOIUrl":"https://doi.org/10.23919/cinc53138.2021.9662721","url":null,"abstract":"<p><p>Long-QT is commonly associated with an increased risk of polymorphic ventricular tachycardia from drug therapy. However, not all drugs prolonging QT interval are proarrhythmic. This study aimed to characterize cellular and tissue mechanisms under which QT-interval prolonging drugs and their combination are proarrhythmic, examining arrhythmia susceptibility due to action potential (AP) triangulation and spatial dispersion of action potential duration (APD). Additionally, we aimed to elucidate that Torsades de Pointe (TdP) associated with long-QT are not necessarily caused by early-after-depolarization (EADs) but are related to the presence of AP alternans in both time and space. Isolated Guinea Pig hearts were Langendorff perfused, and optical mapping was done with a voltage dye-sensitive dye. Two commonly used drugs at the beginning of the COVID-19 pandemic, hydroxychloroquine (HCQ) and Azithromycin (AZM), were added to study the effects of QT interval prolongation. Alternans in time and space were characterized by performing restitution pacing protocols. Comparing APs, HCQ prolongs APD during phase-III repolarization, resulting in a higher triangulation ratio than AZM alone or AZM combined with HCQ. Lower triangulation ratios with AZM are associated with phase-II prolongation, lower arrhythmia, and lower incidence of spatially discordant alternans.</p>","PeriodicalId":72683,"journal":{"name":"Computing in cardiology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9228587/pdf/nihms-1815769.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40401123","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unimapper: An Online Interactive Analyzer/Visualizer of Optical Mapping Experimental Data.","authors":"Shahriar Iravanian,&nbsp;Ilija Uzelac,&nbsp;Darby I Cairns,&nbsp;Elizabeth M Cherry,&nbsp;Abouzar Kaboudian,&nbsp;Flavio H Fenton","doi":"10.23919/cinc53138.2021.9662942","DOIUrl":"https://doi.org/10.23919/cinc53138.2021.9662942","url":null,"abstract":"<p><p>Time series of spatially-extended two-dimensional recordings are the cornerstone of basic and clinical cardiac electrophysiology. The data source may be either multipolar catheters, multi-electrode arrays, optical mapping with the help of voltage and calcium-sensitive fluorescent dyes, or the output of simulation studies. The resulting data cubes (usually two spatial and one temporal dimension) are shared either as movie files or, after additional processing, various graphs and tables. However, such data products can only convey a limited view of the data. It will be beneficial if the data consumers can interactively process the data, explore different processing options and change its visualization. This paper presents the Unified Electrophysiology Mapping Framework (Unimapper) to facilitate the exchange of electrophysiology data. Its pedigree includes a Java-based optical mapping application. The core of Unimapper is a website and a collection of JavaScript utility functions for data import and visualization (including multi-channel visualization for simultaneous voltage/calcium mapping), basic image processing (e.g., smoothing), basic signal processing (e.g., signal detrending), and advanced processing (e.g., phase calculation using the Hilbert transform). Additionally, Unimapper can optionally use graphics processing units (GPUs) for computationally intensive operations. The Unimapper ecosystem also includes utility libraries for commonly used scientific programming languages (MATLAB, Python, and Julia) that allow the data producers to convert images and recorded signals into a standard format readable by Unimapper. Unimapper can act as a nexus to share electrophysiology data - whether recorded experimentally, clinically or generated by simulation - and enhance communication and collaboration among researchers.</p>","PeriodicalId":72683,"journal":{"name":"Computing in cardiology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9228589/pdf/nihms-1815725.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40401126","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interactive 3D Human Heart Simulations on Segmented Human MRI Hearts.","authors":"John P Berman,&nbsp;Abouzar Kaboudian,&nbsp;Ilija Uzelac,&nbsp;Shahriar Iravanian,&nbsp;Tinen Iles,&nbsp;Paul A Iaizzo,&nbsp;Hyunkyung Lim,&nbsp;Scott Smolka,&nbsp;James Glimm,&nbsp;Elizabeth M Cherry,&nbsp;Flavio H Fenton","doi":"10.23919/cinc53138.2021.9662948","DOIUrl":"https://doi.org/10.23919/cinc53138.2021.9662948","url":null,"abstract":"Understanding cardiac arrhythmic mechanisms and developing new strategies to control and terminate them using computer simulations requires realistic physiological cell models with anatomically accurate heart structures. Furthermore, numerical simulations must be fast enough to study and validate model and structure parameters. Here, we present an interactive parallel approach for solving detailed cell dynamics in high-resolution human heart structures with a local PC's GPU. In vitro human heart MRI scans were manually segmented to produce 3D structures with anatomically realistic electrophysiology. The Abubu.js library was used to create an interactive code to solve the OVVR human ventricular cell model and the FDA extension of the model in the human MRI heart structures, allowing the simulation of reentrant waves and investigation of their dynamics in real time. Interactive simulations of a physiological cell model in a detailed anatomical human heart reveals propagation of waves through the fine structures of the trabeculae and pectinate muscle that can perpetuate arrhythmias, thereby giving new insights into effects that may need to be considered when planning ablation and other defibrillation methods.","PeriodicalId":72683,"journal":{"name":"Computing in cardiology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9228622/pdf/nihms-1815722.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40401127","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Network-based Cardiac Electrophysiology Simulator with Realistic Signal Generation and Response to Pacing Maneuvers.","authors":"Shahriar Iravanian,&nbsp;Ilija Uzelac,&nbsp;Abouzar Kaboudian,&nbsp;Jonathan Langberg,&nbsp;Flavio Fenton","doi":"10.23919/cinc53138.2021.9662834","DOIUrl":"https://doi.org/10.23919/cinc53138.2021.9662834","url":null,"abstract":"<p><p>Diagnosis and localization of cardiac arrhythmias, especially supraventricular tachycardia (SVT), by inspecting intracardiac signals and performing pacing maneuvers is the core of electrophysiology studies. Acquiring and maintaining complex skill sets can be facilitated by using simulators, allowing the operator to practice in a safe and controlled setting. An electrophysiology simulator should not only display arrhythmias but it has to respond to the user's arbitrary inputs. While, in principle, it is possible to model the heart using a detailed anatomical and cellular model, such a system would be unduly complex and computationally intensive. In this paper, we describe a freely available web-based electrophysiology simulator (http://svtsim.com), which is composed of a visualization/interface unit and a heart model based on a dynamical network. In the network, nodes represent the points of interest, such as the sinus and the atrioventricular nodes, and links model the conduction system and pathways. The dynamics are encoded explicitly in the state machines attached to the nodes and links. Simulated intracardiac signals and surface ECGs are generated from the internal state of the heart model. Reentrant tachycardias, especially various forms of SVT, can emerge in this system in response to the user's actions in the form of pacing maneuvers. Additionally, the resulting arrhythmias respond realistically to various inputs, such as overdrive pacing and delivery of extra stimuli, cardioversion, ablation, and infusion of medications. For nearly a decade, svtsim.com has been used successfully to train electrophysiology practitioners in many institutions. We will present our experience regarding best practices in designing and using electrophysiology simulators for training and testing. We will also discuss the current trends in clinical cardiac electrophysiology and the anticipated next generation electrophysiology simulators.</p>","PeriodicalId":72683,"journal":{"name":"Computing in cardiology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9228610/pdf/nihms-1815723.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40401122","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interactive Simulation of the ECG: Effects of Cell Types, Distributions, Shapes and Duration.","authors":"Jorge Ramirez Ortiz,&nbsp;Abouzar Kaboudian,&nbsp;Ilija Uzelac,&nbsp;Shahriar Iravanian,&nbsp;Elizabeth M Cherry,&nbsp;Flavio H Fenton","doi":"10.23919/cinc53138.2021.9662928","DOIUrl":"https://doi.org/10.23919/cinc53138.2021.9662928","url":null,"abstract":"<p><p>The shape of the ECG depends on the lead positions but also on the distribution and dispersion of different cell types and their action potential (AP) durations and shapes. We present an interactive JavaScript program that allows fast simulations of the ECG by solving and displaying the dynamics of cardiac cells in tissue using a web browser. We use physiologically accurate ODE models of cardiac cells of different types including SA node, right and left atria, AV node, Purkinje, and right and left ventricular cells with dispersion that accounts for apex-to-base and epi-to-endo variations. The software allows for real-time variations for each cell type and their spatial range so as to identify how the shape of the ECG varies as a function of cell type, distribution, excitation duration and AP shape. The propagation of the wave is visualized in real time through all the regions as parameters are kept fixed or varied to modify ECG morphology. The code solves thousands of simulated cells in real time and is independent of operating system, so it can run on PCs, laptops, tablets and cellphones. This program can be used to teach students, fellows and the general public how and why lead positions and the different cell physiology in the heart affect the various features of the ECG.</p>","PeriodicalId":72683,"journal":{"name":"Computing in cardiology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9228611/pdf/nihms-1815724.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40401125","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Real-Time Interactive Simulations of Complex Ionic Cardiac Cell Models in 2D and 3D Heart Structures with GPUs on Personal Computers.","authors":"Abouzar Kaboudian,&nbsp;Elizabeth M Cherry,&nbsp;Flavio H Fenton","doi":"10.23919/cinc53138.2021.9662759","DOIUrl":"https://doi.org/10.23919/cinc53138.2021.9662759","url":null,"abstract":"<p><strong>Aims: </strong>Cardiac modeling in heart structures for the study of arrhythmia mechanisms requires the use of software that runs on supercomputers. Therefore, computational studies are limited to groups with access to computer clusters and personnel with high-performance computing experience. We present how to use and implement WebGL programs via a custom-written library to run and visualize simulations of the most complex ionic models in 2D and 3D, in real time, interactively using the multi-core GPU of a single computer.</p><p><strong>Methods: </strong>We use Abubu.js, a library we developed for solving partial differential equations such as those describing crystal growth and fluid flow, along with a newly implemented visualization algorithm, to simulate complex ionic cell models. By combining this library with JavaScript, we allow direct real-time interactions with simulations. We implemented: 1) modification of any model parameters and equations at any time, with direct access to the code while it runs, 2) electrode stimulation anywhere in the 2D/3D tissue with a mouse click, 3) saving the solution of the system at any time to re-initiate the dynamics from saved initial conditions, and 4) rotation/visualization of 3D structures at any angle.</p><p><strong>Results: </strong>As examples of this modeling platform, we implemented a phenomenological cell model and the human ventricular OVVR model (41 variables). In 2D we illustrate the dynamics in an annulus, disk, and square tissue; in 2D and 3D porcine ventricles, we show the initiation of functional/anatomical reentry, spiral wave dynamics in different regimes, initiation of early afterdepolarizations (EADs), and the effects of model parameter variations in real time.</p><p><strong>Conclusions: </strong>We present the first simulations of complex models in anatomical structures with enhanced visualization and extended interactivity that run on a single PC, without software downloads, and as fast as in real-time even for 3D full ventricles.</p>","PeriodicalId":72683,"journal":{"name":"Computing in cardiology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9228612/pdf/nihms-1815727.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40401121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}