1
|
Bergquist JA, Zenger B, Rupp LC, Busatto A, Tate J, Brooks DH, Narayan A, MacLeod RS. Uncertainty quantification of the effect of cardiac position variability in the inverse problem of electrocardiographic imaging. Physiol Meas 2023; 44:105003. [PMID: 37734339 DOI: 10.1088/1361-6579/acfc32] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Accepted: 09/21/2023] [Indexed: 09/23/2023]
Abstract
Objective.Electrocardiographic imaging (ECGI) is a functional imaging modality that consists of two related problems, the forward problem of reconstructing body surface electrical signals given cardiac bioelectric activity, and the inverse problem of reconstructing cardiac bioelectric activity given measured body surface signals. ECGI relies on a model for how the heart generates bioelectric signals which is subject to variability in inputs. The study of how uncertainty in model inputs affects the model output is known as uncertainty quantification (UQ). This study establishes develops, and characterizes the application of UQ to ECGI.Approach.We establish two formulations for applying UQ to ECGI: a polynomial chaos expansion (PCE) based parametric UQ formulation (PCE-UQ formulation), and a novel UQ-aware inverse formulation which leverages our previously established 'joint-inverse' formulation (UQ joint-inverse formulation). We apply these to evaluate the effect of uncertainty in the heart position on the ECGI solutions across a range of ECGI datasets.Main results.We demonstrated the ability of our UQ-ECGI formulations to characterize the effect of parameter uncertainty on the ECGI inverse problem. We found that while the PCE-UQ inverse solution provided more complex outputs such as sensitivities and standard deviation, the UQ joint-inverse solution provided a more interpretable output in the form of a single ECGI solution. We find that between these two methods we are able to assess a wide range of effects that heart position variability has on the ECGI solution.Significance.This study, for the first time, characterizes in detail the application of UQ to the ECGI inverse problem. We demonstrated how UQ can provide insight into the behavior of ECGI using variability in cardiac position as a test case. This study lays the groundwork for future development of UQ-ECGI studies, as well as future development of ECGI formulations which are robust to input parameter variability.
Collapse
Affiliation(s)
- Jake A Bergquist
- Scientific Computing and Imaging Institute, University of Utah, SLC, UT, United States of America
- Nora Eccles Harrison Cardiovascular Research and Training Institute, University of Utah, SLC, UT, United States of America
- Department of Biomedical Engineering, University of Utah, SLC, UT, United States of America
| | - Brian Zenger
- Scientific Computing and Imaging Institute, University of Utah, SLC, UT, United States of America
- Nora Eccles Harrison Cardiovascular Research and Training Institute, University of Utah, SLC, UT, United States of America
- Department of Biomedical Engineering, University of Utah, SLC, UT, United States of America
- School of Medicine, University of Utah, SLC, UT, United States of America
| | - Lindsay C Rupp
- Scientific Computing and Imaging Institute, University of Utah, SLC, UT, United States of America
- Nora Eccles Harrison Cardiovascular Research and Training Institute, University of Utah, SLC, UT, United States of America
- Department of Biomedical Engineering, University of Utah, SLC, UT, United States of America
| | - Anna Busatto
- Scientific Computing and Imaging Institute, University of Utah, SLC, UT, United States of America
- Nora Eccles Harrison Cardiovascular Research and Training Institute, University of Utah, SLC, UT, United States of America
- Department of Biomedical Engineering, University of Utah, SLC, UT, United States of America
| | - Jess Tate
- Scientific Computing and Imaging Institute, University of Utah, SLC, UT, United States of America
| | - Dana H Brooks
- Department of Electrical and Computer Engineering, Northeastern University, United States of America
| | - Akil Narayan
- Scientific Computing and Imaging Institute, University of Utah, SLC, UT, United States of America
- Department of Mathematics, University of Utah, SLC, UT, United States of America
| | - Rob S MacLeod
- Scientific Computing and Imaging Institute, University of Utah, SLC, UT, United States of America
- Nora Eccles Harrison Cardiovascular Research and Training Institute, University of Utah, SLC, UT, United States of America
- Department of Biomedical Engineering, University of Utah, SLC, UT, United States of America
| |
Collapse
|
2
|
Zenger B, Bergquist JA, Busatto A, Good WW, Rupp LC, Sharma V, MacLeod RS. Tipping the scales of understanding: An engineering approach to design and implement whole-body cardiac electrophysiology experimental models. Front Physiol 2023; 14:1100471. [PMID: 36744034 PMCID: PMC9893785 DOI: 10.3389/fphys.2023.1100471] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Accepted: 01/02/2023] [Indexed: 01/21/2023] Open
Abstract
The study of cardiac electrophysiology is built on experimental models that span all scales, from ion channels to whole-body preparations. Novel discoveries made at each scale have contributed to our fundamental understanding of human cardiac electrophysiology, which informs clinicians as they detect, diagnose, and treat complex cardiac pathologies. This expert review describes an engineering approach to developing experimental models that is applicable across scales. The review also outlines how we applied the approach to create a set of multiscale whole-body experimental models of cardiac electrophysiology, models that are driving new insights into the response of the myocardium to acute ischemia. Specifically, we propose that researchers must address three critical requirements to develop an effective experimental model: 1) how the experimental model replicates and maintains human physiological conditions, 2) how the interventions possible with the experimental model capture human pathophysiology, and 3) what signals need to be measured, at which levels of resolution and fidelity, and what are the resulting requirements of the measurement system and the access to the organs of interest. We will discuss these requirements in the context of two examples of whole-body experimental models, a closed chest in situ model of cardiac ischemia and an isolated-heart, torso-tank preparation, both of which we have developed over decades and used to gather valuable insights from hundreds of experiments.
Collapse
Affiliation(s)
- Brian Zenger
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, United States,Nora Eccles Harrison Cardiovascular Research and Training Institute, The University of Utah, Salt Lake City, UT, United States,Spencer Eccles School of Medicine, University of Utah, Salt Lake City, UT, United States,*Correspondence: Brian Zenger,
| | - Jake A. Bergquist
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, United States,Nora Eccles Harrison Cardiovascular Research and Training Institute, The University of Utah, Salt Lake City, UT, United States,Department of Biomedical Engineering, College of Engineering, University of Utah, Salt Lake City, UT, United States
| | - Anna Busatto
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, United States,Nora Eccles Harrison Cardiovascular Research and Training Institute, The University of Utah, Salt Lake City, UT, United States,Department of Biomedical Engineering, College of Engineering, University of Utah, Salt Lake City, UT, United States
| | | | - Lindsay C. Rupp
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, United States,Nora Eccles Harrison Cardiovascular Research and Training Institute, The University of Utah, Salt Lake City, UT, United States,Department of Biomedical Engineering, College of Engineering, University of Utah, Salt Lake City, UT, United States
| | - Vikas Sharma
- Spencer Eccles School of Medicine, University of Utah, Salt Lake City, UT, United States
| | - Rob S. MacLeod
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, United States,Nora Eccles Harrison Cardiovascular Research and Training Institute, The University of Utah, Salt Lake City, UT, United States,Department of Biomedical Engineering, College of Engineering, University of Utah, Salt Lake City, UT, United States
| |
Collapse
|
3
|
Supine versus sitting upright position electrocardiography in acute patients with chest pain. Eur J Emerg Med 2022; 29:152-153. [PMID: 35210383 DOI: 10.1097/mej.0000000000000874] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
|
4
|
Bergquist JA, Coll-Font J, Zenger B, Rupp LC, Good WW, Brooks DH, MacLeod RS. Reconstruction of cardiac position using body surface potentials. Comput Biol Med 2022; 142:105174. [DOI: 10.1016/j.compbiomed.2021.105174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Revised: 12/22/2021] [Accepted: 12/23/2021] [Indexed: 11/03/2022]
|
5
|
Gyawali PK, Murkute JV, Toloubidokhti M, Jiang X, Horacek BM, Sapp JL, Wang L. Learning to Disentangle Inter-Subject Anatomical Variations in Electrocardiographic Data. IEEE Trans Biomed Eng 2022; 69:860-870. [PMID: 34460360 PMCID: PMC8858595 DOI: 10.1109/tbme.2021.3108164] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
OBJECTIVE This work investigates the possibility of disentangled representation learning of inter-subject anatomical variations within electrocardiographic (ECG) data. METHODS Since ground truth anatomical factors are generally not known in clinical ECG for assessing the disentangling ability of the models, the presented work first proposes the SimECG data set, a 12-lead ECG data set procedurally generated with a controlled set of anatomical generative factors. Second, to perform such disentanglement, the presented method evaluates and compares deep generative models with latent density modeled by nonparametric Indian Buffet Process to account for the complex generative process of ECG data. RESULTS In the simulated data, the experiments demonstrate, for the first time, concrete evidence of the possibility to disentangle key generative anatomical factors within ECG data in separation from task-relevant generative factors. We achieve a disentanglement score of 92.1% while disentangling five anatomical generative factors and the task-relevant generative factor. In both simulated and real-data experiments, this work further provides quantitative evidence for the benefit of disentanglement learning on the downstream clinical task of localizing the origin of ventricular activation. Overall, the presented method achieves an improvement of around 18.5%, and 11.3% for the simulated dataset, and around 7.2%, and 3.6% for the real dataset, over baseline CNN, and standard generative model, respectively. CONCLUSION These results demonstrate the importance as well as the feasibility of the disentangled representation learning of inter-subject anatomical variations within ECG data. SIGNIFICANCE This work suggests the important research direction to deal with the well-known challenge posed by the presence of significant inter-subject variations during an automated analysis of ECG data.
Collapse
|
6
|
Bergquist J, Rupp L, Zenger B, Brundage J, Busatto A, MacLeod RS. Body Surface Potential Mapping: Contemporary Applications and Future Perspectives. HEARTS 2021; 2:514-542. [PMID: 35665072 PMCID: PMC9164986 DOI: 10.3390/hearts2040040] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/01/2023] Open
Abstract
Body surface potential mapping (BSPM) is a noninvasive modality to assess cardiac bioelectric activity with a rich history of practical applications for both research and clinical investigation. BSPM provides comprehensive acquisition of bioelectric signals across the entire thorax, allowing for more complex and extensive analysis than the standard electrocardiogram (ECG). Despite its advantages, BSPM is not a common clinical tool. BSPM does, however, serve as a valuable research tool and as an input for other modes of analysis such as electrocardiographic imaging and, more recently, machine learning and artificial intelligence. In this report, we examine contemporary uses of BSPM, and provide an assessment of its future prospects in both clinical and research environments. We assess the state of the art of BSPM implementations and explore modern applications of advanced modeling and statistical analysis of BSPM data. We predict that BSPM will continue to be a valuable research tool, and will find clinical utility at the intersection of computational modeling approaches and artificial intelligence.
Collapse
Affiliation(s)
- Jake Bergquist
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT 84112, USA
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT 84112, USA
- Nora Eccles Harrison Cardiovascular Research and Training Institute, University of Utah, Salt Lake City, UT 84112, USA
| | - Lindsay Rupp
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT 84112, USA
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT 84112, USA
- Nora Eccles Harrison Cardiovascular Research and Training Institute, University of Utah, Salt Lake City, UT 84112, USA
| | - Brian Zenger
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT 84112, USA
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT 84112, USA
- Nora Eccles Harrison Cardiovascular Research and Training Institute, University of Utah, Salt Lake City, UT 84112, USA
- School of Medicine, University of Utah, Salt Lake City, UT 84112, USA
| | - James Brundage
- School of Medicine, University of Utah, Salt Lake City, UT 84112, USA
| | - Anna Busatto
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT 84112, USA
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT 84112, USA
| | - Rob S. MacLeod
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT 84112, USA
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT 84112, USA
- Nora Eccles Harrison Cardiovascular Research and Training Institute, University of Utah, Salt Lake City, UT 84112, USA
| |
Collapse
|
7
|
Bergquist JA, Coll-Font J, Zenger B, Rupp LC, Good WW, Brooks DH, MacLeod RS. Simultaneous Multi-Heartbeat ECGI Solution with a Time-Varying Forward Model: a Joint Inverse Formulation. FUNCTIONAL IMAGING AND MODELING OF THE HEART : ... INTERNATIONAL WORKSHOP, FIMH ..., PROCEEDINGS. FIMH 2021; 12738:493-502. [PMID: 34447971 PMCID: PMC8385662 DOI: 10.1007/978-3-030-78710-3_47] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Electrocardiographic imaging (ECGI) is an effective tool for noninvasive diagnosis of a range of cardiac dysfunctions. ECGI leverages a model of how cardiac bioelectric sources appear on the torso surface (the forward problem) and uses recorded body surface potential signals to reconstruct the bioelectric source (the inverse problem). Solutions to the inverse problem are sensitive to noise and variations in the body surface potential (BSP) recordings such as those caused by changes or errors in cardiac position. Techniques such as signal averaging seek to improve ECGI solutions by incorporating BSP signals from multiple heartbeats into an averaged BSP with a higher SNR to use when estimating the cardiac bioelectric source. However, signal averaging is limited when it comes to addressing sources of BSP variability such as beat to beat differences in the forward solution. We present a novel joint inverse formulation to solve for the cardiac source given multiple BSP recordings and known changes in the forward solution, here changes in the heart position. We report improved ECGI accuracy over signal averaging and averaged individual inverse solutions using this joint inverse formulation across multiple activation sequence types and regularization techniques with measured canine data and simulated heart motion. Our joint inverse formulation builds upon established techniques and consequently can easily be applied with many existing regularization techniques, source models, and forward problem formulations.
Collapse
Affiliation(s)
- Jake A Bergquist
- Biomedical Engineering Department, University of Utah, SLC, UT, 84112, USA
| | - Jaume Coll-Font
- Cardiovascular Bioengineering & Imaging Lab, Massachusetts General Hospital, and Harvard Medical School, Boston, MA, USA
| | - Brian Zenger
- Biomedical Engineering Department, University of Utah, SLC, UT, 84112, USA
| | - Lindsay C Rupp
- Biomedical Engineering Department, University of Utah, SLC, UT, 84112, USA
| | - Wilson W Good
- Biomedical Engineering Department, University of Utah, SLC, UT, 84112, USA
| | - Dana H Brooks
- Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, USA
| | - Rob S MacLeod
- Biomedical Engineering Department, University of Utah, SLC, UT, 84112, USA
| |
Collapse
|
8
|
Tate JD, Good W, Zemzemi N, Boonstra M, van Dam P, Brooks DH, Narayan A, MacLeod RS. Uncertainty Quantification of the Effects of Segmentation Variability in ECGI. FUNCTIONAL IMAGING AND MODELING OF THE HEART : ... INTERNATIONAL WORKSHOP, FIMH ..., PROCEEDINGS. FIMH 2021; 12738:515-522. [PMID: 35449797 PMCID: PMC9019843 DOI: 10.1007/978-3-030-78710-3_49] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Despite advances in many of the techniques used in Electrocardiographic Imaging (ECGI), uncertainty remains insufficiently quantified for many aspects of the pipeline. The effect of geometric uncertainty, particularly due to segmentation variability, may be the least explored to date. We use statistical shape modeling and uncertainty quantification (UQ) to compute the effect of segmentation variability on ECGI solutions. The shape model was made with Shapeworks from nine segmentations of the same patient and incorporated into an ECGI pipeline. We computed uncertainty of the pericardial potentials and local activation times (LATs) using polynomial chaos expansion (PCE) implemented in UncertainSCI. Uncertainty in pericardial potentials from segmentation variation mirrored areas of high variability in the shape model, near the base of the heart and the right ventricular outflow tract, and that ECGI was less sensitive to uncertainty in the posterior region of the heart. Subsequently LAT calculations could vary dramatically due to segmentation variability, with a standard deviation as high as 126ms, yet mainly in regions with low conduction velocity. Our shape modeling and UQ pipeline presented possible uncertainty in ECGI due to segmentation variability and can be used by researchers to reduce said uncertainty or mitigate its effects. The demonstrated use of statistical shape modeling and UQ can also be extended to other types of modeling pipelines.
Collapse
|
9
|
Bergquist JA, Good WW, Zenger B, Tate JD, Rupp LC, MacLeod RS. The electrocardiographic forward problem: A benchmark study. Comput Biol Med 2021; 134:104476. [PMID: 34051453 DOI: 10.1016/j.compbiomed.2021.104476] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Revised: 05/02/2021] [Accepted: 05/03/2021] [Indexed: 11/30/2022]
Abstract
BACKGROUND Electrocardiographic forward problems are crucial components for noninvasive electrocardiographic imaging (ECGI) that compute torso potentials from cardiac source measurements. Forward problems have few sources of error as they are physically well posed and supported by mature numerical and computational techniques. However, the residual errors reported from experimental validation studies between forward computed and measured torso signals remain surprisingly high. OBJECTIVE To test the hypothesis that incomplete cardiac source sampling, especially above the atrioventricular (AV) plane is a major contributor to forward solution errors. METHODS We used a modified Langendorff preparation suspended in a human-shaped electrolytic torso-tank and a novel pericardiac-cage recording array to thoroughly sample the cardiac potentials. With this carefully controlled experimental preparation, we minimized possible sources of error, including geometric error and torso inhomogeneities. We progressively removed recorded signals from above the atrioventricular plane to determine how the forward-computed torso-tank potentials were affected by incomplete source sampling. RESULTS We studied 240 beats total recorded from three different activation sequence types (sinus, and posterior and anterior left-ventricular free-wall pacing) in each of two experiments. With complete sampling by the cage electrodes, all correlation metrics between computed and measured torso-tank potentials were above 0.93 (maximum 0.99). The mean root-mean-squared error across all beat types was also low, less than or equal to 0.10 mV. A precipitous drop in forward solution accuracy was observed when we included only cage measurements below the AV plane. CONCLUSION First, our forward computed potentials using complete cardiac source measurements set a benchmark for similar studies. Second, this study validates the importance of complete cardiac source sampling above the AV plane to produce accurate forward computed torso potentials. Testing ECGI systems and techniques with these more complete and highly accurate datasets will improve inverse techniques and noninvasive detection of cardiac electrical abnormalities.
Collapse
Affiliation(s)
- Jake A Bergquist
- Scientific Computing and Imaging Institute, University of Utah, SLC, UT, USA; Nora Eccles Cardiovascular Research and Training Institute, University of Utah, SLC, UT, USA; Department of Biomedical Engineering, University of Utah, SLC, UT, USA
| | - Wilson W Good
- Scientific Computing and Imaging Institute, University of Utah, SLC, UT, USA; Nora Eccles Cardiovascular Research and Training Institute, University of Utah, SLC, UT, USA; Department of Biomedical Engineering, University of Utah, SLC, UT, USA
| | - Brian Zenger
- Scientific Computing and Imaging Institute, University of Utah, SLC, UT, USA; Nora Eccles Cardiovascular Research and Training Institute, University of Utah, SLC, UT, USA; Department of Biomedical Engineering, University of Utah, SLC, UT, USA; School of Medicine, University of Utah, SLC, UT, USA.
| | - Jess D Tate
- Scientific Computing and Imaging Institute, University of Utah, SLC, UT, USA
| | - Lindsay C Rupp
- Scientific Computing and Imaging Institute, University of Utah, SLC, UT, USA; Nora Eccles Cardiovascular Research and Training Institute, University of Utah, SLC, UT, USA; Department of Biomedical Engineering, University of Utah, SLC, UT, USA
| | - Rob S MacLeod
- Scientific Computing and Imaging Institute, University of Utah, SLC, UT, USA; Nora Eccles Cardiovascular Research and Training Institute, University of Utah, SLC, UT, USA; Department of Biomedical Engineering, University of Utah, SLC, UT, USA; School of Medicine, University of Utah, SLC, UT, USA
| |
Collapse
|
10
|
McCabe KJ, Rangamani P. Computational modeling approaches to cAMP/PKA signaling in cardiomyocytes. J Mol Cell Cardiol 2021; 154:32-40. [PMID: 33548239 DOI: 10.1016/j.yjmcc.2021.01.008] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Revised: 01/11/2021] [Accepted: 01/15/2021] [Indexed: 12/12/2022]
Abstract
The cAMP/PKA pathway is a fundamental regulator of excitation-contraction coupling in cardiomyocytes. Activation of cAMP has a variety of downstream effects on cardiac function including enhanced contraction, accelerated relaxation, adaptive stress response, mitochondrial regulation, and gene transcription. Experimental advances have shed light on the compartmentation of cAMP and PKA, which allow for control over the varied targets of these second messengers and is disrupted in heart failure conditions. Computational modeling is an important tool for understanding the spatial and temporal complexities of this system. In this review article, we outline the advances in computational modeling that have allowed for deeper understanding of cAMP/PKA dynamics in the cardiomyocyte in health and disease, and explore new modeling frameworks that may bring us closer to a more complete understanding of this system. We outline various compartmental and spatial signaling models that have been used to understand how β-adrenergic signaling pathways function in a variety of simulation conditions. We also discuss newer subcellular models of cardiovascular function that may be used as templates for the next phase of computational study of cAMP and PKA in the heart, and outline open challenges which are important to consider in future models.
Collapse
Affiliation(s)
- Kimberly J McCabe
- Simula Research Laboratory, Department of Computational Physiology, PO Box 134, 1325 Lysaker, Norway.
| | - Padmini Rangamani
- University of California San Diego, Department of Mechanical and Aerospace Engineering, 9500 Gilman Drive MC 0411, La Jolla, CA 92093, United States of America
| |
Collapse
|
11
|
Good WW, Zenger B, Bergquist JA, Rupp LC, Gillette K, Angel N, Chou D, Plank G, MacLeod RS. Combining endocardial mapping and electrocardiographic imaging (ECGI) for improving PVC localization: A feasibility study. J Electrocardiol 2021; 69S:51-54. [PMID: 34649726 PMCID: PMC9014370 DOI: 10.1016/j.jelectrocard.2021.08.013] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Revised: 08/10/2021] [Accepted: 08/13/2021] [Indexed: 12/29/2022]
Abstract
INTRODUCTION Accurate reconstruction of cardiac activation wavefronts is crucial for clinical diagnosis, management, and treatment of cardiac arrhythmias. Furthermore, reconstruction of activation profiles within the intramural myocardium has long been impossible because electrical mapping was only performed on the endocardial surface. Recent advancements in electrocardiographic imaging (ECGI) have made endocardial and epicardial activation mapping possible. We propose a novel approach to use both endocardial and epicardial mapping in a combined approach to reconstruct intramural activation times. OBJECTIVE To implement and validate a combined epicardial/endocardial intramural activation time reconstruction technique. METHODS We used 11 simulations of ventricular activation paced from sites throughout myocardial wall and extracted endocardial and epicardial activation maps at approximate clinical resolution. From these maps, we interpolated the activation times through the myocardium using thin-plate-spline radial basis functions. We evaluated activation time reconstruction accuracy using root-mean-squared error (RMSE) of activation times and the percent of nodes within 1 ms of the ground truth. RESULTS Reconstructed intramural activation times showed an RMSE and percentage of nodes within 1 ms of the ground truth simulations of 3 ms and 70%, respectively. In the worst case, the RMSE and percentage of nodes were 4 ms and 60%, respectively. CONCLUSION We showed that a simple, yet effective combination of clinical endocardial and epicardial activation maps can accurately reconstruct intramural wavefronts. Furthermore, we showed that this approach provided robust reconstructions across multiple intramural stimulation sites.
Collapse
Affiliation(s)
- Wilson W. Good
- Scientific Computing and Imaging Institute, University of Utah, SLC, UT, USA,Department of Biomedical Engineering, University of Utah, SLC, UT, USA,Nora Eccles Cardiovascular Research and Training Institute, University of Utah, SLC, UT, USA,Acutus Medical, Carlsbad, CA, USA
| | - Brian Zenger
- Scientific Computing and Imaging Institute, University of Utah, SLC, UT, USA,Department of Biomedical Engineering, University of Utah, SLC, UT, USA,Nora Eccles Cardiovascular Research and Training Institute, University of Utah, SLC, UT, USA
| | - Jake A. Bergquist
- Scientific Computing and Imaging Institute, University of Utah, SLC, UT, USA,Department of Biomedical Engineering, University of Utah, SLC, UT, USA,Nora Eccles Cardiovascular Research and Training Institute, University of Utah, SLC, UT, USA
| | - Lindsay C. Rupp
- Scientific Computing and Imaging Institute, University of Utah, SLC, UT, USA,Department of Biomedical Engineering, University of Utah, SLC, UT, USA,Nora Eccles Cardiovascular Research and Training Institute, University of Utah, SLC, UT, USA
| | - Karli Gillette
- Institute of Biophysics, Medical University of Graz, Graz, Austria
| | | | | | - Gernot Plank
- Institute of Biophysics, Medical University of Graz, Graz, Austria
| | - Rob S. MacLeod
- Scientific Computing and Imaging Institute, University of Utah, SLC, UT, USA,Department of Biomedical Engineering, University of Utah, SLC, UT, USA,Nora Eccles Cardiovascular Research and Training Institute, University of Utah, SLC, UT, USA
| |
Collapse
|
12
|
Bergquist JA, Coll-Font J, Zenger B, Rupp LC, Good WW, Brooks DH, MacLeod RS. Improving Localization of Cardiac Geometry Using ECGI. COMPUTING IN CARDIOLOGY 2020; 47:10.22489/cinc.2020.273. [PMID: 33937429 PMCID: PMC8082332 DOI: 10.22489/cinc.2020.273] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
INTRODUCTION Electrocardiographic imaging (ECGI) requires a model of the torso, and inaccuracy in the position of the heart is a known source of error. We previously presented a method to localize the heart when body and heart surface potentials are known. The goal of this study is to extend this approach to only use body surface potentials. METHODS We used an iterative coordinate descent optimization to estimate the positions of the heart for several consecutive heartbeats relying on the assumption that the epicardial potential sequence is the same in each beat. The method was tested with data synthesized using measurements from a isolated-heart, torso-tank preparation. Improvement was evaluated in terms of both heart localization and ECGI accuracy. RESULTS The geometric correction resulted in cardiac geometries closely matching ground truth geometry. ECGI accuracy increased dramatically by all metrics using the corrected geometry. DISCUSSION Future studies will employ more realistic animal models and then human subjects. Success could impact clinical ECGI by reducing errors from respiratory movement and perhaps decrease imaging requirements, reducing both cost and logistical difficulty of ECGI, widening clinical applicability.
Collapse
Affiliation(s)
- Jake A Bergquist
- Scientific Computing and Imaging Institute, University of Utah, SLC, UT, USA
- Nora Eccles Cardiovascular Research and Training Institute, University of Utah, SLC, UT, USA
- Department of Biomedical Engineering, University of Utah, SLC, UT, USA
| | - Jaume Coll-Font
- Cardiovascular Bioengineering & Imaging (CBM) Lab at the Massachusetts General Hospital, Boston (MA) and Harvard Medical School, Boston, MA, USA
| | - Brian Zenger
- Scientific Computing and Imaging Institute, University of Utah, SLC, UT, USA
- Nora Eccles Cardiovascular Research and Training Institute, University of Utah, SLC, UT, USA
- Department of Biomedical Engineering, University of Utah, SLC, UT, USA
- School of Medicine, University of Utah, SLC, UT, USA
| | - Lindsay C Rupp
- Scientific Computing and Imaging Institute, University of Utah, SLC, UT, USA
- Nora Eccles Cardiovascular Research and Training Institute, University of Utah, SLC, UT, USA
- Department of Biomedical Engineering, University of Utah, SLC, UT, USA
| | - Wilson W Good
- Scientific Computing and Imaging Institute, University of Utah, SLC, UT, USA
- Nora Eccles Cardiovascular Research and Training Institute, University of Utah, SLC, UT, USA
- Department of Biomedical Engineering, University of Utah, SLC, UT, USA
| | - Dana H Brooks
- Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, USA
| | - Rob S MacLeod
- Scientific Computing and Imaging Institute, University of Utah, SLC, UT, USA
- Nora Eccles Cardiovascular Research and Training Institute, University of Utah, SLC, UT, USA
- Department of Biomedical Engineering, University of Utah, SLC, UT, USA
| |
Collapse
|
13
|
Rupp LC, Liu Z, Bergquist JA, Rampersad S, White D, Tate JD, Brooks DH, Narayan A, MacLeod RS. Using UncertainSCI to Quantify Uncertainty in Cardiac Simulations. COMPUTING IN CARDIOLOGY 2020; 47:10.22489/cinc.2020.275. [PMID: 36845870 PMCID: PMC9956381 DOI: 10.22489/cinc.2020.275] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Cardiac simulations have become increasingly accurate at representing physiological processes. However, simulations often fail to capture the impact of parameter uncertainty in predictions. Uncertainty quantification (UQ) is a set of techniques that captures variability in simulation output based on model assumptions. Although many UQ methods exist, practical implementation can be challenging. We created UncertainSCI, a UQ framework that uses polynomial chaos (PC) expansion to model the forward stochastic error in simulations parameterized with random variables. UncertainSCI uses non-intrusive methods that parsimoniously explores parameter space. The result is an efficient, stable, and accurate PC emulator that can be analyzed to compute output statistics. We created a Python API to run UncertainSCI, minimizing user inputs needed to guide the UQ process. We have implemented UncertainSCI to: (1) quantify the sensitivity of computed torso potentials using the boundary element method to uncertainty in the heart position, and (2) quantify the sensitivity of computed torso potentials using the finite element method to uncertainty in the conductivities of biological tissues. With UncertainSCI, it is possible to evaluate the robustness of simulations to parameter uncertainty and establish realistic expectations on the accuracy of the model results and the clinical guidance they can provide.
Collapse
Affiliation(s)
- Lindsay C Rupp
- Scientific Computing and Imaging Institute, University of Utah, SLC, UT, USA.,Nora Eccles Cardiovascular Research and Training Institute, University of Utah, SLC, UT, USA.,Department of Biomedical Engineering, University of Utah, SLC, UT, USA
| | - Zexin Liu
- Scientific Computing and Imaging Institute, University of Utah, SLC, UT, USA.,Department of Mathematics, University of Utah, SLC, UT, USA
| | - Jake A Bergquist
- Scientific Computing and Imaging Institute, University of Utah, SLC, UT, USA.,Nora Eccles Cardiovascular Research and Training Institute, University of Utah, SLC, UT, USA.,Department of Biomedical Engineering, University of Utah, SLC, UT, USA
| | - Sumientra Rampersad
- Department of Electrical and Computer Engineering, Northeastern, BOS, MA, USA
| | - Dan White
- Scientific Computing and Imaging Institute, University of Utah, SLC, UT, USA
| | - Jess D Tate
- Scientific Computing and Imaging Institute, University of Utah, SLC, UT, USA.,Nora Eccles Cardiovascular Research and Training Institute, University of Utah, SLC, UT, USA.,Department of Biomedical Engineering, University of Utah, SLC, UT, USA
| | - Dana H Brooks
- Department of Electrical and Computer Engineering, Northeastern, BOS, MA, USA
| | - Akil Narayan
- Scientific Computing and Imaging Institute, University of Utah, SLC, UT, USA.,Department of Mathematics, University of Utah, SLC, UT, USA
| | - Rob S MacLeod
- Scientific Computing and Imaging Institute, University of Utah, SLC, UT, USA.,Nora Eccles Cardiovascular Research and Training Institute, University of Utah, SLC, UT, USA.,Department of Biomedical Engineering, University of Utah, SLC, UT, USA
| |
Collapse
|
14
|
Tate JD, Pilcher TA, Aras KK, Burton BM, MacLeod RS. Validating defibrillation simulation in a human-shaped phantom. Heart Rhythm 2019; 17:661-668. [PMID: 31765807 DOI: 10.1016/j.hrthm.2019.11.020] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/14/2018] [Indexed: 01/23/2023]
Abstract
BACKGROUND We previously developed a computational model to aid clinicians in positioning implantable cardioverter-defibrillators (ICDs), especially in the case of abnormal anatomies that commonly arise in pediatric cases. We have validated the model clinically on the body surface; however, validation within the volume of the heart is required to establish complete confidence in the model and improve its use in clinical settings. OBJECTIVE The goal of this study was to use an animal model and thoracic phantom to record the ICD potential field within the heart and on the torso to validate our defibrillation simulation system. METHODS We recorded defibrillator shock potentials from an ICD suspended together with an animal heart in a human-shaped torso tank and compared them with simulated values. We also compared the scaled distribution threshold, an analog to the defibrillation threshold, calculated from the measured and simulated electric fields within the myocardium. RESULTS ICD potentials recorded on the tank and cardiac surface and within the myocardium agreed well with those predicted by the simulation. A quantitative comparison of the recorded and simulated potentials yielded a mean correlation of 0.94 and a relative error of 19.1%. The simulation can also predict scaled distribution thresholds similar to those calculated from the measured potential fields. CONCLUSION We found that our simulation could predict potential fields with high correlation with the measured values within the heart and on the torso surface. These results support the use of this model for the optimization of ICD placements.
Collapse
Affiliation(s)
- Jess D Tate
- Department of Biomedical Engineering, University of Utah, Salt Lake City, Utah; Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, Utah.
| | - Thomas A Pilcher
- Division of Pediatric Cardiology, University of Utah, Salt Lake City, Utah
| | - Kedar K Aras
- Department of Biomedical Engineering, University of Utah, Salt Lake City, Utah; Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, Utah
| | - Brett M Burton
- Department of Biomedical Engineering, University of Utah, Salt Lake City, Utah; Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, Utah
| | - Rob S MacLeod
- Department of Biomedical Engineering, University of Utah, Salt Lake City, Utah; Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, Utah
| |
Collapse
|
15
|
Tate JD, Schuler S, Dössel O, MacLeod RS, Oostendorp TF. Correcting Undersampled Cardiac Sources in Equivalent Double Layer Forward Simulations. FUNCTIONAL IMAGING AND MODELING OF THE HEART : ... INTERNATIONAL WORKSHOP, FIMH ..., PROCEEDINGS. FIMH 2019; 11504:147-155. [PMID: 31799513 DOI: 10.1007/978-3-030-21949-9_17] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Electrocardiographic Imaging (ECGI) requires robust ECG forward simulations to accurately calculate cardiac activity. However, many questions remain regarding ECG forward simulations, for instance: there are not common guidelines for the required cardiac source sampling. In this study we test equivalent double layer (EDL) forward simulations with differing cardiac source resolutions and different spatial interpolation techniques. The goal is to reduce error caused by undersampling of cardiac sources and provide guidelines to reduce said source undersampling in ECG forward simulations. Using a simulated dataset sampled at 5 spatial resolutions, we computed body surface potentials using an EDL forward simulation pipeline. We tested two spatial interpolation methods to reduce error due to undersampling triangle weighting and triangle splitting. This forward modeling pipeline showed high frequency artifacts in the predicted ECG time signals when the cardiac source resolution was too low. These low resolutions could also cause shifts in extrema location on the body surface maps. However, these errors in predicted potentials can be mitigated by using a spatial interpolation method. Using spatial interpolation can reduce the number of nodes required for accurate body surface potentials from 9,218 to 2,306. Spatial interpolation in this forward model could also help improve accuracy and reduce computational cost in subsequent ECGI applications.
Collapse
Affiliation(s)
- Jess D Tate
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, USA
| | | | - Olaf Dössel
- Institute of Biomed. Eng., KIT, Karlsruhe, Germany
| | - Rob S MacLeod
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, USA
| | - Thom F Oostendorp
- Donders Centre for Neuroscience, Radboud University, Nijmegen, Netherlands
| |
Collapse
|
16
|
Rodríguez‐Cantano R, Sundnes J, Rognes ME. Uncertainty in cardiac myofiber orientation and stiffnesses dominate the variability of left ventricle deformation response. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2019; 35:e3178. [PMID: 30632711 PMCID: PMC6618163 DOI: 10.1002/cnm.3178] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/08/2018] [Accepted: 12/15/2018] [Indexed: 05/26/2023]
Abstract
Computational cardiac modelling is a mature area of biomedical computing and is currently evolving from a pure research tool to aiding in clinical decision making. Assessing the reliability of computational model predictions is a key factor for clinical use, and uncertainty quantification (UQ) and sensitivity analysis are important parts of such an assessment. In this study, we apply UQ in computational heart mechanics to study uncertainty both in material parameters characterizing global myocardial stiffness and in the local muscle fiber orientation that governs tissue anisotropy. The uncertainty analysis is performed using the polynomial chaos expansion (PCE) method, which is a nonintrusive meta-modeling technique that surrogates the original computational model with a series of orthonormal polynomials over the random input parameter space. In addition, in order to study variability in the muscle fiber architecture, we model the uncertainty in orientation of the fiber field as an approximated random field using a truncated Karhunen-Loéve expansion. The results from the UQ and sensitivity analysis identify clear differences in the impact of various material parameters on global output quantities. Furthermore, our analysis of random field variations in the fiber architecture demonstrate a substantial impact of fiber angle variations on the selected outputs, highlighting the need for accurate assignment of fiber orientation in computational heart mechanics models.
Collapse
Affiliation(s)
- Rocío Rodríguez‐Cantano
- Department of Numerical Analysis and Scientific ComputingSimula Research Laboratory ASBærumNorway
| | - Joakim Sundnes
- Center for Cardiological InnovationSimula Research LaboratoryBærumNorway
| | - Marie E. Rognes
- Department of Numerical Analysis and Scientific ComputingSimula Research Laboratory ASBærumNorway
| |
Collapse
|
17
|
Perez Alday EA, Whittaker DG, Benson AP, Colman MA. Effects of Heart Rate and Ventricular Wall Thickness on Non-invasive Mapping: An in silico Study. Front Physiol 2019; 10:308. [PMID: 31024330 PMCID: PMC6460935 DOI: 10.3389/fphys.2019.00308] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2018] [Accepted: 03/07/2019] [Indexed: 01/08/2023] Open
Abstract
Background: Non-invasive cardiac mapping—also known as Electrocardiographic imaging (ECGi)—is a novel, painless and relatively economic method to map the electrical activation and repolarization patterns of the heart, providing a valuable tool for early identification and diagnosis of conduction abnormalities and arrhythmias. Moreover, the ability to obtain information on cardiac electrical activity non-invasively using ECGi provides the potential for a priori information to guide invasive surgical procedures, improving success rates, and reducing procedure time. Previous studies have shown the influence of clinical variables, such as heart rate, heart size, endocardial wall, and body composition on surface electrocardiogram (ECG) measurements. The influence of clinical variables on the ECG variability has provided information on cardiovascular control and its abnormalities in various pathologies. However, the effects of such clinical variables on the Body Surface Potential (BSP) and ECGi maps have yet to be systematically investigated. Methods: In this study we investigated the effects of heart size, intracardiac thickness, and heart rate on BSP and ECGi maps using a previously-developed 3D electrophysiologically-detailed ventricles-torso model. The inverse solution was solved using the three different Tikhonov regularization methods. Results: Through comparison of multiple measures of error/accuracy on the ECGi reconstructions, our results showed that using different heart geometries to solve the forward and inverse problems produced a larger estimated focal excitation location. An increase of ~2 mm in the Euclidean distance error was observed for an increase in the heart size. However, the estimation of the location of focal activity was still able to be obtained. Similarly, a Euclidean distance increase was observed when the order of regularization was reduced. For the case of activation maps reconstructed at the same ectopic focus location but different heart rates, an increase in the errors and Euclidean distance was observed when the heart rate was increased. Conclusions: Non-invasive cardiac mapping can still provide useful information about cardiac activation patterns for the cases when a different geometry is used for the inverse problem compared to the one used for the forward solution; rapid pacing rates can induce order-dependent errors in the accuracy of reconstruction.
Collapse
Affiliation(s)
- Erick Andres Perez Alday
- Knight Cardiovascular Institute, Oregon Health & Science University, Portland, OR, United States
| | - Dominic G Whittaker
- School of Biomedical Science and Multidisciplinary Cardiovascular Research Centre, University of Leeds, Leeds, United Kingdom
| | - Alan P Benson
- School of Biomedical Science and Multidisciplinary Cardiovascular Research Centre, University of Leeds, Leeds, United Kingdom
| | - Michael A Colman
- School of Biomedical Science and Multidisciplinary Cardiovascular Research Centre, University of Leeds, Leeds, United Kingdom
| |
Collapse
|
18
|
Tate JD, Zemzemi N, Good WW, van Dam P, Brooks DH, MacLeod RS. Effect of Segmentation Variation on ECG Imaging. COMPUTING IN CARDIOLOGY 2018; 45. [PMID: 31632991 DOI: 10.22489/cinc.2018.374] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
ECG imaging (ECGI) is the process of calculating electrical cardiac activity from body surface recordings from the geometry and conductivity of the torso volume. A key first step to create geometric models for ECGI and a possible source of considerable variability is to segment the surface of the heart. We hypothesize that this variation in cardiac segmentation will produce variation in the computed ventricular surface potentials from ECGI. To evaluate this hypothesis, we leveraged the resources of the Consortium for ECG Imaging (CEI) to carry out a comparison of ECGI results from the same body surface potentials and multiple ventricular segmentations. We found that using the different segmentations produced variability in the computed ventricular surface potentials. Not surprisingly, locations of greater variance in the computed potential correlated to locations of greater variance in the segmentations, for example near the pulmonary artery and basal anterior left ventricular wall. Our results indicate that ECGI may be more sensitive to segmentation errors on the anterior epicardial surface than on other areas of the heart.
Collapse
Affiliation(s)
- Jess D Tate
- University of Utah, Salt Lake City, Utah, USA
| | | | | | | | | | | |
Collapse
|
19
|
Coll-Font J, Brooks DH. Tracking the Position of the Heart From Body Surface Potential Maps and Electrograms. Front Physiol 2018; 9:1727. [PMID: 30559678 PMCID: PMC6287036 DOI: 10.3389/fphys.2018.01727] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2018] [Accepted: 11/16/2018] [Indexed: 11/13/2022] Open
Abstract
The accurate generation of forward models is an important element in general research in electrocardiography, and in particular for the techniques for ElectroCardioGraphic Imaging (ECGI). Recent research efforts have been devoted to the reliable and fast generation of forward models. However, these model can suffer from several sources of inaccuracy, which in turn can lead to considerable error in both the forward simulation of body surface potentials and even more so for ECGI solutions. In particular, the accurate localization of the heart within the torso is sensitive to movements due to respiration and changes in position of the subject, a problem that cannot be resolved with better imaging and segmentation alone. Here, we propose an algorithm to localize the position of the heart using electrocardiographic recordings on both the heart and torso surface over a sequence of cardiac cycles. We leverage the dependency of electrocardiographic forward models on the underlying geometry to parameterize the forward model with respect to the position (translation) and orientation of the heart, and then estimate these parameters from heart and body surface potentials in a numerical inverse problem. We show that this approach is capable of localizing the position of the heart in synthetic experiments and that it reduces the modeling error in the forward models and resulting inverse solutions in canine experiments. Our results show a consistent decrease in error of both simulated body surface potentials and inverse reconstructed heart surface potentials after re-localizing the heart based on our estimated geometric correction. These results suggest that this method is capable of improving electrocardiographic models used in research settings and suggest the basis for the extension of the model presented here to its application in a purely inverse setting, where the heart potentials are unknown.
Collapse
Affiliation(s)
- Jaume Coll-Font
- Computational Radiology Laboratory, Children's Hospital, Boston, MA, United States.,Harvard Medical School, Boston, MA, United States
| | - Dana H Brooks
- Signal Processing, Imaging, Reasoning, and Learning (SPIRAL) Group, Electrical and Computer Engineering Department, Northeastern University, Boston, MA, United States
| |
Collapse
|
20
|
Coll-Font J, Wang L, Brooks DH. A Common-Ground Review of the Potential for Machine Learning Approaches in Electrocardiographic Imaging Based on Probabilistic Graphical Models. COMPUTING IN CARDIOLOGY 2018; 45:10.22489/CinC.2018.348. [PMID: 30899763 PMCID: PMC6424344 DOI: 10.22489/cinc.2018.348] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Machine learning (ML) methods have seen an explosion in their development and application. They are increasingly being used in many different fields with considerable success. However, although the interest is growing, their impact in the field of electrocardiographic imaging (ECGI) remains limited. One of the main reasons that ML has yet to become more prevalent in ECGI is that the published literature is scattered and there is no common ground description and comparison of these methods in an ML framework. Here we address this limitation with a review of ECGI methods from the perspective of ML. We will use probabilistic modeling to provide a common ground framework to compare different methods and well known approaches. Finally, we will discuss which approaches have been used to do inference on these models and which alternatives could be utilized as the methods in ML become more mature.
Collapse
Affiliation(s)
- Jaume Coll-Font
- Computational Radiology Lab, Children’s Hospital, Boston (MA), USA
| | - Linwei Wang
- Rochester Institute of Technology, Rochester (NY), USA
| | - Dana H Brooks
- SPIRAL Group, ECE Dept. Northeastern University, Boston (MA), USA
| |
Collapse
|
21
|
Lawson BA, Burrage K, Burrage P, Drovandi CC, Bueno-Orovio A. Slow Recovery of Excitability Increases Ventricular Fibrillation Risk as Identified by Emulation. Front Physiol 2018; 9:1114. [PMID: 30210355 PMCID: PMC6121112 DOI: 10.3389/fphys.2018.01114] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2018] [Accepted: 07/25/2018] [Indexed: 12/28/2022] Open
Abstract
Purpose: Rotor stability and meandering are key mechanisms determining and sustaining cardiac fibrillation, with important implications for anti-arrhythmic drug development. However, little is yet known on how rotor dynamics are modulated by variability in cellular electrophysiology, particularly on kinetic properties of ion channel recovery. Methods: We propose a novel emulation approach, based on Gaussian process regression augmented with machine learning, for data enrichment, automatic detection, classification, and analysis of re-entrant biomarkers in cardiac tissue. More than 5,000 monodomain simulations of long-lasting arrhythmic episodes with Fenton-Karma ionic dynamics, further enriched by emulation to 80 million electrophysiological scenarios, were conducted to investigate the role of variability in ion channel densities and kinetics in modulating rotor-driven arrhythmic behavior. Results: Our methods predicted the class of excitation behavior with classification accuracy up to 96%, and emulation effectively predicted frequency, stability, and spatial biomarkers of functional re-entry. We demonstrate that the excitation wavelength interpretation of re-entrant behavior hides critical information about rotor persistence and devolution into fibrillation. In particular, whereas action potential duration directly modulates rotor frequency and meandering, critical windows of excitability are identified as the main determinants of breakup. Further novel electrophysiological insights of particular relevance for ventricular arrhythmias arise from our multivariate analysis, including the role of incomplete activation of slow inward currents in mediating tissue rate-dependence and dispersion of repolarization, and the emergence of slow recovery of excitability as a significant promoter of this mechanism of dispersion and increased arrhythmic risk. Conclusions: Our results mechanistically explain pro-arrhythmic effects of class Ic anti-arrhythmics in the ventricles despite their established role in the pharmacological management of atrial fibrillation. This is mediated by their slow recovery of excitability mode of action, promoting incomplete activation of slow inward currents and therefore increased dispersion of repolarization, given the larger influence of these currents in modulating the action potential in the ventricles compared to the atria. These results exemplify the potential of emulation techniques in elucidating novel mechanisms of arrhythmia and further application to cardiac electrophysiology.
Collapse
Affiliation(s)
- Brodie A Lawson
- ARC Centre of Excellence for Mathematical and Statistical Frontiers, School of Mathematical Sciences, Queensland University of Technology, Brisbane, QLD, Australia
| | - Kevin Burrage
- ARC Centre of Excellence for Mathematical and Statistical Frontiers, School of Mathematical Sciences, Queensland University of Technology, Brisbane, QLD, Australia.,Department of Computer Science, University of Oxford, Oxford, United Kingdom
| | - Pamela Burrage
- ARC Centre of Excellence for Mathematical and Statistical Frontiers, School of Mathematical Sciences, Queensland University of Technology, Brisbane, QLD, Australia
| | - Christopher C Drovandi
- ARC Centre of Excellence for Mathematical and Statistical Frontiers, School of Mathematical Sciences, Queensland University of Technology, Brisbane, QLD, Australia
| | | |
Collapse
|
22
|
Giffard-Roisin S, Delingette H, Jackson T, Webb J, Fovargue L, Lee J, Rinaldi CA, Razavi R, Ayache N, Sermesant M. Transfer Learning From Simulations on a Reference Anatomy for ECGI in Personalized Cardiac Resynchronization Therapy. IEEE Trans Biomed Eng 2018; 66:343-353. [PMID: 29993409 DOI: 10.1109/tbme.2018.2839713] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
GOAL Noninvasive cardiac electrophysiology (EP) model personalisation has raised interest for instance in the scope of predicting EP cardiac resynchronization therapy (CRT) response. However, the restricted clinical applicability of current methods is due in particular to the limitation to simple situations and the important computational cost. METHODS We propose in this manuscript an approach to tackle these two issues. First, we analyze more complex propagation patterns (multiple onsets and scar tissue) using relevance vector regression and shape dimensionality reduction on a large simulated database. Second, this learning is performed offline on a reference anatomy and transferred onto patient-specific anatomies in order to achieve fast personalized predictions online. RESULTS We evaluated our method on a dataset composed of 20 dyssynchrony patients with a total of 120 different cardiac cycles. The comparison with a commercially available electrocardiographic imaging (ECGI) method shows a good identification of the cardiac activation pattern. From the cardiac parameters estimated in sinus rhythm, we predicted five different paced patterns for each patient. The comparison with the body surface potential mappings (BSPM) measured during pacing and the ECGI method indicates a good predictive power. CONCLUSION We showed that learning offline from a large simulated database on a reference anatomy was able to capture the main cardiac EP characteristics from noninvasive measurements for fast patient-specific predictions. SIGNIFICANCE The fast CRT pacing predictions are a step forward to a noninvasive CRT patient selection and therapy optimisation, to help clinicians in these difficult tasks.
Collapse
|
23
|
Dhamala J, Arevalo HJ, Sapp J, Horácek BM, Wu KC, Trayanova NA, Wang L. Quantifying the uncertainty in model parameters using Gaussian process-based Markov chain Monte Carlo in cardiac electrophysiology. Med Image Anal 2018; 48:43-57. [PMID: 29843078 DOI: 10.1016/j.media.2018.05.007] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2017] [Revised: 03/17/2018] [Accepted: 05/14/2018] [Indexed: 02/02/2023]
Abstract
Model personalization requires the estimation of patient-specific tissue properties in the form of model parameters from indirect and sparse measurement data. Moreover, a low-dimensional representation of the parameter space is needed, which often has a limited ability to reveal the underlying tissue heterogeneity. As a result, significant uncertainty can be associated with the estimated values of the model parameters which, if left unquantified, will lead to unknown variability in model outputs that will hinder their reliable clinical adoption. Probabilistic estimation of model parameters, however, remains an unresolved challenge. Direct Markov Chain Monte Carlo (MCMC) sampling of the posterior distribution function (pdf) of the parameters is infeasible because it involves repeated evaluations of the computationally expensive simulation model. To accelerate this inference, one popular approach is to construct a computationally efficient surrogate and sample from this approximation. However, by sampling from an approximation, efficiency is gained at the expense of sampling accuracy. In this paper, we address this issue by integrating surrogate modeling of the posterior pdf into accelerating the Metropolis-Hastings (MH) sampling of the exact posterior pdf. It is achieved by two main components: (1) construction of a Gaussian process (GP) surrogate of the exact posterior pdf by actively selecting training points that allow for a good global approximation accuracy with a focus on the regions of high posterior probability; and (2) use of the GP surrogate to improve the proposal distribution in MH sampling, in order to improve the acceptance rate. The presented framework is evaluated in its estimation of the local tissue excitability of a cardiac electrophysiological model in both synthetic data experiments and real data experiments. In addition, the obtained posterior distributions of model parameters are interpreted in relation to the factors contributing to parameter uncertainty, including different low-dimensional representations of the parameter space, parameter non-identifiability, and parameter correlations.
Collapse
Affiliation(s)
- Jwala Dhamala
- Rochester Institute of Technology, Rochester, NY, USA. http://www.jwaladhamala.com
| | | | - John Sapp
- Dalhousie University, Halifax, Canada
| | | | | | | | - Linwei Wang
- Rochester Institute of Technology, Rochester, NY, USA
| |
Collapse
|
24
|
Rodenhauser A, Good WW, Zenger B, Tate J, Aras K, Burton B, MacLeod RS. PFEIFER: Preprocessing Framework for Electrograms Intermittently Fiducialized from Experimental Recordings. JOURNAL OF OPEN SOURCE SOFTWARE 2018; 3:472. [PMID: 30259008 PMCID: PMC6152894 DOI: 10.21105/joss.00472] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/13/2023]
Affiliation(s)
- Anton Rodenhauser
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA
| | - Wilson W Good
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA
- Bioengineering Department, University of Utah, Salt Lake City, UT, USA
| | - Brian Zenger
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA
- Bioengineering Department, University of Utah, Salt Lake City, UT, USA
| | - Jess Tate
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA
- Bioengineering Department, University of Utah, Salt Lake City, UT, USA
| | - Kedar Aras
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA
- Bioengineering Department, University of Utah, Salt Lake City, UT, USA
| | - Brett Burton
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA
- Bioengineering Department, University of Utah, Salt Lake City, UT, USA
| | - Rob S MacLeod
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA
- Bioengineering Department, University of Utah, Salt Lake City, UT, USA
| |
Collapse
|
25
|
Johnston BM, Coveney S, Chang ETY, Johnston PR, Clayton RH. Quantifying the effect of uncertainty in input parameters in a simplified bidomain model of partial thickness ischaemia. Med Biol Eng Comput 2017; 56:761-780. [PMID: 28933043 PMCID: PMC5906519 DOI: 10.1007/s11517-017-1714-y] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2016] [Accepted: 08/01/2017] [Indexed: 11/18/2022]
Abstract
Reduced blood flow in the coronary arteries can lead to damaged heart tissue (myocardial ischaemia). Although one method for detecting myocardial ischaemia involves changes in the ST segment of the electrocardiogram, the relationship between these changes and subendocardial ischaemia is not fully understood. In this study, we modelled ST-segment epicardial potentials in a slab model of cardiac ventricular tissue, with a central ischaemic region, using the bidomain model, which considers conduction longitudinal, transverse and normal to the cardiac fibres. We systematically quantified the effect of uncertainty on the input parameters, fibre rotation angle, ischaemic depth, blood conductivity and six bidomain conductivities, on outputs that characterise the epicardial potential distribution. We found that three typical types of epicardial potential distributions (one minimum over the central ischaemic region, a tripole of minima, and two minima flanking a central maximum) could all occur for a wide range of ischaemic depths. In addition, the positions of the minima were affected by both the fibre rotation angle and the ischaemic depth, but not by changes in the conductivity values. We also showed that the magnitude of ST depression is affected only by changes in the longitudinal and normal conductivities, but not by the transverse conductivities.
Collapse
Affiliation(s)
- Barbara M Johnston
- Queensland Micro- and Nanotechnology Centre and School of Natural Sciences, Griffith University, Nathan, QLD, 4111, Australia
| | - Sam Coveney
- Department of Physics and Astronomy, University of Sheffield, Sheffield, UK
| | - Eugene T Y Chang
- Department of Computer Science and INSIGNEO Institute for in-silico Medicine, University of Sheffield, Sheffield, UK
| | - Peter R Johnston
- Queensland Micro- and Nanotechnology Centre and School of Natural Sciences, Griffith University, Nathan, QLD, 4111, Australia
| | - Richard H Clayton
- Department of Computer Science and INSIGNEO Institute for in-silico Medicine, University of Sheffield, Sheffield, UK.
| |
Collapse
|
26
|
Giffard-Roisin S, Jackson T, Fovargue L, Lee J, Delingette H, Razavi R, Ayache N, Sermesant M. Noninvasive Personalization of a Cardiac Electrophysiology Model From Body Surface Potential Mapping. IEEE Trans Biomed Eng 2016; 64:2206-2218. [PMID: 28113292 DOI: 10.1109/tbme.2016.2629849] [Citation(s) in RCA: 44] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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.
Collapse
|
27
|
Schmidt C, Wagner S, Burger M, van Rienen U, Wolters CH. Impact of uncertain head tissue conductivity in the optimization of transcranial direct current stimulation for an auditory target. J Neural Eng 2015; 12:046028. [PMID: 26170066 PMCID: PMC4539365 DOI: 10.1088/1741-2560/12/4/046028] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
OBJECTIVE Transcranial direct current stimulation (tDCS) is a non-invasive brain stimulation technique to modify neural excitability. Using multi-array tDCS, we investigate the influence of inter-individually varying head tissue conductivity profiles on optimal electrode configurations for an auditory cortex stimulation. APPROACH In order to quantify the uncertainty of the optimal electrode configurations, multi-variate generalized polynomial chaos expansions of the model solutions are used based on uncertain conductivity profiles of the compartments skin, skull, gray matter, and white matter. Stochastic measures, probability density functions, and sensitivity of the quantities of interest are investigated for each electrode and the current density at the target with the resulting stimulation protocols visualized on the head surface. MAIN RESULTS We demonstrate that the optimized stimulation protocols are only comprised of a few active electrodes, with tolerable deviations in the stimulation amplitude of the anode. However, large deviations in the order of the uncertainty in the conductivity profiles could be noted in the stimulation protocol of the compensating cathodes. Regarding these main stimulation electrodes, the stimulation protocol was most sensitive to uncertainty in skull conductivity. Finally, the probability that the current density amplitude in the auditory cortex target region is supra-threshold was below 50%. SIGNIFICANCE The results suggest that an uncertain conductivity profile in computational models of tDCS can have a substantial influence on the prediction of optimal stimulation protocols for stimulation of the auditory cortex. The investigations carried out in this study present a possibility to predict the probability of providing a therapeutic effect with an optimized electrode system for future auditory clinical and experimental procedures of tDCS applications.
Collapse
Affiliation(s)
- Christian Schmidt
- Institute of General Electrical Engineering, University of Rostock, 18059 Rostock, Germany
| | - Sven Wagner
- Institute of Biomagnetism and Biosignalanalysis, University of Münster, 48149 Münster, Germany
| | - Martin Burger
- Institute for Computational and Applied Mathematics and Cells-in-Motion Cluster of Excellence, University of Münster, 48149 Münster, Germany
| | - Ursula van Rienen
- Institute of General Electrical Engineering, University of Rostock, 18059 Rostock, Germany
| | - Carsten H Wolters
- Institute of Biomagnetism and Biosignalanalysis, University of Münster, 48149 Münster, Germany
| |
Collapse
|
28
|
Rahimi A, Wang L. Sensitivity of Noninvasive Cardiac Electrophysiological Imaging to Variations in Personalized Anatomical Modeling. IEEE Trans Biomed Eng 2015; 62:1563-75. [PMID: 25615906 PMCID: PMC4581729 DOI: 10.1109/tbme.2015.2395387] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
OBJECTIVE Noninvasive cardiac electrophysiological (EP) imaging techniques rely on anatomically-detailed heart-torso models derived from high-quality tomographic images of individual subjects. However, anatomical modeling involves variations that lead to unresolved uncertainties in the outcome of EP imaging, bringing questions to the robustness of these methods in clinical practice. In this study, we design a systematic statistical approach to assess the sensitivity of EP imaging methods to the variations in personalized anatomical modeling. METHODS We first quantify the variations in personalized anatomical models by a novel application of statistical shape modeling. Given the statistical distribution of the variation in personalized anatomical models, we then employ unscented transform to determine the sensitivity of EP imaging outputs to the variation in input personalized anatomical modeling. RESULTS We test the feasibility of our proposed approach using two of the existing EP imaging methods: epicardial-based electrocardiographic imaging and transmural electrophysiological imaging. Both phantom and real-data experiments show that variations in personalized anatomical models have negligible impact on the outcome of EP imaging. CONCLUSION This study verifies the robustness of EP imaging methods to the errors in personalized anatomical modeling and suggests the possibility to simplify the process of anatomical modeling in future clinical practice. SIGNIFICANCE This study proposes a systematic statistical approach to quantify anatomical modeling variations and assess their impact on EP imaging, which can be extended to find a balance between the quality of personalized anatomical models and the accuracy of EP imaging that may improve the clinical feasibility of EP imaging.
Collapse
Affiliation(s)
- Azar Rahimi
- Galisano College of Computing and Information Sciences, Rochester Institute of Technology, Rochester, NY 14607 USA
| | | |
Collapse
|
29
|
Demetzos C, Pippa N. Fractal analysis as a complementary approach to predict the stability of drug delivery nano systems in aqueous and biological media: A regulatory proposal or a dream? Int J Pharm 2014; 473:213-8. [DOI: 10.1016/j.ijpharm.2014.07.015] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2014] [Revised: 06/27/2014] [Accepted: 07/08/2014] [Indexed: 02/02/2023]
|
30
|
Lanza G. Influencia de los cambios posturales en el electrocardiograma. REVISTA COLOMBIANA DE CARDIOLOGÍA 2014. [DOI: 10.1016/s0120-5633(14)70260-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
|
31
|
Pippa N, Dokoumetzidis A, Demetzos C, Macheras P. On the ubiquitous presence of fractals and fractal concepts in pharmaceutical sciences: A review. Int J Pharm 2013; 456:340-52. [DOI: 10.1016/j.ijpharm.2013.08.087] [Citation(s) in RCA: 43] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2013] [Revised: 08/26/2013] [Accepted: 08/28/2013] [Indexed: 11/27/2022]
|
32
|
Preston JS, Tasdizen T, Terry CM, Cheung AK, Kirby RM. Using the stochastic collocation method for the uncertainty quantification of drug concentration due to depot shape variability. IEEE Trans Biomed Eng 2009; 56:609-20. [PMID: 19272865 DOI: 10.1109/tbme.2008.2009882] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Numerical simulations entail modeling assumptions that impact outcomes. Therefore, characterizing, in a probabilistic sense, the relationship between the variability of model selection and the variability of outcomes is important. Under certain assumptions, the stochastic collocation method offers a computationally feasible alternative to traditional Monte Carlo approaches for assessing the impact of model and parameter variability. We propose a framework that combines component shape parameterization with the stochastic collocation method to study the effect of drug depot shape variability on the outcome of drug diffusion simulations in a porcine model. We use realistic geometries segmented from MR images and employ level-set techniques to create two alternative univariate shape parameterizations. We demonstrate that once the underlying stochastic process is characterized, quantification of the introduced variability is quite straightforward and provides an important step in the validation and verification process.
Collapse
Affiliation(s)
- J Samuel Preston
- Scientific Computing and Imaging Institute and the School of Computing, University of Utah, Salt Lake City, UT 84112, USA.
| | | | | | | | | |
Collapse
|