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Tsukada S, Iwasaki YK, Tsukada YT. Tensor cardiography: A novel ECG analysis of deviations in collective myocardial action potential transitions based on point processes and cumulative distribution functions. PLOS DIGITAL HEALTH 2024; 3:e0000273. [PMID: 39116062 PMCID: PMC11309480 DOI: 10.1371/journal.pdig.0000273] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Accepted: 06/17/2024] [Indexed: 08/10/2024]
Abstract
To improve clinical diagnoses, assessments of potential cardiac disease risk, and predictions of lethal arrhythmias, the analysis of electrocardiograms (ECGs) requires a more accurate method of weighting waveforms to efficiently detect abnormalities that appear as minute strains in the waveforms. In addition, the inverse problem of estimating the myocardial action potential from the ECG has been a longstanding challenge. To analyze the variance of the ECG waveforms and to estimate collective myocardial action potentials (APs) from the ECG, we designed a model equation incorporating the probability densities of Gaussian functions of time-series point processes in the cardiac cycle and dipoles of the collective APs in the myocardium. The equation, which involves taking the difference between the cumulative distribution functions (CDFs) that represent positive endocardial and negative epicardial potentials, fits both R and T waves. The mean, standard deviation, weights, and level of each cumulative distribution function (CDF) are metrics for the variance of the transition state of the collective myocardial AP. Clinical ECGs of myocardial ischemia during coronary intervention show abnormalities in the aforementioned specific elements of the tensor associated with repolarization transition variance earlier than in conventional indicators of ischemia. The tensor can be used to evaluate the beat-to-beat dynamic repolarization changes between the ventricular epi and endocardium in terms of the Mahalanobis distance (MD). This tensor-based cardiography that uses the differences between CDFs to show changes in collective myocardial APs has the potential to be a new analysis tool for ECGs.
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Affiliation(s)
- Shingo Tsukada
- Molecular and Bio Science Research Group, NTT Basic Research Laboratories and Bio-Medical Informatics Research Center, 3–1, Morinosato Wakamiya, Atsugi-city, Kanagawa Pref., Japan Premium Research Institute for Human Metaverse Medicine (WPI-PRIMe), Osaka University, Japan
| | - Yu-ki Iwasaki
- Department of Cardiovascular Medicine, Nippon Medical School, Japan
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Hu W, Bian G, Huang L, Pi Y, Zhang X, Zhang X, de Albuquerque VHC, Wu W. Constructing Bodily Emotion Maps Based on High-Density Body Surface Potentials for Psychophysiological Computing. IEEE J Biomed Health Inform 2024; 28:2500-2511. [PMID: 38051611 DOI: 10.1109/jbhi.2023.3339382] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/07/2023]
Abstract
Emotion is a complex physiological and psychological activity, accompanied by subjective physiological sensations and objective physiological changes. The body sensation map describes the changes in body sensation associated with emotion in a topographic manner, but it relies on subjective evaluations from participants. Physiological signals are a more reliable measure of emotion, but most research focuses on the central nervous system, neglecting the importance of the peripheral nervous system. In this study, a body surface potential mapping (BSPM) system was constructed, and an experiment was designed to induce emotions and obtain high-density body surface potential information under negative and non-negative emotions. Then, by constructing and analyzing the functional connectivity network of BSPs, the high-density electrophysiological characteristics are obtained and visualized as bodily emotion maps. The results showed that the functional connectivity network of BSPs under negative emotions had denser connections, and emotion maps based on local clustering coefficient (LCC) are consistent with BSMs under negative emotions. in addition, our features can classify negative and non-negative emotions with the highest classification accuracy of 80.77%. In conclusion, this study constructs an emotion map based on high-density BSPs, which offers a novel approach to psychophysiological computing.
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Ondrusova B, Tino P, Svehlikova J. A two-step inverse solution for a single dipole cardiac source. Front Physiol 2023; 14:1264690. [PMID: 37745249 PMCID: PMC10513503 DOI: 10.3389/fphys.2023.1264690] [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: 07/21/2023] [Accepted: 08/25/2023] [Indexed: 09/26/2023] Open
Abstract
Introduction: The inverse problem of electrocardiography noninvasively localizes the origin of undesired cardiac activity, such as a premature ventricular contraction (PVC), from potential recordings from multiple torso electrodes. However, the optimal number and placement of electrodes for an accurate solution of the inverse problem remain undetermined. This study presents a two-step inverse solution for a single dipole cardiac source, which investigates the significance of the torso electrodes on a patient-specific level. Furthermore, the impact of the significant electrodes on the accuracy of the inverse solution is studied. Methods: Body surface potential recordings from 128 electrodes of 13 patients with PVCs and their corresponding homogeneous and inhomogeneous torso models were used. The inverse problem using a single dipole was solved in two steps: First, using information from all electrodes, and second, using a subset of electrodes sorted in descending order according to their significance estimated by a greedy algorithm. The significance of electrodes was computed for three criteria derived from the singular values of the transfer matrix that correspond to the inversely estimated origin of the PVC computed in the first step. The localization error (LE) was computed as the Euclidean distance between the ground truth and the inversely estimated origin of the PVC. The LE obtained using the 32 and 64 most significant electrodes was compared to the LE obtained when all 128 electrodes were used for the inverse solution. Results: The average LE calculated for both torso models and using all 128 electrodes was 28.8 ± 11.9 mm. For the three tested criteria, the average LEs were 32.6 ± 19.9 mm, 29.6 ± 14.7 mm, and 28.8 ± 14.5 mm when 32 electrodes were used. When 64 electrodes were used, the average LEs were 30.1 ± 16.8 mm, 29.4 ± 12.0 mm, and 29.5 ± 12.6 mm. Conclusion: The study found inter-patient variability in the significance of torso electrodes and demonstrated that an accurate localization by the inverse solution with a single dipole could be achieved using a carefully selected reduced number of electrodes.
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Affiliation(s)
- Beata Ondrusova
- Institute of Measurement Science, Slovak Academy of Sciences, Bratislava, Slovakia
- Faculty of Electrical Engineering and Information Technology, Slovak University of Technology in Bratislava, Bratislava, Slovakia
| | - Peter Tino
- School of Computer Science, University of Birmingham, Birmingham, United Kingdom
| | - Jana Svehlikova
- Institute of Measurement Science, Slovak Academy of Sciences, Bratislava, Slovakia
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Dogrusoz YS, Rasoolzadeh N, Ondrusova B, Hlivak P, Zelinka J, Tysler M, Svehlikova J. Comparison of dipole-based and potential-based ECGI methods for premature ventricular contraction beat localization with clinical data. Front Physiol 2023; 14:1197778. [PMID: 37362428 PMCID: PMC10288213 DOI: 10.3389/fphys.2023.1197778] [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: 03/31/2023] [Accepted: 05/22/2023] [Indexed: 06/28/2023] Open
Abstract
Introduction: Localization of premature ventricular contraction (PVC) origin to guide the radiofrequency ablation (RFA) procedure is one of the prominent clinical goals of non-invasive electrocardiographic imaging. However, the results reported in the literature vary significantly depending on the source model and the level of complexity in the forward model. This study aims to compare the paced and spontaneous PVC localization performances of dipole-based and potential-based source models and corresponding inverse methods using the same clinical data and to evaluate the effects of torso inhomogeneities on these performances. Methods: The publicly available EP solution data from the EDGAR data repository (BSPs from a maximum of 240 electrodes) with known pacing locations and the Bratislava data (BSPs in 128 leads) with spontaneous PVCs from patients who underwent successful RFA procedures were used. Homogeneous and inhomogeneous torso models and corresponding forward problem solutions were used to relate sources on the closed epicardial and epicardial-endocardial surfaces. The localization error (LE) between the true and estimated pacing site/PVC origin was evaluated. Results: For paced data, the median LE values were 25.2 and 13.9 mm for the dipole-based and potential-based models, respectively. These median LE values were higher for the spontaneous PVC data: 30.2-33.0 mm for the dipole-based model and 28.9-39.2 mm for the potential-based model. The assumption of inhomogeneities in the torso model did not change the dipole-based solutions much, but using an inhomogeneous model improved the potential-based solutions on the epicardial-endocardial ventricular surface. Conclusion: For the specific task of localization of pacing site/PVC origin, the dipole-based source model is more stable and robust than the potential-based source model. The torso inhomogeneities affect the performances of PVC origin localization in each source model differently. Hence, care must be taken in generating patient-specific geometric and forward models depending on the source model representation used in electrocardiographic imaging (ECGI).
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Affiliation(s)
- Yesim Serinagaoglu Dogrusoz
- Department of Electrical-Electronics Engineering, Middle East Technical University, Ankara, Türkiye
- Department of Scientific Computing, Middle East Technical University, Institute of Applied Mathematics, Ankara, Türkiye
| | - Nika Rasoolzadeh
- Department of Electrical-Electronics Engineering, Middle East Technical University, Ankara, Türkiye
- Department of Scientific Computing, Middle East Technical University, Institute of Applied Mathematics, Ankara, Türkiye
| | - Beata Ondrusova
- Institute of Measurement Science, Slovak Academy of Sciences, Bratislava, Slovakia
- Faculty of Electrical Engineering and Information Technology, Slovak University of Technology, Bratislava, Slovakia
| | - Peter Hlivak
- National Institute for Cardiovascular Diseases, Bratislava, Slovakia
| | - Jan Zelinka
- Institute of Measurement Science, Slovak Academy of Sciences, Bratislava, Slovakia
| | - Milan Tysler
- Institute of Measurement Science, Slovak Academy of Sciences, Bratislava, Slovakia
| | - Jana Svehlikova
- Institute of Measurement Science, Slovak Academy of Sciences, Bratislava, Slovakia
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5
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Boonstra MJ, Brooks DH, Loh P, van Dam PM. CineECG: A novel method to image the average activation sequence in the heart from the 12-lead ECG. Comput Biol Med 2022; 141:105128. [PMID: 34973587 DOI: 10.1016/j.compbiomed.2021.105128] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Revised: 12/08/2021] [Accepted: 12/08/2021] [Indexed: 11/03/2022]
Abstract
The standard 12-lead electrocardiogram (ECG) is a diagnostic tool to asses cardiac electrical activity. The vectorcardiogram is a related tool that represents that activity as the direction of a vector. In this work we investigate CineECG, a new 12-lead ECG based analysis method designed to directly estimate the average cardiac anatomical location of activation over time. We describe CineECG calculation and a novel comparison parameter, the average isochrone position (AIP). In a model study, fourteen different activation sequences were simulated and corresponding 12-lead ECGs were computed. The CineECG was compared to AIP in terms of location and direction. In addition, 67-lead body surface potential maps from ten patients were used to study the sensitivity of CineECG to electrode mispositioning and anatomical model selection. Epicardial activation maps from four patients were used for further evaluation. The average distance between CineECG and AIP across the fourteen sequences was 23.7 ± 2.4 mm, with significantly better agreement in the terminal (27.3 ± 5.7 mm) versus the initial QRS segment (34.2 ± 6.1 mm). Up to four cm variation in electrode positioning produced an average distance of 6.5 ± 4.5 mm between CineECG trajectories, while substituting a generic heart/torso model for a patient-specific one produced an average difference of 6.1 ± 4.8 mm. Dominant epicardial activation map features were recovered. Qualitatively, CineECG captured significant features of activation sequences and was robust to electrode misplacement. CineECG provides a realistic representation of the average cardiac activation in normal and diseased hearts. In particular, the terminal segment of the CineECG might be useful to detect pathology.
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Affiliation(s)
- Machteld J Boonstra
- Department of Cardiology, Division Heart & Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Dana H Brooks
- Electrical and Computer Engineering, Northeastern University, Boston, MA, 02115, USA
| | - Peter Loh
- Department of Cardiology, Division Heart & Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Peter M van Dam
- Department of Cardiology, Division Heart & Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands; ECG Excellence BV, Nieuwerbrug aan den Rijn, the Netherlands.
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Nakano Y, Rashed EA, Nakane T, Laakso I, Hirata A. ECG Localization Method Based on Volume Conductor Model and Kalman Filtering. SENSORS (BASEL, SWITZERLAND) 2021; 21:4275. [PMID: 34206512 PMCID: PMC8271910 DOI: 10.3390/s21134275] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 06/15/2021] [Accepted: 06/17/2021] [Indexed: 11/24/2022]
Abstract
The 12-lead electrocardiogram was invented more than 100 years ago and is still used as an essential tool in the early detection of heart disease. By estimating the time-varying source of the electrical activity from the potential changes, several types of heart disease can be noninvasively identified. However, most previous studies are based on signal processing, and thus an approach that includes physics modeling would be helpful for source localization problems. This study proposes a localization method for cardiac sources by combining an electrical analysis with a volume conductor model of the human body as a forward problem and a sparse reconstruction method as an inverse problem. Our formulation estimates not only the current source location but also the current direction. For a 12-lead electrocardiogram system, a sensitivity analysis of the localization to cardiac volume, tilted angle, and model inhomogeneity was evaluated. Finally, the estimated source location is corrected by Kalman filter, considering the estimated electrocardiogram source as time-sequence data. For a high signal-to-noise ratio (greater than 20 dB), the dominant error sources were the model inhomogeneity, which is mainly attributable to the high conductivity of the blood in the heart. The average localization error of the electric dipole sources in the heart was 12.6 mm, which is comparable to that in previous studies, where a less detailed anatomical structure was considered. A time-series source localization with Kalman filtering indicated that source mislocalization could be compensated, suggesting the effectiveness of the source estimation using the current direction and location simultaneously. For the electrocardiogram R-wave, the mean distance error was reduced to less than 7.3 mm using the proposed method. Considering the physical properties of the human body with Kalman filtering enables highly accurate estimation of the cardiac electric signal source location and direction. This proposal is also applicable to electrode configuration, such as ECG sensing systems.
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Affiliation(s)
- Yuki Nakano
- Department of Electrical and Mechanical Engineering, Nagoya Institute of Technology, Nagoya 466-8555, Japan; (Y.N.); (E.A.R.); (T.N.)
| | - Essam A. Rashed
- Department of Electrical and Mechanical Engineering, Nagoya Institute of Technology, Nagoya 466-8555, Japan; (Y.N.); (E.A.R.); (T.N.)
- Department of Mathematics, Faculty of Science, Suez Canal University, Ismailia 41522, Egypt
| | - Tatsuhito Nakane
- Department of Electrical and Mechanical Engineering, Nagoya Institute of Technology, Nagoya 466-8555, Japan; (Y.N.); (E.A.R.); (T.N.)
| | - Ilkka Laakso
- Department of Electrical Engineering and Automation, Aalto University, 02150 Espoo, Finland;
| | - Akimasa Hirata
- Department of Electrical and Mechanical Engineering, Nagoya Institute of Technology, Nagoya 466-8555, Japan; (Y.N.); (E.A.R.); (T.N.)
- Center of Biomedical Physics and Information Technology, Nagoya Institute of Technology, Nagoya 466-8555, Japan
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Pereira H, Niederer S, Rinaldi CA. Electrocardiographic imaging for cardiac arrhythmias and resynchronization therapy. Europace 2020; 22:euaa165. [PMID: 32754737 PMCID: PMC7544539 DOI: 10.1093/europace/euaa165] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2020] [Accepted: 05/25/2020] [Indexed: 12/12/2022] Open
Abstract
Use of the 12-lead electrocardiogram (ECG) is fundamental for the assessment of heart disease, including arrhythmias, but cannot always reveal the underlying mechanism or the location of the arrhythmia origin. Electrocardiographic imaging (ECGi) is a non-invasive multi-lead ECG-type imaging tool that enhances conventional 12-lead ECG. Although it is an established technology, its continuous development has been shown to assist in arrhythmic activation mapping and provide insights into the mechanism of cardiac resynchronization therapy (CRT). This review addresses the validity, reliability, and overall feasibility of ECGi for use in a diverse range of arrhythmias. A systematic search limited to full-text human studies published in peer-reviewed journals was performed through Medline via PubMed, using various combinations of three key concepts: ECGi, arrhythmia, and CRT. A total of 456 studies were screened through titles and abstracts. Ultimately, 42 studies were included for literature review. Evidence to date suggests that ECGi can be used to provide diagnostic insights regarding the mechanistic basis of arrhythmias and the location of arrhythmia origin. Furthermore, ECGi can yield valuable information to guide therapeutic decision-making, including during CRT. Several studies have used ECGi as a diagnostic tool for atrial and ventricular arrhythmias. More recently, studies have tested the value of this technique in predicting outcomes of CRT. As a non-invasive method for assessing cardiovascular disease, particularly arrhythmias, ECGi represents a significant advancement over standard procedures in contemporary cardiology. Its full potential has yet to be fully explored.
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Affiliation(s)
- Helder Pereira
- Division of Imaging Sciences and Biomedical Engineering, King’s College London, 4th Floor, Lambeth Wing, St. Thomas’ Hospital, Westminster Bridge Rd, London SE1 7EH, UK
- Cardiac Physiology Services—Clinical Investigation Centre, Bupa Cromwell Hospital, London, UK
| | - Steven Niederer
- Division of Imaging Sciences and Biomedical Engineering, King’s College London, 4th Floor, Lambeth Wing, St. Thomas’ Hospital, Westminster Bridge Rd, London SE1 7EH, UK
| | - Christopher A Rinaldi
- Division of Imaging Sciences and Biomedical Engineering, King’s College London, 4th Floor, Lambeth Wing, St. Thomas’ Hospital, Westminster Bridge Rd, London SE1 7EH, UK
- Cardiovascular Department, Guys and St Thomas NHS Foundation Trust, London, UK
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Cheniti G, Puyo S, Martin CA, Frontera A, Vlachos K, Takigawa M, Bourier F, Kitamura T, Lam A, Dumas-Pommier C, Pillois X, Pambrun T, Duchateau J, Klotz N, Denis A, Derval N, Cochet H, Sacher F, Dubois R, Jais P, Hocini M, Haissaguerre M. Noninvasive Mapping and Electrocardiographic Imaging in Atrial and Ventricular Arrhythmias (CardioInsight). Card Electrophysiol Clin 2019; 11:459-471. [PMID: 31400870 DOI: 10.1016/j.ccep.2019.05.004] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Electrocardiographic imaging is a mapping technique aiming to noninvasively characterize cardiac electrical activity using signals collected from the torso to reconstruct epicardial potentials. Its efficacy has been demonstrated clinically, from mapping premature ventricular complexes and accessory pathways to of complex arrhythmias. Electrocardiographic imaging uses a standardized workflow. Signals should be checked manually to avoid automatic processing errors. Reentry is confirmed in the presence of local activation covering the arrhythmia cycle length. Focal breakthroughs demonstrate a QS pattern associated with centrifugal activation. Electrocardiographic imaging offers a unique opportunity to better understand the mechanism of cardiac arrhythmias and guide ablation.
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Affiliation(s)
- Ghassen Cheniti
- Cardiac electrophysiology department, Hôpital Haut-Lévêque, 1 Magellan Avenue, Bordeaux, Pessac 33600, France; Electrophysiology and Heart Modeling Institute (LIRYC), Bordeaux University, avenue Haut Leveque, Pessac 33600, France.
| | - Stephane Puyo
- Electrophysiology and Heart Modeling Institute (LIRYC), Bordeaux University, avenue Haut Leveque, Pessac 33600, France
| | - Claire A Martin
- Cardiac electrophysiology department, Hôpital Haut-Lévêque, 1 Magellan Avenue, Bordeaux, Pessac 33600, France; Electrophysiology and Heart Modeling Institute (LIRYC), Bordeaux University, avenue Haut Leveque, Pessac 33600, France
| | - Antonio Frontera
- Cardiac electrophysiology department, Hôpital Haut-Lévêque, 1 Magellan Avenue, Bordeaux, Pessac 33600, France; Electrophysiology and Heart Modeling Institute (LIRYC), Bordeaux University, avenue Haut Leveque, Pessac 33600, France
| | - Konstantinos Vlachos
- Cardiac electrophysiology department, Hôpital Haut-Lévêque, 1 Magellan Avenue, Bordeaux, Pessac 33600, France; Electrophysiology and Heart Modeling Institute (LIRYC), Bordeaux University, avenue Haut Leveque, Pessac 33600, France
| | - Masateru Takigawa
- Cardiac electrophysiology department, Hôpital Haut-Lévêque, 1 Magellan Avenue, Bordeaux, Pessac 33600, France; Electrophysiology and Heart Modeling Institute (LIRYC), Bordeaux University, avenue Haut Leveque, Pessac 33600, France
| | - Felix Bourier
- Cardiac electrophysiology department, Hôpital Haut-Lévêque, 1 Magellan Avenue, Bordeaux, Pessac 33600, France; Electrophysiology and Heart Modeling Institute (LIRYC), Bordeaux University, avenue Haut Leveque, Pessac 33600, France
| | - Takeshi Kitamura
- Cardiac electrophysiology department, Hôpital Haut-Lévêque, 1 Magellan Avenue, Bordeaux, Pessac 33600, France; Electrophysiology and Heart Modeling Institute (LIRYC), Bordeaux University, avenue Haut Leveque, Pessac 33600, France
| | - Anna Lam
- Cardiac electrophysiology department, Hôpital Haut-Lévêque, 1 Magellan Avenue, Bordeaux, Pessac 33600, France; Electrophysiology and Heart Modeling Institute (LIRYC), Bordeaux University, avenue Haut Leveque, Pessac 33600, France
| | - Carole Dumas-Pommier
- Cardiac electrophysiology department, Hôpital Haut-Lévêque, 1 Magellan Avenue, Bordeaux, Pessac 33600, France
| | - Xavier Pillois
- Cardiac electrophysiology department, Hôpital Haut-Lévêque, 1 Magellan Avenue, Bordeaux, Pessac 33600, France
| | - Thomas Pambrun
- Cardiac electrophysiology department, Hôpital Haut-Lévêque, 1 Magellan Avenue, Bordeaux, Pessac 33600, France; Electrophysiology and Heart Modeling Institute (LIRYC), Bordeaux University, avenue Haut Leveque, Pessac 33600, France
| | - Josselin Duchateau
- Cardiac electrophysiology department, Hôpital Haut-Lévêque, 1 Magellan Avenue, Bordeaux, Pessac 33600, France; Electrophysiology and Heart Modeling Institute (LIRYC), Bordeaux University, avenue Haut Leveque, Pessac 33600, France
| | - Nicolas Klotz
- Cardiac electrophysiology department, Hôpital Haut-Lévêque, 1 Magellan Avenue, Bordeaux, Pessac 33600, France; Electrophysiology and Heart Modeling Institute (LIRYC), Bordeaux University, avenue Haut Leveque, Pessac 33600, France
| | - Arnaud Denis
- Cardiac electrophysiology department, Hôpital Haut-Lévêque, 1 Magellan Avenue, Bordeaux, Pessac 33600, France; Electrophysiology and Heart Modeling Institute (LIRYC), Bordeaux University, avenue Haut Leveque, Pessac 33600, France
| | - Nicolas Derval
- Cardiac electrophysiology department, Hôpital Haut-Lévêque, 1 Magellan Avenue, Bordeaux, Pessac 33600, France; Electrophysiology and Heart Modeling Institute (LIRYC), Bordeaux University, avenue Haut Leveque, Pessac 33600, France
| | - Hubert Cochet
- Electrophysiology and Heart Modeling Institute (LIRYC), Bordeaux University, avenue Haut Leveque, Pessac 33600, France; Department of Cardiovascular Imaging, Hôpital Haut-Lévêque, 1 Magellan Avenue, Bordeaux, Pessac 33600, France
| | - Frederic Sacher
- Cardiac electrophysiology department, Hôpital Haut-Lévêque, 1 Magellan Avenue, Bordeaux, Pessac 33600, France; Electrophysiology and Heart Modeling Institute (LIRYC), Bordeaux University, avenue Haut Leveque, Pessac 33600, France
| | - Remi Dubois
- Electrophysiology and Heart Modeling Institute (LIRYC), Bordeaux University, avenue Haut Leveque, Pessac 33600, France
| | - Pierre Jais
- Cardiac electrophysiology department, Hôpital Haut-Lévêque, 1 Magellan Avenue, Bordeaux, Pessac 33600, France; Electrophysiology and Heart Modeling Institute (LIRYC), Bordeaux University, avenue Haut Leveque, Pessac 33600, France
| | - Meleze Hocini
- Cardiac electrophysiology department, Hôpital Haut-Lévêque, 1 Magellan Avenue, Bordeaux, Pessac 33600, France; Electrophysiology and Heart Modeling Institute (LIRYC), Bordeaux University, avenue Haut Leveque, Pessac 33600, France
| | - Michel Haissaguerre
- Cardiac electrophysiology department, Hôpital Haut-Lévêque, 1 Magellan Avenue, Bordeaux, Pessac 33600, France; Electrophysiology and Heart Modeling Institute (LIRYC), Bordeaux University, avenue Haut Leveque, Pessac 33600, France
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Lv W, Lee K, Arai T, Barrett CD, Hasan MM, Hayward AM, Marini RP, Barley ME, Galea A, Hirschman G, Armoundas AA, Cohen RJ. Accuracy of cardiac ablation catheter guidance by means of a single equivalent moving dipole inverse algorithm to identify sites of origin of cardiac electrical activation. J Interv Card Electrophysiol 2019; 58:323-331. [DOI: 10.1007/s10840-019-00605-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/10/2019] [Accepted: 08/02/2019] [Indexed: 12/19/2022]
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10
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Cluitmans M, Brooks DH, MacLeod R, Dössel O, Guillem MS, van Dam PM, Svehlikova J, He B, Sapp J, Wang L, Bear L. Validation and Opportunities of Electrocardiographic Imaging: From Technical Achievements to Clinical Applications. Front Physiol 2018; 9:1305. [PMID: 30294281 PMCID: PMC6158556 DOI: 10.3389/fphys.2018.01305] [Citation(s) in RCA: 73] [Impact Index Per Article: 12.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2018] [Accepted: 08/29/2018] [Indexed: 11/23/2022] Open
Abstract
Electrocardiographic imaging (ECGI) reconstructs the electrical activity of the heart from a dense array of body-surface electrocardiograms and a patient-specific heart-torso geometry. Depending on how it is formulated, ECGI allows the reconstruction of the activation and recovery sequence of the heart, the origin of premature beats or tachycardia, the anchors/hotspots of re-entrant arrhythmias and other electrophysiological quantities of interest. Importantly, these quantities are directly and non-invasively reconstructed in a digitized model of the patient's three-dimensional heart, which has led to clinical interest in ECGI's ability to personalize diagnosis and guide therapy. Despite considerable development over the last decades, validation of ECGI is challenging. Firstly, results depend considerably on implementation choices, which are necessary to deal with ECGI's ill-posed character. Secondly, it is challenging to obtain (invasive) ground truth data of high quality. In this review, we discuss the current status of ECGI validation as well as the major challenges remaining for complete adoption of ECGI in clinical practice. Specifically, showing clinical benefit is essential for the adoption of ECGI. Such benefit may lie in patient outcome improvement, workflow improvement, or cost reduction. Future studies should focus on these aspects to achieve broad adoption of ECGI, but only after the technical challenges have been solved for that specific application/pathology. We propose 'best' practices for technical validation and highlight collaborative efforts recently organized in this field. Continued interaction between engineers, basic scientists, and physicians remains essential to find a hybrid between technical achievements, pathological mechanisms insights, and clinical benefit, to evolve this powerful technique toward a useful role in clinical practice.
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Affiliation(s)
- Matthijs Cluitmans
- Department of Cardiology, Cardiovascular Research Institute Maastricht Maastricht University, Maastricht, Netherlands
| | - Dana H. Brooks
- Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, United States
| | - Rob MacLeod
- Biomedical Engineering Department, Scientific Computing and Imaging Institute (SCI), and Cardiovascular Research and Training Institute (CVRTI), The University of Utah, Salt Lake City, UT, United States
| | - Olaf Dössel
- Karlsruhe Institute of Technology, Karlsruhe, Germany
| | | | - Peter M. van Dam
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen Medical Center, Nijmegen, Netherlands
| | - Jana Svehlikova
- Institute of Measurement Science, Slovak Academy of Sciences, Bratislava, Slovakia
| | - Bin He
- Department of Biomedical Engineering Carnegie Mellon University, Pittsburgh, PA, United States
| | - John Sapp
- QEII Health Sciences Centre and Department of Medicine, Dalhousie University, Halifax, NS, Canada
| | - Linwei Wang
- Rochester Institute of Technology, Rochester, NY, United States
| | - Laura Bear
- IHU LIRYC, Fondation Bordeaux Université, Inserm U1045 and Université de Bordeaux, Bordeaux, France
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