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Nazar W, Szymanowicz S, Nazar K, Kaufmann D, Wabich E, Braun-Dullaeus R, Daniłowicz-Szymanowicz L. Artificial intelligence models in prediction of response to cardiac resynchronization therapy: a systematic review. Heart Fail Rev 2024; 29:133-150. [PMID: 37861853 PMCID: PMC10904439 DOI: 10.1007/s10741-023-10357-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 09/28/2023] [Indexed: 10/21/2023]
Abstract
The aim of the presented review is to summarize the literature data on the accuracy and clinical applicability of artificial intelligence (AI) models as a valuable alternative to the current guidelines in predicting cardiac resynchronization therapy (CRT) response and phenotyping of patients eligible for CRT implantation. This systematic review was performed according to the PRISMA guidelines. After a search of Scopus, PubMed, Cochrane Library, and Embase databases, 675 records were identified. Twenty supervised (prediction of CRT response) and 9 unsupervised (clustering and phenotyping) AI models were analyzed qualitatively (22 studies, 14,258 patients). Fifty-five percent of AI models were based on retrospective studies. Unsupervised AI models were able to identify clusters of patients with significantly different rates of primary outcome events (death, heart failure event). In comparison to the guideline-based CRT response prediction accuracy of 70%, supervised AI models trained on cohorts with > 100 patients achieved up to 85% accuracy and an AUC of 0.86 in their prediction of response to CRT for echocardiographic and clinical outcomes, respectively. AI models seem to be an accurate and clinically applicable tool in phenotyping of patients eligible for CRT implantation and predicting potential responders. In the future, AI may help to increase CRT response rates to over 80% and improve clinical decision-making and prognosis of the patients, including reduction of mortality rates. However, these findings must be validated in randomized controlled trials.
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Affiliation(s)
- Wojciech Nazar
- Faculty of Medicine, Medical University of Gdańsk, Marii Skłodowskiej-Curie 3a, 80-210, Gdańsk, Poland
| | | | - Krzysztof Nazar
- Faculty of Electronics, Telecommunications and Informatics, Gdańsk University of Technology, Gabriela Narutowicza 11/12, 80-233, Gdańsk, Poland
| | - Damian Kaufmann
- Department of Cardiology and Electrotherapy, Faculty of Medicine, Medical University of Gdańsk, Smoluchowskiego 17, 80-213, Gdańsk, Poland
| | - Elżbieta Wabich
- Department of Cardiology and Electrotherapy, Faculty of Medicine, Medical University of Gdańsk, Smoluchowskiego 17, 80-213, Gdańsk, Poland
| | - Rüdiger Braun-Dullaeus
- Department of Cardiology and Angiology, Otto von Guericke University Magdeburg, Leipziger Street 44, 39120, Magdeburg, Germany
| | - Ludmiła Daniłowicz-Szymanowicz
- Department of Cardiology and Electrotherapy, Faculty of Medicine, Medical University of Gdańsk, Smoluchowskiego 17, 80-213, Gdańsk, Poland.
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Khan MS, Arshad MS, Greene SJ, Van Spall HGC, Pandey A, Vemulapalli S, Perakslis E, Butler J. Artificial intelligence and heart failure: A state-of-the-art review. Eur J Heart Fail 2023; 25:1507-1525. [PMID: 37560778 DOI: 10.1002/ejhf.2994] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 08/06/2023] [Accepted: 08/08/2023] [Indexed: 08/11/2023] Open
Abstract
Heart failure (HF) is a heterogeneous syndrome affecting more than 60 million individuals globally. Despite recent advancements in understanding of the pathophysiology of HF, many issues remain including residual risk despite therapy, understanding the pathophysiology and phenotypes of patients with HF and preserved ejection fraction, and the challenges related to integrating a large amount of disparate information available for risk stratification and management of these patients. Risk prediction algorithms based on artificial intelligence (AI) may have superior predictive ability compared to traditional methods in certain instances. AI algorithms can play a pivotal role in the evolution of HF care by facilitating clinical decision making to overcome various challenges such as allocation of treatment to patients who are at highest risk or are more likely to benefit from therapies, prediction of adverse outcomes, and early identification of patients with subclinical disease or worsening HF. With the ability to integrate and synthesize large amounts of data with multidimensional interactions, AI algorithms can supply information with which physicians can improve their ability to make timely and better decisions. In this review, we provide an overview of the AI algorithms that have been developed for establishing early diagnosis of HF, phenotyping HF with preserved ejection fraction, and stratifying HF disease severity. This review also discusses the challenges in clinical deployment of AI algorithms in HF, and the potential path forward for developing future novel learning-based algorithms to improve HF care.
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Affiliation(s)
| | | | - Stephen J Greene
- Division of Cardiology, Duke University School of Medicine, Durham, NC, USA
- Duke Clinical Research Institute, Durham, NC, USA
| | - Harriette G C Van Spall
- Department of Medicine and Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada
| | - Ambarish Pandey
- Canada Population Health Research Institute, Hamilton, ON, Canada
- Division of Cardiology, Department of Internal Medicine, UT Southwestern Medical Center, Dallas, TX, USA
| | - Sreekanth Vemulapalli
- Division of Cardiology, Duke University School of Medicine, Durham, NC, USA
- Duke Clinical Research Institute, Durham, NC, USA
| | | | - Javed Butler
- Department of Medicine, University of Mississippi Medical Center, Jackson, MS, USA
- Baylor Scott and White Research Institute, Dallas, TX, USA
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Zeng Y, Wang C, Ye Q, Liu G, Zhang L, Wan J, Zhu Y. Machine learning model of imipenem-resistant Klebsiella pneumoniae based on MALDI-TOF-MS platform: An observational study. Health Sci Rep 2023; 6:e1108. [PMID: 37711674 PMCID: PMC10497903 DOI: 10.1002/hsr2.1108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Revised: 01/11/2023] [Accepted: 01/30/2023] [Indexed: 09/16/2023] Open
Abstract
Background and Aim Machine learning is an important branch and supporting technology of artificial intelligence, we established four machine learning model for the drug sensitivity of Klebsiella pneumoniae to imipenem based on matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF-MS) and compared their diagnostic effect. Methods The data of MALDI-TOF-MS and imipenem sensitivity of 174 cases of K. pneumoniae isolated from clinical specimens in the laboratory of microbiology department of Tianjin Haihe Hospital from 2019 January to 2020 December were collected. The mass spectrometry and imipenem sensitivity of 70 cases of imipenem-sensitive and 70 resistant cases were randomly selected to establish the training set model, 17 cases of sensitive and 17 cases of resistant cases were randomly selected to establish the test set model. Mass spectral peak data were subjected to orthogonal partial least squares discriminant analysis (OPLS-DA), the training set data model was established by machine learning least absolute shrinkage and selection operator (LASSO) algorithm, logistic regression (LR) algorithm, support vector machines (SVM) algorithm, neural network (NN) algorithm, the area under the curve (AUC) and confusion matrix of training set and test set model were calculated and selected by Grid search and 3-fold Cross-validation respectively, the accuracy of the prediction model was verified by test set confusion matrix. Results The R²Y and Q² of OPLS-DA were 0.546 and 0.0178. The AUC of the best training set and test set models were 0.9726 and 0.9100, 1.0000 and 0.8581, 0.8462 and 0.6263, 1.0000 and 0.7180 evaluated by LASSO, LR, SVM and NN model respectively. The accuracy of the LASSO, LR, SVM and NN model were 87%, 79%, 62%, and 68% in test set, respectively. Conclusion The LASSO prediction model of K. pneumoniae sensitivity to imipenem established in this study has a high accuracy rate and has potential clinical decision support ability.
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Affiliation(s)
- Yu Zeng
- School of Chemistry and Molecular EngineeringEast China Normal UniversityShanghaiChina
| | - Chao Wang
- Department of Clinical LaboratoryFirst Teaching Hospital of Tianjin University of Traditional Chinese MedicineTianjinChina
| | - Qing Ye
- Department of HepatologyThe Third Central Hospital of TianjinTianjinChina
| | - Gang Liu
- Department of Clinical LaboratoryTianjin Haihe HospitalTianjinChina
| | - Lixia Zhang
- Department of Clinical LaboratoryTianjin Haihe HospitalTianjinChina
| | - Jingjing Wan
- School of Chemistry and Molecular EngineeringEast China Normal UniversityShanghaiChina
| | - Yu Zhu
- Department of Clinical LaboratoryThe Third Central Hospital of TianjinTianjinChina
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Westphal P, Luo H, Shahmohammadi M, Prinzen FW, Delhaas T, Cornelussen RN. Machine learning-powered, device-embedded heart sound measurement can optimize AV delay in patients with CRT. Heart Rhythm 2023; 20:1316-1324. [PMID: 37247684 DOI: 10.1016/j.hrthm.2023.05.025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Revised: 04/28/2023] [Accepted: 05/17/2023] [Indexed: 05/31/2023]
Abstract
BACKGROUND Continuous optimization of atrioventricular (AV) delay for cardiac resynchronization therapy (CRT) is mainly performed by electrical means. OBJECTIVE The purpose of this study was to develop an estimation model of cardiac function that uses a piezoelectric microphone embedded in a pulse generator to guide CRT optimization. METHODS Electrocardiogram, left ventricular pressure (LVP), and heart sounds were simultaneously collected during CRT device implantation procedures. A piezoelectric alarm transducer embedded in a modified CRT device facilitated recording of heart sounds in patients undergoing a pacing protocol with different AV delays. Machine learning (ML) was used to produce a decision-tree ensemble model capable of estimating absolute maximal LVP (LVPmax) and maximal rise of LVP (LVdP/dtmax) using 3 heart sound-based features. To gauge the applicability of ML in AV delay optimization, polynomial curves were fitted to measured and estimated values. RESULTS In the data set of ∼30,000 heartbeats, ML indicated S1 amplitude, S2 amplitude, and S1 integral (S1 energy for LVdP/dtmax) as most prominent features for AV delay optimization. ML resulted in single-beat estimation precision for absolute values of LVPmax and LVdP/dtmax of 67% and 64%, respectively. For 20-30 beat averages, cross-correlation between measured and estimated LVPmax and LVdP/dtmax was 0.999 for both. The estimated optimal AV delays were not significantly different from those measured using invasive LVP (difference -5.6 ± 17.1 ms for LVPmax and +5.1 ± 6.7 ms for LVdP/dtmax). The difference in function at estimated and measured optimal AV delays was not statiscally significant (1 ± 3 mm Hg for LVPmax and 9 ± 57 mm Hg/s for LVdP/dtmax). CONCLUSION Heart sound sensors embedded in a CRT device, powered by a ML algorithm, provide a reliable assessment of optimal AV delays and absolute LVPmax and LVdP/dtmax.
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Affiliation(s)
- Philip Westphal
- Department of Physiology, Cardiovascular Research Institute Maastricht (CARIM), Maastricht, The Netherlands; Bakken Research Center, Medtronic, plc, Maastricht, The Netherlands
| | - Hongxing Luo
- Department of Physiology, Cardiovascular Research Institute Maastricht (CARIM), Maastricht, The Netherlands
| | - Mehrdad Shahmohammadi
- Department of Biomedical Engineering, Cardiovascular Research Institute Maastricht (CARIM), Maastricht, The Netherlands
| | - Frits W Prinzen
- Department of Physiology, Cardiovascular Research Institute Maastricht (CARIM), Maastricht, The Netherlands
| | - Tammo Delhaas
- Department of Biomedical Engineering, Cardiovascular Research Institute Maastricht (CARIM), Maastricht, The Netherlands
| | - Richard N Cornelussen
- Department of Physiology, Cardiovascular Research Institute Maastricht (CARIM), Maastricht, The Netherlands; Bakken Research Center, Medtronic, plc, Maastricht, The Netherlands.
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Dokuchaev A, Chumarnaya T, Bazhutina A, Khamzin S, Lebedeva V, Lyubimtseva T, Zubarev S, Lebedev D, Solovyova O. Combination of personalized computational modeling and machine learning for optimization of left ventricular pacing site in cardiac resynchronization therapy. Front Physiol 2023; 14:1162520. [PMID: 37497440 PMCID: PMC10367108 DOI: 10.3389/fphys.2023.1162520] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Accepted: 06/26/2023] [Indexed: 07/28/2023] Open
Abstract
Introduction: The 30-50% non-response rate to cardiac resynchronization therapy (CRT) calls for improved patient selection and optimized pacing lead placement. The study aimed to develop a novel technique using patient-specific cardiac models and machine learning (ML) to predict an optimal left ventricular (LV) pacing site (ML-PS) that maximizes the likelihood of LV ejection fraction (LVEF) improvement in a given CRT candidate. To validate the approach, we evaluated whether the distance DPS between the clinical LV pacing site (ref-PS) and ML-PS is associated with improved response rate and magnitude. Materials and methods: We reviewed retrospective data for 57 CRT recipients. A positive response was defined as a more than 10% LVEF improvement. Personalized models of ventricular activation and ECG were created from MRI and CT images. The characteristics of ventricular activation during intrinsic rhythm and biventricular (BiV) pacing with ref-PS were derived from the models and used in combination with clinical data to train supervised ML classifiers. The best logistic regression model classified CRT responders with a high accuracy of 0.77 (ROC AUC = 0.84). The LR classifier, model simulations and Bayesian optimization with Gaussian process regression were combined to identify an optimal ML-PS that maximizes the ML-score of CRT response over the LV surface in each patient. Results: The optimal ML-PS improved the ML-score by 17 ± 14% over the ref-PS. Twenty percent of the non-responders were reclassified as positive at ML-PS. Selection of positive patients with a max ML-score >0.5 demonstrated an improved clinical response rate. The distance DPS was shorter in the responders. The max ML-score and DPS were found to be strong predictors of CRT response (ROC AUC = 0.85). In the group with max ML-score > 0.5 and DPS< 30 mm, the response rate was 83% compared to 14% in the rest of the cohort. LVEF improvement in this group was higher than in the other patients (16 ± 8% vs. 7 ± 8%). Conclusion: A new technique combining clinical data, personalized heart modelling and supervised ML demonstrates the potential for use in clinical practice to assist in optimizing patient selection and predicting optimal LV pacing lead position in HF candidates for CRT.
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Affiliation(s)
- Arsenii Dokuchaev
- Institute of Immunology and Physiology, Ural Branch of the Russian Academy of Sciences, Ekaterinburg, Russia
| | - Tatiana Chumarnaya
- Institute of Immunology and Physiology, Ural Branch of the Russian Academy of Sciences, Ekaterinburg, Russia
- Laboratory of Mathematical Modeling in Physiology and Medicine Based on Supercomputers, Ural Federal University, Ekaterinburg, Russia
| | - Anastasia Bazhutina
- Institute of Immunology and Physiology, Ural Branch of the Russian Academy of Sciences, Ekaterinburg, Russia
- Laboratory of Mathematical Modeling in Physiology and Medicine Based on Supercomputers, Ural Federal University, Ekaterinburg, Russia
| | - Svyatoslav Khamzin
- Institute of Immunology and Physiology, Ural Branch of the Russian Academy of Sciences, Ekaterinburg, Russia
| | | | - Tamara Lyubimtseva
- Institute of Immunology and Physiology, Ural Branch of the Russian Academy of Sciences, Ekaterinburg, Russia
- Almazov National Medical Research Centre, Saint Petersburg, Russia
| | - Stepan Zubarev
- Institute of Immunology and Physiology, Ural Branch of the Russian Academy of Sciences, Ekaterinburg, Russia
- Almazov National Medical Research Centre, Saint Petersburg, Russia
| | - Dmitry Lebedev
- Institute of Immunology and Physiology, Ural Branch of the Russian Academy of Sciences, Ekaterinburg, Russia
- Almazov National Medical Research Centre, Saint Petersburg, Russia
| | - Olga Solovyova
- Institute of Immunology and Physiology, Ural Branch of the Russian Academy of Sciences, Ekaterinburg, Russia
- Laboratory of Mathematical Modeling in Physiology and Medicine Based on Supercomputers, Ural Federal University, Ekaterinburg, Russia
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Galli E, Galand V, Le Rolle V, Taconne M, Wazzan AA, Hernandez A, Leclercq C, Donal E. The saga of dyssynchrony imaging: Are we getting to the point. Front Cardiovasc Med 2023; 10:1111538. [PMID: 37063957 PMCID: PMC10103462 DOI: 10.3389/fcvm.2023.1111538] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Accepted: 02/27/2023] [Indexed: 04/03/2023] Open
Abstract
Cardiac resynchronisation therapy (CRT) has an established role in the management of patients with heart failure, reduced left ventricular ejection fraction (LVEF < 35%) and widened QRS (>130 msec). Despite the complex pathophysiology of left ventricular (LV) dyssynchrony and the increasing evidence supporting the identification of specific electromechanical substrates that are associated with a higher probability of CRT response, the assessment of LVEF is the only imaging-derived parameter used for the selection of CRT candidates.This review aims to (1) provide an overview of the evolution of cardiac imaging for the assessment of LV dyssynchrony and its role in the selection of patients undergoing CRT; (2) highlight the main pitfalls and advantages of the application of cardiac imaging for the assessment of LV dyssynchrony; (3) provide some perspectives for clinical application and future research in this field.Conclusionthe road for a more individualized approach to resynchronization therapy delivery is open and imaging might provide important input beyond the assessment of LVEF.
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Loeffler SE, Trayanova N. Primer on Machine Learning in Electrophysiology. Arrhythm Electrophysiol Rev 2023; 12:e06. [PMID: 37427298 PMCID: PMC10323871 DOI: 10.15420/aer.2022.43] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Accepted: 01/10/2023] [Indexed: 07/11/2023] Open
Abstract
Artificial intelligence has become ubiquitous. Machine learning, a branch of artificial intelligence, leads the current technological revolution through its remarkable ability to learn and perform on data sets of varying types. Machine learning applications are expected to change contemporary medicine as they are brought into mainstream clinical practice. In the field of cardiac arrhythmia and electrophysiology, machine learning applications have enjoyed rapid growth and popularity. To facilitate clinical acceptance of these methodologies, it is important to promote general knowledge of machine learning in the wider community and continue to highlight the areas of successful application. The authors present a primer to provide an overview of common supervised (least squares, support vector machine, neural networks and random forest) and unsupervised (k-means and principal component analysis) machine learning models. The authors also provide explanations as to how and why the specific machine learning models have been used in arrhythmia and electrophysiology studies.
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Affiliation(s)
- Shane E Loeffler
- Alliance for Cardiovascular Diagnostic and Treatment Innovation (ADVANCE), Johns Hopkins University Baltimore, MD, US
| | - Natalia Trayanova
- Alliance for Cardiovascular Diagnostic and Treatment Innovation (ADVANCE), Johns Hopkins University Baltimore, MD, US
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, US
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Doste R, Lozano M, Jimenez-Perez G, Mont L, Berruezo A, Penela D, Camara O, Sebastian R. Training machine learning models with synthetic data improves the prediction of ventricular origin in outflow tract ventricular arrhythmias. Front Physiol 2022; 13:909372. [PMID: 36035489 PMCID: PMC9412034 DOI: 10.3389/fphys.2022.909372] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 07/07/2022] [Indexed: 11/13/2022] Open
Abstract
In order to determine the site of origin (SOO) in outflow tract ventricular arrhythmias (OTVAs) before an ablation procedure, several algorithms based on manual identification of electrocardiogram (ECG) features, have been developed. However, the reported accuracy decreases when tested with different datasets. Machine learning algorithms can automatize the process and improve generalization, but their performance is hampered by the lack of large enough OTVA databases. We propose the use of detailed electrophysiological simulations of OTVAs to train a machine learning classification model to predict the ventricular origin of the SOO of ectopic beats. We generated a synthetic database of 12-lead ECGs (2,496 signals) by running multiple simulations from the most typical OTVA SOO in 16 patient-specific geometries. Two types of input data were considered in the classification, raw and feature ECG signals. From the simulated raw 12-lead ECG, we analyzed the contribution of each lead in the predictions, keeping the best ones for the training process. For feature-based analysis, we used entropy-based methods to rank the obtained features. A cross-validation process was included to evaluate the machine learning model. Following, two clinical OTVA databases from different hospitals, including ECGs from 365 patients, were used as test-sets to assess the generalization of the proposed approach. The results show that V2 was the best lead for classification. Prediction of the SOO in OTVA, using both raw signals or features for classification, presented high accuracy values (>0.96). Generalization of the network trained on simulated data was good for both patient datasets (accuracy of 0.86 and 0.84, respectively) and presented better values than using exclusively real ECGs for classification (accuracy of 0.84 and 0.76 for each dataset). The use of simulated ECG data for training machine learning-based classification algorithms is critical to obtain good SOO predictions in OTVA compared to real data alone. The fast implementation and generalization of the proposed methodology may contribute towards its application to a clinical routine.
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Affiliation(s)
- Ruben Doste
- Department of Computer Science, University of Oxford, Oxford, United Kingdom
- *Correspondence: Ruben Doste,
| | - Miguel Lozano
- Computational Multiscale Simulation Lab (CoMMLab), Department of Computer Science, Universitat de Valencia, Valencia, Spain
| | - Guillermo Jimenez-Perez
- Physense, BCN Medtech, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
| | - Lluis Mont
- Arrhythmia Section, Cardiology Department, Cardiovascular Clinical Institute, Hospital Clínic, Universitat de Barcelona - IDIBAPS, Barcelona, Spain
| | - Antonio Berruezo
- Cardiology Department, Heart Institute, Teknon Medical Center, Barcelona, Spain
| | - Diego Penela
- Cardiology Department, Heart Institute, Teknon Medical Center, Barcelona, Spain
| | - Oscar Camara
- Physense, BCN Medtech, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
| | - Rafael Sebastian
- Computational Multiscale Simulation Lab (CoMMLab), Department of Computer Science, Universitat de Valencia, Valencia, Spain
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