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Mason F, Pandey AC, Gadaleta M, Topol EJ, Muse ED, Quer G. AI-enhanced reconstruction of the 12-lead electrocardiogram via 3-leads with accurate clinical assessment. NPJ Digit Med 2024; 7:201. [PMID: 39090394 PMCID: PMC11294561 DOI: 10.1038/s41746-024-01193-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2023] [Accepted: 07/12/2024] [Indexed: 08/04/2024] Open
Abstract
The 12-lead electrocardiogram (ECG) is an integral component to the diagnosis of a multitude of cardiovascular conditions. It is performed using a complex set of skin surface electrodes, limiting its use outside traditional clinical settings. We developed an artificial intelligence algorithm, trained over 600,000 clinically acquired ECGs, to explore whether fewer leads as input are sufficient to reconstruct a 12-lead ECG. Two limb leads (I and II) and one precordial lead (V3) were required to generate a reconstructed 12-lead ECG highly correlated with the original ECG. An automatic algorithm for detection of ECG features consistent with acute myocardial infarction (MI) performed similarly for original and reconstructed ECGs (AUC = 0.95). When interpreted by cardiologists, reconstructed ECGs achieved an accuracy of 81.4 ± 5.0% in identifying ECG features of ST-segment elevation MI, comparable with the original 12-lead ECGs (accuracy 84.6 ± 4.6%). These results will impact development efforts to innovate ECG acquisition methods with simplified tools in non-specialized settings.
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Affiliation(s)
- Federico Mason
- Scripps Research Translational Institute, La Jolla, 92037, CA, USA
- Department of Information Engineering, University of Padova, Padova, 35131, Italy
| | - Amitabh C Pandey
- Scripps Research Translational Institute, La Jolla, 92037, CA, USA
- Scripps Clinic, La Jolla, 92037, CA, USA
- Tulane University School of Medicine, New Orleans, 70122, LA, USA
| | - Matteo Gadaleta
- Scripps Research Translational Institute, La Jolla, 92037, CA, USA
| | - Eric J Topol
- Scripps Research Translational Institute, La Jolla, 92037, CA, USA
- Scripps Clinic, La Jolla, 92037, CA, USA
| | - Evan D Muse
- Scripps Research Translational Institute, La Jolla, 92037, CA, USA.
- Scripps Clinic, La Jolla, 92037, CA, USA.
| | - Giorgio Quer
- Scripps Research Translational Institute, La Jolla, 92037, CA, USA.
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2
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Savostin A, Koshekov K, Ritter Y, Savostina G, Ritter D. 12-Lead ECG Reconstruction Based on Data From the First Limb Lead. Cardiovasc Eng Technol 2024; 15:346-358. [PMID: 38424391 DOI: 10.1007/s13239-024-00719-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Accepted: 01/11/2024] [Indexed: 03/02/2024]
Abstract
PURPOSE Electrocardiogram (ECG) data obtained from 12 leads are the most common and informative source for analyzing the cardiovascular system's (CVS) condition in medical practice. However, the large number of electrodes, specific placements on the body, and the need for specialized equipment make the ECG acquisition procedure complex and cumbersome. This raises the challenge of reducing the number of ECG leads by reconstructing missing leads based on available data. METHODS Most existing methods for reconstructing missing ECG leads rely on utilizing signals simultaneously from multiple known leads. This study proposes a method for reconstructing ECG data in 12 leads using signal data from the first lead, lead I. Such an approach can significantly simplify the ECG registration procedure. The study demonstrates the effectiveness of using unique models with a developed architecture of artificial neural networks (ANNs) to generate the reconstructed ECG signals. Fragments of ECG from lead I, with a duration of 128 samples and a sampling frequency of 100 Hz, are input to the models. ECG fragments can be extracted from the original signal at arbitrary time points. Each model generates an ECG signal of the same length at its output for the corresponding lead. RESULTS Despite existing limitations, the proposed method surpasses known solutions regarding ECG generation quality when using a single lead. The study shows that introducing an additional feature of the direction of the electrical axis of the heart (EAH) as input to the ANN models enhances the generation quality. The quality of ECG generation by the proposed ANN models is found to be dependent on the presence of CVS diseases. CONCLUSIONS The developed ECG reconstruction method holds significant potential for use in portable registration devices, screening procedures, and providing support for medical decision-making by healthcare specialists.
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Affiliation(s)
- Alexey Savostin
- M. Kozybayev North Kazakhstan University, Petropavlovsk, Republic of Kazakhstan
| | | | - Yekaterina Ritter
- M. Kozybayev North Kazakhstan University, Petropavlovsk, Republic of Kazakhstan
| | - Galina Savostina
- M. Kozybayev North Kazakhstan University, Petropavlovsk, Republic of Kazakhstan.
| | - Dmitriy Ritter
- M. Kozybayev North Kazakhstan University, Petropavlovsk, Republic of Kazakhstan
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Mason F, Pandey AC, Gadaleta M, Topol EJ, Muse ED, Quer G. AI-Enhanced Reconstruction of the 12-Lead Electrocardiogram via 3-Leads with Accurate Clinical Assessment. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.01.30.24302001. [PMID: 38352465 PMCID: PMC10862987 DOI: 10.1101/2024.01.30.24302001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/27/2024]
Abstract
The 12-lead electrocardiogram (ECG) is an integral component to the diagnosis of a multitude of cardiovascular conditions. It is performed using a complex set of skin surface electrodes, limiting its use outside traditional clinical settings. We developed an artificial intelligence algorithm, trained over 600,000 clinically acquired ECGs, to explore whether fewer leads as input are sufficient to reconstruct a full 12-lead ECG. Two limb leads (I and II) and one precordial lead (V3) were required to generate a reconstructed synthetic 12-lead ECG highly correlated with the original ECG. An automatic algorithm for detection of acute myocardial infarction (MI) performed similarly for original and reconstructed ECGs (AUC=0.94). When interpreted by cardiologists, reconstructed ECGs achieved an accuracy of 81.4±5.0% in identifying ST elevation MI, comparable with the original 12-lead ECGs (accuracy 84.6±4.6%). These results will impact development efforts to innovate ECG acquisition methods with simplified tools in non-specialized settings.
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4
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Zheng Y, Chen Z, Huang S, Zhang N, Wang Y, Hong S, Chan JSK, Chen KY, Xia Y, Zhang Y, Lip GY, Qin J, Tse G, Liu T. Machine Learning in Cardio-Oncology: New Insights from an Emerging Discipline. Rev Cardiovasc Med 2023; 24:296. [PMID: 39077576 PMCID: PMC11273149 DOI: 10.31083/j.rcm2410296] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2023] [Revised: 05/13/2023] [Accepted: 05/16/2023] [Indexed: 07/31/2024] Open
Abstract
A growing body of evidence on a wide spectrum of adverse cardiac events following oncologic therapies has led to the emergence of cardio-oncology as an increasingly relevant interdisciplinary specialty. This also calls for better risk-stratification for patients undergoing cancer treatment. Machine learning (ML), a popular branch discipline of artificial intelligence that tackles complex big data problems by identifying interaction patterns among variables, has seen increasing usage in cardio-oncology studies for risk stratification. The objective of this comprehensive review is to outline the application of ML approaches in cardio-oncology, including deep learning, artificial neural networks, random forest and summarize the cardiotoxicity identified by ML. The current literature shows that ML has been applied for the prediction, diagnosis and treatment of cardiotoxicity in cancer patients. In addition, role of ML in gender and racial disparities for cardiac outcomes and potential future directions of cardio-oncology are discussed. It is essential to establish dedicated multidisciplinary teams in the hospital and educate medical professionals to become familiar and proficient in ML in the future.
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Affiliation(s)
- Yi Zheng
- Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular
Disease, Department of Cardiology, Tianjin Institute of Cardiology, Second
Hospital of Tianjin Medical University, 300211 Tianjin, China
| | - Ziliang Chen
- Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular
Disease, Department of Cardiology, Tianjin Institute of Cardiology, Second
Hospital of Tianjin Medical University, 300211 Tianjin, China
| | - Shan Huang
- Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular
Disease, Department of Cardiology, Tianjin Institute of Cardiology, Second
Hospital of Tianjin Medical University, 300211 Tianjin, China
| | - Nan Zhang
- Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular
Disease, Department of Cardiology, Tianjin Institute of Cardiology, Second
Hospital of Tianjin Medical University, 300211 Tianjin, China
| | - Yueying Wang
- Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular
Disease, Department of Cardiology, Tianjin Institute of Cardiology, Second
Hospital of Tianjin Medical University, 300211 Tianjin, China
| | - Shenda Hong
- National Institute of Health Data Science at Peking University, Peking
University, 100871 Beijing, China
- Institute of Medical Technology, Peking University Health Science Center,
100871 Beijing, China
| | - Jeffrey Shi Kai Chan
- Cardio-Oncology Research Unit, Cardiovascular Analytics Group, PowerHealth Limited, 999077 Hong
Kong, China
| | - Kang-Yin Chen
- Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular
Disease, Department of Cardiology, Tianjin Institute of Cardiology, Second
Hospital of Tianjin Medical University, 300211 Tianjin, China
| | - Yunlong Xia
- Department of Cardiology, First Affiliated Hospital of Dalian Medical
University, 116011 Dalian, Liaoning, China
| | - Yuhui Zhang
- Heart Failure Center, State Key Laboratory of Cardiovascular Disease,
Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of
Medical Sciences and Peking Union Medical College, 100037 Beijing, China
| | - Gregory Y.H. Lip
- Liverpool Centre for Cardiovascular Science, University of Liverpool,
Liverpool John Moores University and Liverpool Heart & Chest Hospital, L69 3BX
Liverpool, UK
- Danish Center for Health Services Research, Department of Clinical Medicine,
Aalborg University, 999017 Aalborg, Denmark
| | - Juan Qin
- Section of Cardio-Oncology & Immunology, Division of Cardiology and the
Cardiovascular Research Institute, University of California San Francisco, San
Francisco, CA 94143, USA
| | - Gary Tse
- Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular
Disease, Department of Cardiology, Tianjin Institute of Cardiology, Second
Hospital of Tianjin Medical University, 300211 Tianjin, China
- Cardio-Oncology Research Unit, Cardiovascular Analytics Group, PowerHealth Limited, 999077 Hong
Kong, China
- School of Nursing and Health Studies, Hong Kong Metropolitan University,
999077 Hong Kong, China
| | - Tong Liu
- Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular
Disease, Department of Cardiology, Tianjin Institute of Cardiology, Second
Hospital of Tianjin Medical University, 300211 Tianjin, China
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EPMoghaddam D, Banta A, Post A, Razavi M, Aazhang B. A novel method for 12-lead ECG reconstruction. CONFERENCE RECORD. ASILOMAR CONFERENCE ON SIGNALS, SYSTEMS & COMPUTERS 2023; 2023:1054-1058. [PMID: 39286539 PMCID: PMC11404295 DOI: 10.1109/ieeeconf59524.2023.10476822] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/19/2024]
Abstract
This paper presents a novel approach to synthesize a standard 12-lead electrocardiogram (ECG) from any three independent ECG leads using a patient-specific encoder-decoder convolutional neural network. The objective is to decrease the number of recording locations required to obtain the same information as a 12-lead ECG, thereby enhancing patients' comfort during the recording process. We evaluate the proposed algorithm on a dataset comprising fifteen patients, as well as a randomly selected cohort of patients from the PTB diagnostic database. To evaluate the precision of the reconstructed ECG signals, we present two metrics: the correlation coefficient and root mean square error. Our proposed method achieves superior performance compared to most existing synthesis techniques, with an average correlation coefficient of 0.976 and 0.97 for datasets, respectively. These results demonstrate the potential of our approach to improve the efficiency and comfort of ECG recording for patients, while maintaining high diagnostic accuracy.
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Affiliation(s)
- Dorsa EPMoghaddam
- Department of Electrical and Computer Engineering, Rice University, Houston, United States of America
| | - Anton Banta
- Department of Electrical and Computer Engineering, Rice University, Houston, United States of America
| | - Allison Post
- Electrophysiology Clinical Research and Innovations, Texas Heart Institute, Houston, United States of America
| | - Mehdi Razavi
- Department of Cardiology, Texas Heart Institute, Houston, United States of America
| | - Behnaam Aazhang
- Department of Electrical and Computer Engineering, Rice University, Houston, United States of America
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van der Zande J, Strik M, Dubois R, Ploux S, Alrub SA, Caillol T, Nasarre M, Donker DW, Oppersma E, Bordachar P. Using a Smartwatch to Record Precordial Electrocardiograms: A Validation Study. SENSORS (BASEL, SWITZERLAND) 2023; 23:2555. [PMID: 36904759 PMCID: PMC10007514 DOI: 10.3390/s23052555] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Revised: 02/21/2023] [Accepted: 02/21/2023] [Indexed: 06/18/2023]
Abstract
Smartwatches that support the recording of a single-lead electrocardiogram (ECG) are increasingly being used beyond the wrist, by placement on the ankle and on the chest. However, the reliability of frontal and precordial ECGs other than lead I is unknown. This clinical validation study assessed the reliability of an Apple Watch (AW) to obtain conventional frontal and precordial leads as compared to standard 12-lead ECGs in both subjects without known cardiac anomalies and patients with underlying heart disease. In 200 subjects (67% with ECG anomalies), a standard 12-lead ECG was performed, followed by AW recordings of the standard Einthoven leads (leads I, II, and III) and precordial leads V1, V3, and V6. Seven parameters (P, QRS, ST, and T-wave amplitudes, PR, QRS, and QT intervals) were compared through a Bland-Altman analysis, including the bias, absolute offset, and 95% limits of agreement. AW-ECGs recorded on the wrist but also beyond the wrist had similar durations and amplitudes compared to standard 12-lead ECGs. Significantly greater amplitudes were measured by the AW for R-waves in precordial leads V1, V3, and V6 (+0.094 mV, +0.149 mV, +0.129 mV, respectively, all p < 0.001), indicating a positive bias for the AW. AW can be used to record frontal, and precordial ECG leads, paving the way for broader clinical applications.
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Affiliation(s)
- Joske van der Zande
- IHU Liryc, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux Université, F-33600 Pessac, France
- Cardio-Thoracic Unit, Bordeaux University Hospital (CHU), F-33600 Pessac, France
- Cardiovascular and Respiratory Physiology, TechMed Centre, University of Twente, 7522 NB Enschede, The Netherlands
| | - Marc Strik
- IHU Liryc, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux Université, F-33600 Pessac, France
- Cardio-Thoracic Unit, Bordeaux University Hospital (CHU), F-33600 Pessac, France
| | - Rémi Dubois
- IHU Liryc, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux Université, F-33600 Pessac, France
- Cardio-Thoracic Unit, Bordeaux University Hospital (CHU), F-33600 Pessac, France
| | - Sylvain Ploux
- IHU Liryc, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux Université, F-33600 Pessac, France
- Cardio-Thoracic Unit, Bordeaux University Hospital (CHU), F-33600 Pessac, France
| | - Saer Abu Alrub
- Cardio-Thoracic Unit, Bordeaux University Hospital (CHU), F-33600 Pessac, France
- Cardiology Department, Clermont Universite, Université d’Auvergne, Cardio Vascular Interventional Therapy and Imaging (CaVITI), Image Science for Interventional Techniques (ISIT), UMR6284, CHU Clermont-Ferrand, F-63003 Clermont-Ferrand, France
| | - Théo Caillol
- IHU Liryc, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux Université, F-33600 Pessac, France
- Cardio-Thoracic Unit, Bordeaux University Hospital (CHU), F-33600 Pessac, France
| | - Mathieu Nasarre
- Cardio-Thoracic Unit, Bordeaux University Hospital (CHU), F-33600 Pessac, France
| | - Dirk W. Donker
- Cardiovascular and Respiratory Physiology, TechMed Centre, University of Twente, 7522 NB Enschede, The Netherlands
| | - Eline Oppersma
- Cardiovascular and Respiratory Physiology, TechMed Centre, University of Twente, 7522 NB Enschede, The Netherlands
| | - Pierre Bordachar
- IHU Liryc, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux Université, F-33600 Pessac, France
- Cardio-Thoracic Unit, Bordeaux University Hospital (CHU), F-33600 Pessac, France
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7
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Beco SC, Pinto JR, Cardoso JS. Electrocardiogram lead conversion from single-lead blindly-segmented signals. BMC Med Inform Decis Mak 2022; 22:314. [PMID: 36447207 PMCID: PMC9710059 DOI: 10.1186/s12911-022-02063-6] [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: 10/21/2022] [Accepted: 11/22/2022] [Indexed: 12/02/2022] Open
Abstract
BACKGROUND The standard configuration's set of twelve electrocardiogram (ECG) leads is optimal for the medical diagnosis of diverse cardiac conditions. However, it requires ten electrodes on the patient's limbs and chest, which is uncomfortable and cumbersome. Interlead conversion methods can reconstruct missing leads and enable more comfortable acquisitions, including in wearable devices, while still allowing for adequate diagnoses. Currently, methodologies for interlead ECG conversion either require multiple reference (input) leads and/or require input signals to be temporally aligned considering the ECG landmarks. METHODS Unlike the methods in the literature, this paper studies the possibility of converting ECG signals into all twelve standard configuration leads using signal segments from only one reference lead, without temporal alignment (blindly-segmented). The proposed methodology is based on a deep learning encoder-decoder U-Net architecture, which is compared with adaptations based on convolutional autoencoders and label refinement networks. Moreover, the method is explored for conversion with one single shared encoder or multiple individual encoders for each lead. RESULTS Despite the more challenging settings, the proposed methodology was able to attain state-of-the-art level performance in multiple target leads, and both lead I and lead II seem especially suitable to convert certain sets of leads. In cross-database tests, the methodology offered promising results despite acquisition setup differences. Furthermore, results show that the presence of medical conditions does not have a considerable effect on the method's performance. CONCLUSIONS This study shows the feasibility of converting ECG signals using single-lead blindly-segmented inputs. Although the results are promising, further efforts should be devoted towards the improvement of the methodologies, especially the robustness to diverse acquisition setups, in order to be applicable to cardiac health monitoring in wearable devices and less obtrusive clinical scenarios.
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Affiliation(s)
- Sofia C. Beco
- grid.20384.3d0000 0004 0500 6380Centre for Telecommunications and Multimedia, INESC TEC, Porto, Portugal ,grid.5808.50000 0001 1503 7226Faculdade de Engenharia, Universidade do Porto, Porto, Portugal
| | - João Ribeiro Pinto
- grid.20384.3d0000 0004 0500 6380Centre for Telecommunications and Multimedia, INESC TEC, Porto, Portugal ,grid.5808.50000 0001 1503 7226Faculdade de Engenharia, Universidade do Porto, Porto, Portugal
| | - Jaime S. Cardoso
- grid.20384.3d0000 0004 0500 6380Centre for Telecommunications and Multimedia, INESC TEC, Porto, Portugal ,grid.5808.50000 0001 1503 7226Faculdade de Engenharia, Universidade do Porto, Porto, Portugal
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8
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Santos Rodrigues A, Augustauskas R, Lukoševičius M, Laguna P, Marozas V. Deep-Learning-Based Estimation of the Spatial QRS-T Angle from Reduced-Lead ECGs. SENSORS (BASEL, SWITZERLAND) 2022; 22:5414. [PMID: 35891094 PMCID: PMC9328169 DOI: 10.3390/s22145414] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 07/16/2022] [Accepted: 07/18/2022] [Indexed: 06/15/2023]
Abstract
The spatial QRS-T angle is a promising health indicator for risk stratification of sudden cardiac death (SCD). Thus far, the angle is estimated solely from 12-lead electrocardiogram (ECG) systems uncomfortable for ambulatory monitoring. Methods to estimate QRS-T angles from reduced-lead ECGs registered with consumer healthcare devices would, therefore, facilitate ambulatory monitoring. (1) Objective: Develop a method to estimate spatial QRS-T angles from reduced-lead ECGs. (2) Approach: We designed a deep learning model to locate the QRS and T wave vectors necessary for computing the QRS-T angle. We implemented an original loss function to guide the model in the 3D space to search for each vector's coordinates. A gradual reduction of ECG leads from the largest publicly available dataset of clinical 12-lead ECG recordings (PTB-XL) is used for training and validation. (3) Results: The spatial QRS-T angle can be estimated from leads {I, II, aVF, V2} with sufficient accuracy (absolute mean and median errors of 11.4° and 7.3°) for detecting abnormal angles without sacrificing patient comfortability. (4) Significance: Our model could enable ambulatory monitoring of spatial QRS-T angles using patch- or textile-based ECG devices. Populations at risk of SCD, like chronic cardiac and kidney disease patients, might benefit from this technology.
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Affiliation(s)
- Ana Santos Rodrigues
- Biomedical Engineering Institute, Kaunas University of Technology, 51423 Kaunas, Lithuania;
| | - Rytis Augustauskas
- Department of Automation, Kaunas University of Technology, 51367 Kaunas, Lithuania;
| | - Mantas Lukoševičius
- Faculty of Informatics, Kaunas University of Technology, 51368 Kaunas, Lithuania;
| | - Pablo Laguna
- Biomedical Signal Interpretation and Computational Simulation (BSICoS) Group, Aragón Institute of Engineering Research (I3A), IIS Aragón, University of Zaragoza, 50018 Zaragoza, Spain;
- Biomedical Research Networking Center (CIBER), 50018 Zaragoza, Spain
| | - Vaidotas Marozas
- Biomedical Engineering Institute, Kaunas University of Technology, 51423 Kaunas, Lithuania;
- Faculty of Electrical and Electronics Engineering, Kaunas University of Technology, 51367 Kaunas, Lithuania
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Sprenger N, Sepehri Shamloo A, Schäfer J, Burkhardt S, Mouratis K, Hindricks G, Bollmann A, Arya A. Feasibility and Reliability of Smartwatch to Obtain Precordial Lead Electrocardiogram Recordings. SENSORS 2022; 22:s22031217. [PMID: 35161960 PMCID: PMC8839669 DOI: 10.3390/s22031217] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Revised: 02/02/2022] [Accepted: 02/03/2022] [Indexed: 12/02/2022]
Abstract
The Apple Watch is capable of recording single-lead electrocardiograms (ECGs). To incorporate such devices in routine medical care, the reliability of such devices to obtain precordial leads needs to be validated. The purpose of this study was to assess the feasibility and reliability of a smartwatch (SW) to obtain precordial leads compared to standard ECGs. We included 100 participants (62 male, aged 62.8 ± 13.1 years) with sinus rhythm and recorded a standard 12-lead ECG and the precordial leads with the Apple Watch. The ECGs were quantitively compared. A total of 98 patients were able to record precordial leads without assistance. A strong correlation was observed between the amplitude of the standard and SW-ECGs’ waves, in terms of P waves, QRS-complexes, and T waves (all p-values < 0.01). A significant correlation was observed between the two methods regarding the duration of the ECG waves (all p-values < 0.01). Assessment of polarity showed a significant and a strong concordance between the ECGs’ waves in all six leads (91–100%, all p-values < 0.001). In conclusion, 98% of patients were able to record precordial leads using a SW without assistance. The SW is feasible and reliable for obtaining valid precordial-lead ECG recordings as a validated alternative to a standard ECG.
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Affiliation(s)
- Nora Sprenger
- Department of Electrophysiology, Heart Center Leipzig, University of Leipzig, 04289 Leipzig, Germany; (A.S.S.); (J.S.); (G.H.); (A.B.); (A.A.)
- Correspondence: ; Tel.: +49-341-8651413
| | - Alireza Sepehri Shamloo
- Department of Electrophysiology, Heart Center Leipzig, University of Leipzig, 04289 Leipzig, Germany; (A.S.S.); (J.S.); (G.H.); (A.B.); (A.A.)
- Leipzig Heart Digital, Leipzig Heart Institute, 04289 Leipzig, Germany;
| | - Jonathan Schäfer
- Department of Electrophysiology, Heart Center Leipzig, University of Leipzig, 04289 Leipzig, Germany; (A.S.S.); (J.S.); (G.H.); (A.B.); (A.A.)
| | - Sarah Burkhardt
- Institute of Therapy and Organizational Development, 10961 Berlin, Germany;
| | | | - Gerhard Hindricks
- Department of Electrophysiology, Heart Center Leipzig, University of Leipzig, 04289 Leipzig, Germany; (A.S.S.); (J.S.); (G.H.); (A.B.); (A.A.)
| | - Andreas Bollmann
- Department of Electrophysiology, Heart Center Leipzig, University of Leipzig, 04289 Leipzig, Germany; (A.S.S.); (J.S.); (G.H.); (A.B.); (A.A.)
- Leipzig Heart Digital, Leipzig Heart Institute, 04289 Leipzig, Germany;
| | - Arash Arya
- Department of Electrophysiology, Heart Center Leipzig, University of Leipzig, 04289 Leipzig, Germany; (A.S.S.); (J.S.); (G.H.); (A.B.); (A.A.)
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