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Duca ȘT, Tudorancea I, Haba MȘC, Costache AD, Șerban IL, Pavăl DR, Loghin C, Costache-Enache II. Enhancing Comprehensive Assessments in Chronic Heart Failure Caused by Ischemic Heart Disease: The Diagnostic Utility of Holter ECG Parameters. MEDICINA (KAUNAS, LITHUANIA) 2024; 60:1315. [PMID: 39202596 PMCID: PMC11356511 DOI: 10.3390/medicina60081315] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/14/2024] [Revised: 07/31/2024] [Accepted: 08/13/2024] [Indexed: 09/03/2024]
Abstract
Background and Objectives: Chronic heart failure (CHF) caused by ischemic heart disease (IHD) is the leading cause of death worldwide and presents significant health challenges. Effective management of IHD requires prevention, early detection, and treatment to improve patient outcomes. This study aims to expand the diagnostic utility of various 24 h Holter ECG parameters, such as T-wave alternans (TWA), late ventricular potentials (LVPs), and heart rate variability (HRV) in patients with CHF caused by IHD. Additionally, we seek to explore the association between these parameters and other comorbid conditions affecting the prognosis of CHF patients. Materials and Methods: We conducted a prospective case-control study with 150 patients divided into two subgroups: 100 patients with CHF caused by IHD, and 50 patients in the control group. Data included medical history, physical examination, laboratory tests, echocardiography, and 24 h Holter monitoring. Results: Our comparative analysis demonstrated that both TWA and LVPs were significantly higher in patients with CHF compared to the control group (p < 0.01), indicating increased myocardial electrical vulnerability in CHF patients. Both time and frequency-domain HRV parameters were significantly lower in the CHF group. However, the ratio of NN50 to the total count of NN intervals (PNN50) showed a borderline significance (p = 0.06). While the low-frequency (LF) domain was significantly lower in CHF patients, the high-frequency (HF) domain did not differ significantly between groups. Acceleration and deceleration capacities were also significantly altered in CHF patients. Categorizing CHF patients by left ventricular ejection fraction (LVEF) revealed that the mean of the 5-min normal-to-normal intervals over the complete recording (SDNN Index) was significantly higher in patients with LVEF ≥ 50% compared to those with CHF with reduced EF and CHF with mildly reduced EF (p < 0.001), whereas the other HRV parameters showed no significant differences among the groups. Conclusions: Holter ECG parameters can become a reliable tool in the assessment of patients with CHF. The integration of multiple Holter ECG parameters, such as TWA, LVPs, and HRV, can significantly enhance the diagnostic assessment of CHF caused by IHD. This comprehensive approach allows for a more nuanced understanding of the patient's condition and potential outcomes.
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Affiliation(s)
- Ștefania-Teodora Duca
- Department of Internal Medicine I, Faculty of Medicine, University of Medicine and Pharmacy “Grigore T. Popa”, 700115 Iasi, Romania; (M.Ș.C.H.); (A.-D.C.); (I.-I.C.-E.)
- Department of Cardiology, “St. Spiridon” Emergency County Hospital, 700111 Iasi, Romania;
| | - Ionuț Tudorancea
- Department of Cardiology, “St. Spiridon” Emergency County Hospital, 700111 Iasi, Romania;
- Department of Morpho-Functional Science II-Physiology, University of Medicine and Pharmacy “Grigore T. Popa”, 700115 Iasi, Romania;
| | - Mihai Ștefan Cristian Haba
- Department of Internal Medicine I, Faculty of Medicine, University of Medicine and Pharmacy “Grigore T. Popa”, 700115 Iasi, Romania; (M.Ș.C.H.); (A.-D.C.); (I.-I.C.-E.)
- Department of Cardiology, “St. Spiridon” Emergency County Hospital, 700111 Iasi, Romania;
| | - Alexandru-Dan Costache
- Department of Internal Medicine I, Faculty of Medicine, University of Medicine and Pharmacy “Grigore T. Popa”, 700115 Iasi, Romania; (M.Ș.C.H.); (A.-D.C.); (I.-I.C.-E.)
- Department of Cardiovascular Rehabilitation, Clinical Rehabilitation Hospital, 700661 Iasi, Romania
| | - Ionela-Lăcrămioara Șerban
- Department of Morpho-Functional Science II-Physiology, University of Medicine and Pharmacy “Grigore T. Popa”, 700115 Iasi, Romania;
| | - D. Robert Pavăl
- Faculty of Health Sciences and Sport, University of Stirling, Stirling FK9 4LA, UK;
| | - Cătălin Loghin
- Department of Internal Medicine, Cardiology Division, University of Texas Health Science Center, Houston, TX 77030, USA;
| | - Irina-Iuliana Costache-Enache
- Department of Internal Medicine I, Faculty of Medicine, University of Medicine and Pharmacy “Grigore T. Popa”, 700115 Iasi, Romania; (M.Ș.C.H.); (A.-D.C.); (I.-I.C.-E.)
- Department of Cardiology, “St. Spiridon” Emergency County Hospital, 700111 Iasi, Romania;
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Que W, Bian Y, Chen S, Zhao X, Ji Z, Hu P, Han C, Shi L. Efficient electrocardiogram generation based on cardiac electric vector simulation model. Comput Biol Med 2024; 177:108629. [PMID: 38820778 DOI: 10.1016/j.compbiomed.2024.108629] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Revised: 04/27/2024] [Accepted: 05/18/2024] [Indexed: 06/02/2024]
Abstract
This study introduces a novel Cardiac Electric Vector Simulation Model (CEVSM) to address the computational inefficiencies and low fidelity of traditional electrophysiological models in generating electrocardiograms (ECGs). Our approach leverages CEVSM to efficiently produce reliable ECG samples, facilitating data augmentation essential for the computer-aided diagnosis of myocardial infarction (MI). Significantly, experimental results show that our model dramatically reduces computation time compared to conventional models, with the self-adapting regression transformation matrix method (SRTM) providing clear advantages. SRTM not only achieves high fidelity in ECG simulations but also ensures exceptional consistency with the gold standard method, greatly enhancing MI localization accuracy by data augmentation. These advancements highlight the potential of our model to generate dependable ECG training samples, making it highly suitable for data augmentation and significantly advancing the development and validation of intelligent MI diagnostic systems. Furthermore, this study demonstrates the feasibility of applying life system simulations in the training of medical big models.
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Affiliation(s)
- Wenge Que
- Department of Automation, Tsinghua University, Beijing, 100084, China.
| | - Yingnan Bian
- School of Logistics, Henan College of Transportation, Zhengzhou, 450000, China.
| | - Shengjie Chen
- Department of Automation, Tsinghua University, Beijing, 100084, China.
| | - Xiliang Zhao
- Center for Coronary Artery Disease, Division of Cardiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, 100029, China.
| | - Zehua Ji
- Department of Automation, Tsinghua University, Beijing, 100084, China.
| | - Pingge Hu
- Department of Automation, Tsinghua University, Beijing, 100084, China.
| | - Chuang Han
- School of Computer Science and Technology, Zhengzhou University of Light Industry, Zhengzhou, 450000, China.
| | - Li Shi
- Department of Automation, Tsinghua University, Beijing, 100084, China; Beijing National Research Center for Information Science and Technology, Beijing, 100084, China.
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Kijonka J, Vavra P, Penhaker M, Bibbo D, Kudrna P, Kubicek J. Present results and methods of vectorcardiographic diagnostics of ischemic heart disease. Comput Biol Med 2024; 169:107781. [PMID: 38103481 DOI: 10.1016/j.compbiomed.2023.107781] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Revised: 11/03/2023] [Accepted: 11/28/2023] [Indexed: 12/19/2023]
Abstract
This article presents an overview of existing approaches to perform vectorcardiographic (VCG) diagnostics of ischemic heart disease (IHD). Individual methodologies are divided into categories to create a comprehensive and clear overview of electrical cardiac activity measurement, signal pre-processing, features extraction and classification procedures. An emphasis is placed on methods describing the electrical heart space (EHS) by several features extraction techniques based on spatiotemporal characteristics or signal modelling and signal transformations. Performance of individual methodologies are compared depending on classification of extent of ischemia, acute forms - myocardial infarction (MI) and myocardial scars localization. Based on a comparison of imaging methods, the advantages of VCG over the standard 12-leads ECG such as providing a 3D orthogonal leads imaging, better performance, and appropriate computer processing are highlighted. The issues of electrical cardiac activity measurements on body surface, the lack of VKG databases supported by a more accurate imaging method, possibility of comparison with the physiology of individual cases are outlined as potential reserves for future research.
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Affiliation(s)
- Jan Kijonka
- Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB - Technical University of Ostrava, 17.listopadu 15, Ostrava, Poruba, 708 00, Czech Republic.
| | - Petr Vavra
- Department of Surgical Studies, Faculty of Medicine of the University of Ostrava, Syllabova 19, 703 00, Ostrava 3, Czech Republic; Surgery Clinic, University Hospital Ostrava, 17. listopadu 13, Ostrava, Czech Republic.
| | - Marek Penhaker
- Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB - Technical University of Ostrava, 17.listopadu 15, Ostrava, Poruba, 708 00, Czech Republic; Faculty of Electrical Engineering and Information Technology, University of Zilina, Zilina, Czech Republic.
| | - Daniele Bibbo
- Department of Industrial, Electronic and Mechanical Engineering, Roma Tre University, Via Vito Volterra, 62, 00146, Rome, Italy.
| | - Petr Kudrna
- Department of Biomedical Technology, Faculty of Biomedical Engineering, Czech Technical University in Prague, Nam. Sitna 3105, 272 01, Kladno, Czech Republic.
| | - Jan Kubicek
- Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB - Technical University of Ostrava, 17.listopadu 15, Ostrava, Poruba, 708 00, Czech Republic.
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Mehri M, Calmon G, Odille F, Oster J. A Deep Learning Architecture Using 3D Vectorcardiogram to Detect R-Peaks in ECG with Enhanced Precision. SENSORS (BASEL, SWITZERLAND) 2023; 23:2288. [PMID: 36850889 PMCID: PMC9963088 DOI: 10.3390/s23042288] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Revised: 02/14/2023] [Accepted: 02/15/2023] [Indexed: 06/18/2023]
Abstract
Providing reliable detection of QRS complexes is key in automated analyses of electrocardiograms (ECG). Accurate and timely R-peak detections provide a basis for ECG-based diagnoses and to synchronize radiologic, electrophysiologic, or other medical devices. Compared with classical algorithms, deep learning (DL) architectures have demonstrated superior accuracy and high generalization capacity. Furthermore, they can be embedded on edge devices for real-time inference. 3D vectorcardiograms (VCG) provide a unifying framework for detecting R-peaks regardless of the acquisition strategy or number of ECG leads. In this article, a DL architecture was demonstrated to provide enhanced precision when trained and applied on 3D VCG, with no pre-processing nor post-processing steps. Experiments were conducted on four different public databases. Using the proposed approach, high F1-scores of 99.80% and 99.64% were achieved in leave-one-out cross-validation and cross-database validation protocols, respectively. False detections, measured by a precision of 99.88% or more, were significantly reduced compared with recent state-of-the-art methods tested on the same databases, without penalty in the number of missed peaks, measured by a recall of 99.39% or more. This approach can provide new applications for devices where precision, or positive predictive value, is essential, for instance cardiac magnetic resonance imaging.
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Affiliation(s)
- Maroua Mehri
- Epsidy, 54000 Nancy, France
- Ecole Nationale d’Ingénieurs de Sousse, LATIS-Laboratory of Advanced Technology and Intelligent Systems, Université de Sousse, Sousse 4023, Tunisia
| | | | - Freddy Odille
- Epsidy, 54000 Nancy, France
- IADI-Imagerie Adaptative Diagnostique et Interventionnelle, Inserm U1254, Université de Lorraine, 54000 Nancy, France
- CIC-IT 1433, Inserm, CHRU de Nancy, Université de Lorraine, 54000 Nancy, France
| | - Julien Oster
- IADI-Imagerie Adaptative Diagnostique et Interventionnelle, Inserm U1254, Université de Lorraine, 54000 Nancy, France
- CIC-IT 1433, Inserm, CHRU de Nancy, Université de Lorraine, 54000 Nancy, France
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Moradi H, Al-Hourani A, Concilia G, Khoshmanesh F, Nezami FR, Needham S, Baratchi S, Khoshmanesh K. Recent developments in modeling, imaging, and monitoring of cardiovascular diseases using machine learning. Biophys Rev 2023; 15:19-33. [PMID: 36909958 PMCID: PMC9995635 DOI: 10.1007/s12551-022-01040-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Accepted: 12/21/2022] [Indexed: 01/12/2023] Open
Abstract
Cardiovascular diseases are the leading cause of mortality, morbidity, and hospitalization around the world. Recent technological advances have facilitated analyzing, visualizing, and monitoring cardiovascular diseases using emerging computational fluid dynamics, blood flow imaging, and wearable sensing technologies. Yet, computational cost, limited spatiotemporal resolution, and obstacles for thorough data analysis have hindered the utility of such techniques to curb cardiovascular diseases. We herein discuss how leveraging machine learning techniques, and in particular deep learning methods, could overcome these limitations and offer promise for translation. We discuss the remarkable capacity of recently developed machine learning techniques to accelerate flow modeling, enhance the resolution while reduce the noise and scanning time of current blood flow imaging techniques, and accurate detection of cardiovascular diseases using a plethora of data collected by wearable sensors.
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Affiliation(s)
- Hamed Moradi
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
| | - Akram Al-Hourani
- School of Engineering, RMIT University, Melbourne, Victoria Australia
| | | | - Farnaz Khoshmanesh
- School of Allied Health, Human Services & Sport, La Trobe University, Melbourne, Victoria Australia
| | - Farhad R. Nezami
- Division of Thoracic and Cardiac Surgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA USA
| | - Scott Needham
- Leading Technology Group, Melbourne, Victoria Australia
| | - Sara Baratchi
- School of Health and Biomedical Sciences, RMIT University, Melbourne, Victoria Australia
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Vondrak J, Penhaker M. Review of Processing Pathological Vectorcardiographic Records for the Detection of Heart Disease. Front Physiol 2022; 13:856590. [PMID: 36213240 PMCID: PMC9536877 DOI: 10.3389/fphys.2022.856590] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Accepted: 03/04/2022] [Indexed: 11/23/2022] Open
Abstract
Vectorcardiography (VCG) is another useful method that provides us with useful spatial information about the electrical activity of the heart. The use of vectorcardiography in clinical practice is not common nowadays, mainly due to the well-established 12-lead ECG system. However, VCG leads can be derived from standard 12-lead ECG systems using mathematical transformations. These derived or directly measured VCG records have proven to be a useful tool for diagnosing various heart diseases such as myocardial infarction, ventricular hypertrophy, myocardial scars, long QT syndrome, etc., where standard ECG does not achieve reliable accuracy within automated detection. With the development of computer technology in recent years, vectorcardiography is beginning to come to the forefront again. In this review we highlight the analysis of VCG records within the extraction of functional parameters for the detection of heart disease. We focus on methods of processing VCG functionalities and their use in given pathologies. Improving or combining current or developing new advanced signal processing methods can contribute to better and earlier detection of heart disease. We also focus on the most commonly used methods to derive a VCG from 12-lead ECG.
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Affiliation(s)
- Jaroslav Vondrak
- Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, Ostrava, Czech Republic
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7
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Martinek R, Ladrova M, Sidikova M, Jaros R, Behbehani K, Kahankova R, Kawala-Sterniuk A. Advanced Bioelectrical Signal Processing Methods: Past, Present and Future Approach-Part I: Cardiac Signals. SENSORS (BASEL, SWITZERLAND) 2021; 21:5186. [PMID: 34372424 PMCID: PMC8346990 DOI: 10.3390/s21155186] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Revised: 07/23/2021] [Accepted: 07/26/2021] [Indexed: 11/30/2022]
Abstract
Advanced signal processing methods are one of the fastest developing scientific and technical areas of biomedical engineering with increasing usage in current clinical practice. This paper presents an extensive literature review of the methods for the digital signal processing of cardiac bioelectrical signals that are commonly applied in today's clinical practice. This work covers the definition of bioelectrical signals. It also covers to the extreme extent of classical and advanced approaches to the alleviation of noise contamination such as digital adaptive and non-adaptive filtering, signal decomposition methods based on blind source separation and wavelet transform.
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Affiliation(s)
- Radek Martinek
- FEECS, Department of Cybernetics and Biomedical Engineering, VSB-Technical University Ostrava, 708 00 Ostrava, Czech Republic; (M.L.); (M.S.); (R.J.); (R.K.)
| | - Martina Ladrova
- FEECS, Department of Cybernetics and Biomedical Engineering, VSB-Technical University Ostrava, 708 00 Ostrava, Czech Republic; (M.L.); (M.S.); (R.J.); (R.K.)
| | - Michaela Sidikova
- FEECS, Department of Cybernetics and Biomedical Engineering, VSB-Technical University Ostrava, 708 00 Ostrava, Czech Republic; (M.L.); (M.S.); (R.J.); (R.K.)
| | - Rene Jaros
- FEECS, Department of Cybernetics and Biomedical Engineering, VSB-Technical University Ostrava, 708 00 Ostrava, Czech Republic; (M.L.); (M.S.); (R.J.); (R.K.)
| | - Khosrow Behbehani
- College of Engineering, The University of Texas in Arlington, Arlington, TX 76019, USA;
| | - Radana Kahankova
- FEECS, Department of Cybernetics and Biomedical Engineering, VSB-Technical University Ostrava, 708 00 Ostrava, Czech Republic; (M.L.); (M.S.); (R.J.); (R.K.)
| | - Aleksandra Kawala-Sterniuk
- Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, 45-758 Opole, Poland
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Automatic Classification of Myocardial Infarction Using Spline Representation of Single-Lead Derived Vectorcardiography. SENSORS 2020; 20:s20247246. [PMID: 33348786 PMCID: PMC7767111 DOI: 10.3390/s20247246] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/18/2020] [Revised: 12/13/2020] [Accepted: 12/14/2020] [Indexed: 12/15/2022]
Abstract
Myocardial infarction (MI) is one of the most prevalent cardiovascular diseases worldwide and most patients suffer from MI without awareness. Therefore, early diagnosis and timely treatment are crucial to guarantee the life safety of MI patients. Most wearable monitoring devices only provide single-lead electrocardiography (ECG), which represents a major limitation for their applicability in diagnosis of MI. Incorporating the derived vectorcardiography (VCG) techniques can help monitor the three-dimensional electrical activities of human hearts. This study presents a patient-specific reconstruction method based on long short-term memory (LSTM) network to exploit both intra- and inter-lead correlations of ECG signals. MI-induced changes in the morphological and temporal wave features are extracted from the derived VCG using spline approximation. After the feature extraction, a classifier based on multilayer perceptron network is used for MI classification. Experiments on PTB diagnostic database demonstrate that the proposed system achieved satisfactory performance to differentiating MI patients from healthy subjects and to localizing the infarcted area.
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Feeny AK, Rickard J, Trulock KM, Patel D, Toro S, Moennich LA, Varma N, Niebauer MJ, Gorodeski EZ, Grimm RA, Barnard J, Madabhushi A, Chung MK. Machine Learning of 12-Lead QRS Waveforms to Identify Cardiac Resynchronization Therapy Patients With Differential Outcomes. Circ Arrhythm Electrophysiol 2020; 13:e008210. [PMID: 32538136 PMCID: PMC7901121 DOI: 10.1161/circep.119.008210] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
BACKGROUND Cardiac resynchronization therapy (CRT) improves heart failure outcomes but has significant nonresponse rates, highlighting limitations in ECG selection criteria: QRS duration (QRSd) ≥150 ms and subjective labeling of left bundle branch block (LBBB). We explored unsupervised machine learning of ECG waveforms to identify CRT subgroups that may differentiate outcomes beyond QRSd and LBBB. METHODS We retrospectively analyzed 946 CRT patients with conduction delay. Principal component analysis (PCA) dimensionality reduction obtained a 2-dimensional representation of preCRT 12-lead QRS waveforms. k-means clustering of the 2-dimensional PCA representation of 12-lead QRS waveforms identified 2 patient subgroups (QRS PCA groups). Vectorcardiographic QRS area was also calculated. We examined following 2 primary outcomes: (1) composite end point of death, left ventricular assist device, or heart transplant, and (2) degree of echocardiographic left ventricular ejection fraction (LVEF) change after CRT. RESULTS Compared with QRS PCA Group 2 (n=425), Group 1 (n=521) had lower risk for reaching the composite end point (HR, 0.44 [95% CI, 0.38-0.53]; P<0.001) and experienced greater mean LVEF improvement (11.1±11.7% versus 4.8±9.7%; P<0.001), even among patients with LBBB with QRSd ≥150 ms (HR, 0.42 [95% CI, 0.30-0.57]; P<0.001; mean LVEF change 12.5±11.8% versus 7.3±8.1%; P=0.001). QRS area also stratified outcomes but had significant differences from QRS PCA groups. A stratification scheme combining QRS area and QRS PCA group identified patients with LBBB with similar outcomes to non-LBBB patients (HR, 1.32 [95% CI, 0.93-1.62]; difference in mean LVEF change: 0.8% [95% CI, -2.1% to 3.7%]). The stratification scheme also identified patients with LBBB with QRSd <150 ms with comparable outcomes to patients with LBBB with QRSd ≥150 ms (HR, 0.93 [95% CI, 0.67-1.29]; difference in mean LVEF change: -0.2% [95% CI, -2.7% to 3.0%]). CONCLUSIONS Unsupervised machine learning of ECG waveforms identified CRT subgroups with relevance beyond LBBB and QRSd. This method may assist in objective classification of bundle branch block morphology in CRT.
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Affiliation(s)
- Albert K Feeny
- Cleveland Clinic Lerner College of Medicine (A.K.F., N.V., M.J.N., E.Z.G., R.A.G., J.B., M.K.C.), Case Western Reserve University, Cleveland, OH
| | - John Rickard
- Department of Cardiovascular Medicine, Heart and Vascular Institute (J.R., K.M.T., D.P., S.T., L.A.M., N.V., M.J.N., E.Z.G.), Cleveland Clinic, OH
| | - Kevin M Trulock
- Department of Cardiovascular Medicine, Heart and Vascular Institute (J.R., K.M.T., D.P., S.T., L.A.M., N.V., M.J.N., E.Z.G.), Cleveland Clinic, OH
| | - Divyang Patel
- Department of Cardiovascular Medicine, Heart and Vascular Institute (J.R., K.M.T., D.P., S.T., L.A.M., N.V., M.J.N., E.Z.G.), Cleveland Clinic, OH
| | - Saleem Toro
- Department of Cardiovascular Medicine, Heart and Vascular Institute (J.R., K.M.T., D.P., S.T., L.A.M., N.V., M.J.N., E.Z.G.), Cleveland Clinic, OH
| | - Laurie Ann Moennich
- Department of Cardiovascular Medicine, Heart and Vascular Institute (J.R., K.M.T., D.P., S.T., L.A.M., N.V., M.J.N., E.Z.G.), Cleveland Clinic, OH
| | - Niraj Varma
- Cleveland Clinic Lerner College of Medicine (A.K.F., N.V., M.J.N., E.Z.G., R.A.G., J.B., M.K.C.), Case Western Reserve University, Cleveland, OH
- Department of Cardiovascular Medicine, Heart and Vascular Institute (J.R., K.M.T., D.P., S.T., L.A.M., N.V., M.J.N., E.Z.G.), Cleveland Clinic, OH
| | - Mark J Niebauer
- Cleveland Clinic Lerner College of Medicine (A.K.F., N.V., M.J.N., E.Z.G., R.A.G., J.B., M.K.C.), Case Western Reserve University, Cleveland, OH
- Department of Cardiovascular Medicine, Heart and Vascular Institute (J.R., K.M.T., D.P., S.T., L.A.M., N.V., M.J.N., E.Z.G.), Cleveland Clinic, OH
| | - Eiran Z Gorodeski
- Cleveland Clinic Lerner College of Medicine (A.K.F., N.V., M.J.N., E.Z.G., R.A.G., J.B., M.K.C.), Case Western Reserve University, Cleveland, OH
- Department of Cardiovascular Medicine, Heart and Vascular Institute (J.R., K.M.T., D.P., S.T., L.A.M., N.V., M.J.N., E.Z.G.), Cleveland Clinic, OH
| | - Richard A Grimm
- Cleveland Clinic Lerner College of Medicine (A.K.F., N.V., M.J.N., E.Z.G., R.A.G., J.B., M.K.C.), Case Western Reserve University, Cleveland, OH
| | - John Barnard
- Cleveland Clinic Lerner College of Medicine (A.K.F., N.V., M.J.N., E.Z.G., R.A.G., J.B., M.K.C.), Case Western Reserve University, Cleveland, OH
- Department of Quantitative Health Sciences, Lerner Research Institute (J.B.), Cleveland Clinic, OH
| | - Anant Madabhushi
- Department of Biomedical Engineering (A.M.), Case Western Reserve University, Cleveland, OH
- Louis Stokes Cleveland Veterans Administration Medical Center, Cleveland, OH (A.M.)
| | - Mina K Chung
- Cleveland Clinic Lerner College of Medicine (A.K.F., N.V., M.J.N., E.Z.G., R.A.G., J.B., M.K.C.), Case Western Reserve University, Cleveland, OH
- Department of Cardiovascular and Metabolic Sciences, Lerner Research Institute (M.K.C.), Cleveland Clinic, OH
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Lee J, Oh K, Kim B, Yoo SK. Synthesis of Electrocardiogram V-Lead Signals From Limb-Lead Measurement Using R-Peak Aligned Generative Adversarial Network. IEEE J Biomed Health Inform 2020; 24:1265-1275. [DOI: 10.1109/jbhi.2019.2936583] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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Jaros R, Martinek R, Danys L. Comparison of Different Electrocardiography with Vectorcardiography Transformations. SENSORS (BASEL, SWITZERLAND) 2019; 19:E3072. [PMID: 31336798 PMCID: PMC6678609 DOI: 10.3390/s19143072] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/11/2019] [Revised: 07/04/2019] [Accepted: 07/09/2019] [Indexed: 12/01/2022]
Abstract
This paper deals with transformations from electrocardiographic (ECG) to vectorcardiographic (VCG) leads. VCG provides better sensitivity, for example for the detection of myocardial infarction, ischemia, and hypertrophy. However, in clinical practice, measurement of VCG is not usually used because it requires additional electrodes placed on the patient's body. Instead, mathematical transformations are used for deriving VCG from 12-leads ECG. In this work, Kors quasi-orthogonal transformation, inverse Dower transformation, Kors regression transformation, and linear regression-based transformations for deriving P wave (PLSV) and QRS complex (QLSV) are implemented and compared. These transformation methods were not yet compared before, so we have selected them for this paper. Transformation methods were compared for the data from the Physikalisch-Technische Bundesanstalt (PTB) database and their accuracy was evaluated using a mean squared error (MSE) and a correlation coefficient (R) between the derived and directly measured Frank's leads. Based on the statistical analysis, Kors regression transformation was significantly more accurate for the derivation of the X and Y leads than the others. For the Z lead, there were no statistically significant differences in the medians between Kors regression transformation and the PLSV and QLSV methods. This paper thoroughly compared multiple VCG transformation methods to conventional VCG Frank's orthogonal lead system, used in clinical practice.
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Affiliation(s)
- Rene Jaros
- Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, 17. listopadu 15, 708 33 Ostrava, Czech Republic.
| | - Radek Martinek
- Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, 17. listopadu 15, 708 33 Ostrava, Czech Republic.
| | - Lukas Danys
- Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, 17. listopadu 15, 708 33 Ostrava, Czech Republic
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Halamek J, Leinveber P, Viscor I, Smisek R, Plesinger F, Vondra V, Lipoldova J, Matejkova M, Jurak P. The relationship between ECG predictors of cardiac resynchronization therapy benefit. PLoS One 2019; 14:e0217097. [PMID: 31150418 PMCID: PMC6544221 DOI: 10.1371/journal.pone.0217097] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2019] [Accepted: 05/04/2019] [Indexed: 11/18/2022] Open
Abstract
INTRODUCTION Cardiac resynchronization therapy (CRT) is an effective treatment that reduces mortality and improves cardiac function in patients with left bundle branch block (LBBB). However, about 30% of patients passing the current criteria do not benefit or benefit only a little from CRT. Three predictors of benefit based on different ECG properties were compared: 1) "strict" left bundle branch block classification (SLBBB); 2) QRS area; 3) ventricular electrical delay (VED) which defines the septal-lateral conduction delay. These predictors have never been analyzed concurrently. We analyzed the relationship between them on a subset of 602 records from the MADIT-CRT trial. METHODS & RESULTS SLBBB classification was performed by two experts; QRS area and VED were computed fully automatically. High-frequency QRS (HFQRS) maps were used to inspect conduction abnormalities. The correlation between SLBBB and other predictors was R = 0.613, 0.523 and 0.390 for VED, QRS area in Z lead, and QRS duration, respectively. Scatter plots were used to pick up disagreement between the predictors. The majority of SLBBB subjects- 295 of 330 (89%)-are supposed to respond positively to CRT according to the VED and QRS area, though 93 of 272 (34%) non-SLBBB should also benefit from CRT according to the VED and QRS area. CONCLUSION SLBBB classification is limited by the proper setting of cut-off values. In addition, it is too "strict" and excludes patients that may benefit from CRT therapy. QRS area and VED are clearly defined parameters. They may be used to optimize biventricular stimulation. Detailed analysis of conduction irregularities with CRT optimization should be based on HFQRS maps.
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Affiliation(s)
- Josef Halamek
- Institute of Scientific Instruments of the Czech Academy of Sciences, Brno, Czech Republic
| | - Pavel Leinveber
- International Clinical Research Center, St. Anne’s University Hospital, Brno, Czech Republic
| | - Ivo Viscor
- Institute of Scientific Instruments of the Czech Academy of Sciences, Brno, Czech Republic
| | - Radovan Smisek
- Institute of Scientific Instruments of the Czech Academy of Sciences, Brno, Czech Republic
| | - Filip Plesinger
- Institute of Scientific Instruments of the Czech Academy of Sciences, Brno, Czech Republic
| | - Vlastimil Vondra
- Institute of Scientific Instruments of the Czech Academy of Sciences, Brno, Czech Republic
| | - Jolana Lipoldova
- International Clinical Research Center, St. Anne’s University Hospital, Brno, Czech Republic
| | - Magdalena Matejkova
- International Clinical Research Center, St. Anne’s University Hospital, Brno, Czech Republic
| | - Pavel Jurak
- Institute of Scientific Instruments of the Czech Academy of Sciences, Brno, Czech Republic
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Kück K, Isaksen JL, Graff C, Skaaby T, Linneberg A, Hansen T, Kanters JK. Spatial QRS-T angle variants for prediction of all-cause mortality. J Electrocardiol 2018; 51:768-775. [DOI: 10.1016/j.jelectrocard.2018.05.011] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2018] [Revised: 04/12/2018] [Accepted: 05/18/2018] [Indexed: 11/28/2022]
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Vozda M, Jurek F, Cerny M. Individualization of a vectorcardiographic model by a particle swarm optimization. Biomed Signal Process Control 2015. [DOI: 10.1016/j.bspc.2015.06.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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Abstract
The transformation of recorded electrocardiographic leads (source leads) into leads that are wanted but were not recorded (target leads) has many practical applications. In general, two transformation methods are put to use, a purely statistical one and a model-based one. They are briefly reviewed and compared. Lead transformations were first used in the early nineteen-sixties to transform the component leads of one vectorcardiographic lead system into those of another. Since then, the use of lead transformations has proliferated and they are currently applied for a variety of purposes. Lead transformations can be grouped according to the source and target leads that are involved. A few applications of lead transformations from the different groups are presented, with a focus on the practicality of the application. The validity and value of the dipole approximation in relation to lead transformations is discussed.
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Affiliation(s)
- Jan A Kors
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, the Netherlands.
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