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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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Zhang H, Liu C, Tang F, Li M, Zhang D, Xia L, Zhao N, Li S, Crozier S, Xu W, Liu F. Cardiac Arrhythmia classification based on 3D recurrence plot analysis and deep learning. Front Physiol 2022; 13:956320. [PMID: 35936913 PMCID: PMC9352947 DOI: 10.3389/fphys.2022.956320] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Accepted: 06/27/2022] [Indexed: 11/29/2022] Open
Abstract
Artificial intelligence (AI) aided cardiac arrhythmia (CA) classification has been an emerging research topic. Existing AI-based classification methods commonly analyze electrocardiogram (ECG) signals in lower dimensions, using one-dimensional (1D) temporal signals or two-dimensional (2D) images, which, however, may have limited capability in characterizing lead-wise spatiotemporal correlations, which are critical to the classification accuracy. In addition, existing methods mostly assume that the ECG data are linear temporal signals. This assumption may not accurately represent the nonlinear, nonstationary nature of the cardiac electrophysiological process. In this work, we have developed a three-dimensional (3D) recurrence plot (RP)-based deep learning algorithm to explore the nonlinear recurrent features of ECG and Vectorcardiography (VCG) signals, aiming to improve the arrhythmia classification performance. The 3D ECG/VCG images are generated from standard 12 lead ECG and 3 lead VCG signals for neural network training, validation, and testing. The superiority and effectiveness of the proposed method are validated by various experiments. Based on the PTB-XL dataset, the proposed method achieved an average F1 score of 0.9254 for the 3D ECG-based case and 0.9350 for the 3D VCG-based case. In contrast, recently published 1D and 2D ECG-based CA classification methods yielded lower average F1 scores of 0.843 and 0.9015, respectively. Thus, the improved performance and visual interpretability make the proposed 3D RP-based method appealing for practical CA classification.
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Affiliation(s)
- Hua Zhang
- School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, QLD, Australia
| | - Chengyu Liu
- School of Instrument Science and Engineering, Southeast University, Nanjing, China
| | - Fangfang Tang
- School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, QLD, Australia
| | - Mingyan Li
- School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, QLD, Australia
| | - Dongxia Zhang
- Zhejiang Provincial Centre for Disease Control and Prevention CN, Hangzhou, China
| | - Ling Xia
- Department of Biomedical Engineering, Zhejiang University, Hangzhou, China
| | - Nan Zhao
- School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, QLD, Australia
| | - Sheng Li
- The College of Science, Xijing University, Xi’an, China
| | - Stuart Crozier
- School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, QLD, Australia
| | - Wenlong Xu
- Department of Biomedical Engineering, China Jiliang University, Hangzhou, China
- *Correspondence: Wenlong Xu, ; Feng Liu,
| | - Feng Liu
- School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, QLD, Australia
- *Correspondence: Wenlong Xu, ; Feng Liu,
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Construction of Dynamic Lead Fields from Vectorcardiography to Solve the Forward and the Inverse Problems in Magnetocardiography. Ing Rech Biomed 2021. [DOI: 10.1016/j.irbm.2020.10.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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Rahola JT, Kiviniemi AM, Ukkola OH, Tulppo MP, Junttila MJ, Huikuri HV, Kenttä TV, Perkiömäki JS. Temporal variability of T-wave morphology and risk of sudden cardiac death in patients with coronary artery disease. Ann Noninvasive Electrocardiol 2021; 26:e12830. [PMID: 33486851 PMCID: PMC8164143 DOI: 10.1111/anec.12830] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/03/2020] [Revised: 01/04/2021] [Accepted: 01/04/2021] [Indexed: 12/16/2022] Open
Abstract
Background The possible relationship between temporal variability of electrocardiographic spatial heterogeneity of repolarization and the risk of sudden cardiac death (SCD) in patients with coronary artery disease (CAD) is not completely understood. Methods The standard deviation of T‐wave morphology dispersion (TMD‐SD), of QRST angle (QRSTA‐SD), and of T‐wave area dispersion (TW‐Ad‐SD) were analyzed on beat‐to‐beat basis from 10 min period of the baseline electrocardiographic recording in ARTEMIS study patients with angiographically verified CAD. Results After on average of 8.6 ± 2.3 years of follow‐up, a total of 66 of the 1,678 present study subjects (3.9%) had experienced SCD or were resuscitated from sudden cardiac arrest (SCA). TMD‐SD was most closely associated with the risk for SCD and was significantly higher in patients who had experienced SCD/SCA compared with those who remained alive (3.61 ± 2.83 vs. 2.64 ± 2.52, p = .008, respectively), but did not differ significantly between the patients who had experienced non‐SCD (n = 71, 4.2%) and those who remained alive (3.20 ± 2.73 vs. 2.65 ± 2.53, p = .077, respectively) or between the patients who succumbed to non‐cardiac death (n = 164, 9.8%) and those who stayed alive (2.64 ± 2.17 vs. 2.68 ± 2.58, p = .853). After adjustments with relevant clinical risk indicators of SCD/SCA, TMD‐SD still predicted SCD/SCA (HR 1.107, 95% CIs 1.035–1.185, p = .003). Conclusions Temporal variability of electrocardiographic spatial heterogeneity of repolarization represented by TMD‐SD independently predicts long‐term risk of SCD/SCA in patients with CAD.
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Affiliation(s)
- Janne T Rahola
- Research Unit of Internal Medicine, Medical Research Center Oulu, University of Oulu and Oulu University Hospital, Oulu, Finland
| | - Antti M Kiviniemi
- Research Unit of Internal Medicine, Medical Research Center Oulu, University of Oulu and Oulu University Hospital, Oulu, Finland
| | - Olavi H Ukkola
- Research Unit of Internal Medicine, Medical Research Center Oulu, University of Oulu and Oulu University Hospital, Oulu, Finland
| | - Mikko P Tulppo
- Department of Physiology, Research Unit of Biomedicine, Faculty of Medicine, University of Oulu, Oulu, Finland
| | - M Juhani Junttila
- Research Unit of Internal Medicine, Medical Research Center Oulu, University of Oulu and Oulu University Hospital, Oulu, Finland
| | - Heikki V Huikuri
- Research Unit of Internal Medicine, Medical Research Center Oulu, University of Oulu and Oulu University Hospital, Oulu, Finland
| | - Tuomas V Kenttä
- Research Unit of Internal Medicine, Medical Research Center Oulu, University of Oulu and Oulu University Hospital, Oulu, Finland
| | - Juha S Perkiömäki
- Research Unit of Internal Medicine, Medical Research Center Oulu, University of Oulu and Oulu University Hospital, Oulu, Finland
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Meyers A, Buqammaz M, Yang H. Cross-recurrence analysis for pattern matching of multidimensional physiological signals. CHAOS (WOODBURY, N.Y.) 2020; 30:123125. [PMID: 33380053 DOI: 10.1063/5.0030838] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/25/2020] [Accepted: 11/24/2020] [Indexed: 06/12/2023]
Abstract
Cross-recurrence quantification analysis (CRQA), based on the cross-recurrence plot (CRP), is an effective method to characterize and quantify the nonlinear interrelationships between a pair of nonlinear time series. It allows the flexibility of reconstructing signals in the phase space and to identify different types of patterns at arbitrary positions between trajectories. These advantages make CRQA attractive for time series data mining tasks, which have been of recent interest in the literature. However, little has been done to exploit CRQA for pattern matching of multidimensional, especially spatiotemporal, physiological signals. In this paper, we present a novel methodology in which CRQA statistics serve as measures of dissimilarity between pairs of signals and are subsequently used to uncover clusters within the data. This methodology is evaluated on a real dataset consisting of 3D spatiotemporal vectorcardiogram (VCG) signals from healthy and diseased patients. Experimental results show that Lmax, the length of the longest diagonal line in the CRP, yields the best-performing clustering that almost exactly matches the ground truth diagnoses of patients. Results also show that our proposed measure, Rτ max, which characterizes the maximum similarity between signals over all pairwise time-delayed alignments, outperforms all other tested CRQA measures (in terms of matching the ground truth) when the VCG signals are rescaled to reduce the effects of signal amplitude.
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Affiliation(s)
- Adam Meyers
- Complex Systems Monitoring, Modeling and Control Laboratory, The Pennsylvania State University, University Park, Pennsylvania 16801, USA
| | - Mohammed Buqammaz
- Complex Systems Monitoring, Modeling and Control Laboratory, The Pennsylvania State University, University Park, Pennsylvania 16801, USA
| | - Hui Yang
- Complex Systems Monitoring, Modeling and Control Laboratory, The Pennsylvania State University, University Park, Pennsylvania 16801, USA
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Garfield RE, Murphy L, Gray K, Towe B. Review and Study of Uterine Bioelectrical Waveforms and Vector Analysis to Identify Electrical and Mechanosensitive Transduction Control Mechanisms During Labor in Pregnant Patients. Reprod Sci 2020; 28:838-856. [PMID: 33090378 DOI: 10.1007/s43032-020-00358-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Accepted: 10/11/2020] [Indexed: 12/15/2022]
Abstract
The bioelectrical signals that produce uterine contractions during parturition are not completely understood. The objectives are as follows: (1) to review the literature and information concerning uterine biopotential waveforms generated by the uterus, known to produce contractions, and evaluate mechanotransduction in pregnant patients using electromyographic (EMG) recording methods and (2) to study a new approach, uterine vector analysis, commonly used for the heart: vectorcardiography analysis. The patients used in this study were as follows: (1) patients at term not in labor (n = 3); (2) patients during the 1st stage of labor at cervical dilations from 2 to 10 cm (n = 30); and (3) patients in the 2nd stage of labor and during delivery (n = 3). We used DC-coupled electrodes and PowerLab hardware (model no. PL2604, ADInstruments, Castle Hill, Australia), with software (LabChart, ADInstruments) for storage and analysis of biopotentials. Uterine and abdominal EMG recordings were made from the surface of each patient using 3 electrode pairs with 1 pair (+ and -, with a 31-cm spacing distance) placed in the right/left position (X position) and with 1 pair placed in an up/down position (Y position, also 31 cm apart) and with the third pair at the front/back (Z position). Using signals from the three X, Y, and Z electrodes, slow (0.03 to 0.1 Hz, high amplitude) and fast wave (0.3 to 1 Hz, low amplitude) biopotentials were recorded. The amplitudes of the slow waves and fast waves were significantly higher during the 2nd stage of labor compared to the 1st stage (respectively, p = 9.54 × e-3 and p = 3.94 × e-7). When 2 channels were used, for example, the X vs. Y, for 2-D vector analysis or 3 channels, X vs. Y vs. Z, for 3-D analysis, are plotted against each other on their axes, this produces a vector electromyometriogram (EMMG) that shows no directionality for fast waves and a downward direction for slow waves. Similarly, during the 2nd stage of labor during abdominal contractions ("pushing"), the slow and fast waves were enlarged. Manual applied pressure was used to evoke bioelectrical activity to examine the mechanosensitivity of the uterus. Conclusions: (1) Phasic contractility of the uterus is a product of slow waves and groups of fast waves (bursts of spikes) to produce myometrial contractile responses. (2) 2-D and 3-D uterine vector analyses (uterine vector electromyometriogram) demonstrate no directionality of small fast waves while the larger slow waves represent the downward direction of biopotentials towards the cervical opening. (3) Myometrial cell action event excitability and subsequent contractility likely amplify slow wave activity input and uterine muscle contractility via mechanotransduction systems. (4) Models illustrate the possible relationships of slow to fast waves and the association of a mechanotransduction system and pacemaker activity as observed for slow waves and pacemakers in gastrointestinal muscle. (5) The interaction of these systems is thought to regulate uterine contractility. (6) This study suggests a potential indicator of delivery time. Such vector approaches might help us predict the progress of gestation and better estimate the timing of delivery, gestational pathologies reflected in bioelectric events, and perhaps the potential for premature delivery drug and mechanical interventions.
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Affiliation(s)
- R E Garfield
- Department of Obstetrics and Gynecology, University of Arizona College of Medicine-Phoenix, Phoenix, AZ, USA.
| | - Lauren Murphy
- Department of Obstetrics and Gynecology, University of Arizona College of Medicine-Phoenix, Phoenix, AZ, USA
| | - Kendra Gray
- Department of Obstetrics and Gynecology, University of Arizona College of Medicine-Phoenix, Phoenix, AZ, USA
| | - Bruce Towe
- Department of Biomedical Engineering, Arizona State University, Tempe, AZ, USA
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Boulay E, Troncy E, Accardi MV, Pugsley MK, Downey AM, Miraucourt L, Huang H, Menard A, Tan W, Dubuc-Mageau M, Sanfacon A, Guerrier M, Authier S. Confounders and Pharmacological Characterization When Using the QT, JTp, and Tpe Intervals in Beagle Dogs. Int J Toxicol 2020; 39:530-541. [PMID: 33063577 DOI: 10.1177/1091581820954865] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
INTRODUCTION Corrected QT (QTc) interval is an essential proarrhythmic risk biomarker, but recent data have identified limitations to its use. The J to T-peak (JTp) interval is an alternative biomarker for evaluating drug-induced proarrhythmic risk. The aim of this study was to evaluate pharmacological effects using spatial magnitude leads and DII electrocardiogram (ECG) leads and common ECG confounders (ie, stress and body temperature changes) on covariate adjusted QT (QTca), covariate adjusted JTp (JTpca), and covariate adjusted T-peak to T-end (Tpeca) intervals. METHODS Beagle dogs were exposed to body hyper- (42 °C) or hypothermic (33 °C) conditions or were administered epinephrine to assess confounding effects on heart rate corrected QTca, JTpca, and Tpeca intervals. Dofetilide (0.1, 0.3, 1.0 mg/kg), ranolazine (100, 140, 200 mg/kg), and verapamil (7, 15, 30, 43, 62.5 mg/kg) were administered to evaluate pharmacological effects. RESULTS Covariate adjusted QT (slope -12.57 ms/°C) and JTpca (-14.79 ms/°C) were negatively correlated with body temperature but Tpeca was minimally affected. Epinephrine was associated with QTca and JTpca shortening, which could be related to undercorrection in the presence of tachycardia, while minimal effects were observed for Tpeca. There were no significant ECG change following ranolazine administration. Verapamil decreased QTca and JTpca intervals and increased Tpeca, whereas dofetilide increased QTca and JTpca intervals but had inconsistent effects on Tpeca. CONCLUSION Results highlight potential confounders on QTc interval, but also on JTpca and Tpeca intervals in nonclinical studies. These potential confounding effects may be relevant to the interpretation of ECG data obtained from nonclinical drug safety studies with Beagle dogs.
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Affiliation(s)
- Emmanuel Boulay
- Charles River Laboratories, Laval, Quebec, Canada.,70354Faculté de médecine vétérinaire, Université de Montréal, Québec, Canada
| | - Eric Troncy
- 70354Faculté de médecine vétérinaire, Université de Montréal, Québec, Canada
| | | | | | | | | | - Hai Huang
- Charles River Laboratories, Laval, Quebec, Canada
| | | | - Wendy Tan
- 70354Faculté de médecine vétérinaire, Université de Montréal, Québec, Canada
| | | | - Audrey Sanfacon
- 70354Faculté de médecine vétérinaire, Université de Montréal, Québec, Canada
| | - Mireille Guerrier
- 70354Faculté de médecine vétérinaire, Université de Montréal, Québec, Canada
| | - Simon Authier
- Charles River Laboratories, Laval, Quebec, Canada.,70354Faculté de médecine vétérinaire, Université de Montréal, Québec, Canada
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Chaudhry U, Cortez D, Platonov PG, Carlson J, Borgquist R. Vectorcardiography Findings Are Associated with Recurrent Ventricular Arrhythmias and Mortality in Patients with Heart Failure Treated with Implantable Cardioverter-Defibrillator Device. Cardiology 2020; 145:784-794. [PMID: 32957097 DOI: 10.1159/000509766] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/25/2020] [Accepted: 06/23/2020] [Indexed: 11/19/2022]
Abstract
BACKGROUND There is a need for refined risk stratification of sudden cardiac death and prediction of ventricular arrhythmias to correctly identify patients who are expected to benefit the most from implantable cardioverter-defibrillator (ICD) therapy. METHODS We conducted a registry-based retrospective observational study on patients with either ischemic (ICMP) or nonischemic dilated cardiomyopathy (NICMP) treated with ICD between 2002 and 2013 at a tertiary referral center. We evaluated 3 vectorcardiography (VCG) indices; spatial QRS-T angle, QRS vector magnitude (QRSvm), and T-wave vector magnitude (Twvm), and their association with all-cause mortality and ventricular arrhythmias. The VCG indices were automatically computed from resting 12-lead electrocardiograms before ICD implantation. RESULTS 178 patients were included in the study; 53.4% had ICMP, 79.2% were male, and mean ejection fraction was 27.4%. During the follow-up (median 89 months), 40 patients (23%) died; 31% had appropriate ICD therapy. In multivariate analysis with dichotomized variables, QRS-T angle >152° and Twvm <0.38 mV were significantly associated with increased mortality: HR 2.64 (95% CI 1.14-6.12, p = 0.02) and HR 5.30 (95% CI 2.31-12.11, p < 0.001), respectively. QRSvm <1.54 mV was borderline significant with mortality outcome (p = 0.10). The composite score of all 3 VCG indices, a score of 3, conferred an increased risk of mortality (including heart failure mortality) in multivariate analysis: HR 13.80 (95% CI 3.44-55.39, p < 0.001). CONCLUSION The spatial QRS-T angle and Twvm are emerging VCG indices which are independently associated with mortality in patients with reduced left ventricular ejection fraction due to ICMP or NICMP. Using a composite score of all 3 vector indices, a maximum score was associated with poor long-term survival.
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Affiliation(s)
- Uzma Chaudhry
- Department of Cardiology, Clinical Sciences, Lund University, Arrhythmia Clinic, Skane University Hospital, Lund, Sweden,
| | - Daniel Cortez
- Department of Pediatric Cardiology, University of Minnesota/Masonic Children's Hospital, Minneapolis, Minnesota, USA.,Department of Cardiology, Clinical Sciences, Lund University, Lund, Sweden
| | - Pyotr G Platonov
- Department of Cardiology, Clinical Sciences, Lund University, Arrhythmia Clinic, Skane University Hospital, Lund, Sweden
| | - Jonas Carlson
- Department of Cardiology, Clinical Sciences, Lund University, Lund, Sweden
| | - Rasmus Borgquist
- Department of Cardiology, Clinical Sciences, Lund University, Arrhythmia Clinic, Skane University Hospital, Lund, Sweden
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Silva IDS, Barbosa JR, Sousa RDD, Souza IFBD, Hortegal RDA, Regis CDM. Comparison of spatial temporal representations of the vectorcardiogram using digital image processing. J Electrocardiol 2020; 59:164-170. [PMID: 32160573 DOI: 10.1016/j.jelectrocard.2020.02.013] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2019] [Revised: 01/16/2020] [Accepted: 02/20/2020] [Indexed: 10/24/2022]
Abstract
INTRODUCTION The vectorcardiography (VCG) is a method of representing the heart's electrical activity in three dimensions that is not frequently used in clinical practice due to the higher complexity compared to electrocardiography (ECG). A way around this problem was the development of regression techniques to obtain the VCG from the 12‑lead ECG and the evaluation of these techniques is done by comparing the parameters obtained by the gold standard method and by the VCG obtained by the alternative methods. In this paper it is proposed instead a comparison between the images of the VCG planes using the values returned by digital image processing metrics such as PSNR, SSIM and PW-SSIM. METHODS The signals used were obtained from the Physikalisch-Technische Bundesanstalt Diagnostic ECG Database, which contains both the VCGs obtained by the gold standard method and the 12 lead ECG signals. They were divided into five groups that contained a control group and according to the region of the wall infarction. The ECG signals were then filtered using a Butterworth Finite Impulse Response bandpass filter, with cutoff frequencies of 3 Hz and 45 Hz and then the VCGs were by a computer application using the Kors inverse matrix method, the Kors quasi-orthogonal method and the Dower Inverse Matrix method. The reconstructed signals were then compared using the PSNR, SSIM and PW-SSIM methods. The returned values were presented in tables for each group containing the average value and standard deviance for each method in each VCG plane. RESULTS Using image processing techniques, it was possible to perceive that the alternative methods to obtain the VCG have a high confiability that could be compared to the gold standard in signals from healthy subjects. However, signals from pathological subjects present variations that could be caused by a deficit of these alternative methods to represent the pathology in these cases. Considering the PW-SSIM, the Frontal plane by the reconstructions was considered the most similar to the gold standard, having PW-SSIM values higher than 0.93 and for the Horizontal plane two groups had PW-SSIM values lower than 0.90 and for the Sagittal plane all groups had values lower than this value. DISCUSSION The values yielded by the PSNR and SSIM had low variance, worsening the perception of the effect of the reconstruction method used or the infarction effect over the reconstruction. The values lower than 0.90 could indicate that these planes have their generation most affected by the infarction. CONCLUSION The three methods of obtaining the VCG Frank leads, the Kors Quasi-Orthogonal method, the Kors Linear Regression method and the Dower Inverse Matrix, presented differences in the metrics: PSNR, SSIM and PW-SSIM in normal subjects according to the planes frontal, horizontal and sagittal and in subjects with Myocardial Infarction according to its topography: anterior, inferolateral, inferior or multiarterials. Considering only the PW-SSIM, the QO method had the best performance in different signals, followed by the Dower method.
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Bhattacharyya S, Goswami DP, Sengupta A. Spatial velocity of the dynamic vectorcardiographic loop provides crucial insight in ventricular dysfunction. Med Hypotheses 2020; 135:109484. [DOI: 10.1016/j.mehy.2019.109484] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2019] [Accepted: 11/08/2019] [Indexed: 11/28/2022]
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Chen R, Imani F, Yang H. Heterogeneous Recurrence Analysis of Disease-Altered Spatiotemporal Patterns in Multi-Channel Cardiac Signals. IEEE J Biomed Health Inform 2019; 24:1619-1631. [PMID: 31715575 DOI: 10.1109/jbhi.2019.2952285] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Heart diseases alter the rhythmic behaviors of cardiac electrical activity. Recent advances in sensing technology bring the ease to acquire space-time electrical activity of the heart such as vectorcardiogram (VCG) signals. Recurrence analysis of successive heartbeats is conducive to detect the disease-altered cardiac activities. However, conventional recurrence analysis is more concerned about homogeneous recurrences, and overlook heterogeneous types of recurrence variations in VCG signals (i.e., in terms of state properties and transition dynamics). This paper presents a new framework of heterogeneous recurrence analysis for the characterization and modeling of disease-altered spatiotemporal patterns in multi-channel cardiac signals. Experimental results show that the proposed approach yields an accuracy of 96.9%, a sensitivity of 95.0%, and a specificity of 98.7% for the identification of myocardial infarctions. The proposed method of heterogeneous recurrence analysis shows strong potential to be further extended for the analysis of other physiological signals such as electroencephalogram (EEG) and electromyography (EMG) signals towards medical decision making.
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Yao B, Zhu R, Yang H. Characterizing the Location and Extent of Myocardial Infarctions With Inverse ECG Modeling and Spatiotemporal Regularization. IEEE J Biomed Health Inform 2018; 22:1445-1455. [PMID: 29990091 DOI: 10.1109/jbhi.2017.2768534] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Myocardial infarction (MI) is among the leading causes of death in the United States. It is imperative to identify and characterize MIs for timely delivery of life-saving medical interventions. Cardiac electrical activity propagates in space and evolves over time. Traditional works focus on the analysis of time-domain ECG (e.g., 12-lead ECG) on the body surface for the detection of MIs, but tend to overlook spatiotemporal dynamics in the heart. Body surface potential mappings (BSPMs) provide high-resolution distribution of electric potentials over the entire torso, and therefore provide richer information than 12-lead ECG. However, BSPM are available on the body surface. Clinicians are in need of a closer look of the electric potentials in the heart to investigate cardiac pathology and optimize treatment strategies. In this paper, we applied the method of spatiotemporal inverse ECG (ST-iECG) modeling to map electrical potentials from the body surface to the heart, and then characterize the location and extent of MIs by investigating the reconstructed heart-surface electrograms. First, we investigate the impact of mesh resolution on the inverse ECG modeling. Second, we solve the inverse ECG problem and reconstruct heart-surface electrograms using the ST-iECG model. Finally, we propose a wavelet-clustering method to investigate the pathological behaviors of heart-surface electrograms, and thereby characterize the extent and location of MIs. The proposed methodology is evaluated and validated with real data of MIs from human subjects. Experimental results show that negative QRS waves in heart-surface electrograms indicate potential regions of MI, and the proposed ST-iECG model yields superior characterization results of MIs on the heart surface over existing methods.
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Ray D, Hazra S, Goswami DP, Macfarlane PW, Sengupta A. An evaluation of planarity of the spatial QRS loop by three dimensional vectorcardiography: Its emergence and loss. J Electrocardiol 2017; 50:652-660. [PMID: 28366419 DOI: 10.1016/j.jelectrocard.2017.03.016] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2016] [Indexed: 11/17/2022]
Affiliation(s)
- Dipanjan Ray
- Dept. of Physiology, Calcutta Medical College, 88, College Street, Calcutta, India
| | - Sandipan Hazra
- Dept. of General Medicine, RG Kar Medical College, 1, KhudiramBasuSarani, Calcutta, India
| | - Damodar Prasad Goswami
- Dept. of Mathematics, Narula Institute of Technology, 81, Nilgunj Road, Agarpara, West Bengal, India
| | - Peter W Macfarlane
- University of Glasgow, Inst of Health and Wellbeing, Electrocardiology Group, Royal Infirmary, Glasgow, G31 2ER, UK
| | - Arnab Sengupta
- Dept. of Physiology, Calcutta Medical College, 88, College Street, Calcutta, India.
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Leonelli FM. Map reduce for optimizing a large-scale dynamic network - the Internet of hearts. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2017; 2016:2962-2965. [PMID: 28268934 DOI: 10.1109/embc.2016.7591351] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Rapid advancements of sensing and mobile technology provide an unprecedented opportunity to empower smart and connected healthcare. Realizing the full potential of connected care depends, however, to a great extent on the capability of data analytics. Our previous study proposed a next-generation mobile health system, namely, the Internet of Heart (IoH). The IoH embeds patients into a dynamic network, where the distance between network nodes is determined by the dissimilarity of patients' conditions. Dynamics of the network reveal the change of clinical status of patients. However, it poses a great challenge for real-time recognition of disease patterns when a considerably large number of patients are involved in the IoH. In this present investigation, we develop a novel scheme to optimize the network in a parallel, distributed manner, thereby improving the efficiency of computation. First, a stochastic gradient descent approach is designed to embed patients with similar conditions into a local network. Second, local networks are optimally pieced together to obtain a global network. As opposed to directly embed all patients into one network, the proposed scheme distributes the network optimization into multiple processors for parallel computing. This, in turn, enables the IoH to handle large amount of patients and timely recognize disease patterns in the early stage. Experimental results demonstrated the effectiveness of the proposed scheme, e.g., it achieves 80-fold faster than conventional algorithms for optimizing a network with 20000 patients. The developed scheme is effective and efficient for realizing smart connected healthcare in large-scale IoH contexts.
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Leonelli FM. Whole heart modeling - Spatiotemporal dynamics of electrical wave conduction and propagation. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2017; 2016:5575-5578. [PMID: 28269518 DOI: 10.1109/embc.2016.7591990] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Cardiac electrical activities are varying in both space and time. Human heart consists of a fractal network of muscle cells, Purkinje fibers, arteries and veins. Whole-heart modeling of electrical wave conduction and propagation involves a greater level of complexity. Our previous work developed a computer model of the anatomically realistic heart and simulated the electrical conduction with the use of cellular automata. However, simplistic assumptions and rules limit its ability to provide an accurate approximation of real-world dynamics on the complex heart surface, due to sensitive dependence of nonlinear dynamical systems on initial conditions. In this paper, we propose new reaction-diffusion methods and pattern recognition tools to simulate and model spatiotemporal dynamics of electrical wave conduction and propagation on the complex heart surface, which include (i) whole heart model; (ii) 2D isometric graphing of 3D heart geometry; (iii) reaction-diffusion modeling of electrical waves in 2D graph, and (iv) spatiotemporal pattern recognition. Experimental results show that the proposed numerical solution has strong potentials to model the space-time dynamics of electrical wave conduction in the whole heart, thereby achieving a better understanding of disease-altered cardiac mechanisms.
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16
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Physics-driven Spatiotemporal Regularization for High-dimensional Predictive Modeling: A Novel Approach to Solve the Inverse ECG Problem. Sci Rep 2016; 6:39012. [PMID: 27966576 PMCID: PMC5155286 DOI: 10.1038/srep39012] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2016] [Accepted: 11/14/2016] [Indexed: 11/08/2022] Open
Abstract
This paper presents a novel physics-driven spatiotemporal regularization (STRE) method for high-dimensional predictive modeling in complex healthcare systems. This model not only captures the physics-based interrelationship between time-varying explanatory and response variables that are distributed in the space, but also addresses the spatial and temporal regularizations to improve the prediction performance. The STRE model is implemented to predict the time-varying distribution of electric potentials on the heart surface based on the electrocardiogram (ECG) data from the distributed sensor network placed on the body surface. The model performance is evaluated and validated in both a simulated two-sphere geometry and a realistic torso-heart geometry. Experimental results show that the STRE model significantly outperforms other regularization models that are widely used in current practice such as Tikhonov zero-order, Tikhonov first-order and L1 first-order regularization methods.
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17
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Yang H, Leonelli F. Self-organizing visualization and pattern matching of vectorcardiographic QRS waveforms. Comput Biol Med 2016; 79:1-9. [PMID: 27723506 DOI: 10.1016/j.compbiomed.2016.09.020] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2016] [Revised: 09/26/2016] [Accepted: 09/26/2016] [Indexed: 11/16/2022]
Abstract
QRS morphology is commonly used in the electrocardiographic diagnosis of ventricular depolarization such as left bundle branch block (LBBB) and ventricular septal infarction. We investigated whether pattern matching of QRS loops in the 3-dimensional vectorcardiogram (VCG) will improve the grouping of patients whose space-time electrical activity akin to each other, thereby assisting in clinical decision making. First, pattern dissimilarity of VCG QRS loops is qualitatively measured and characterized among patients, resulting in a 93×93 distance matrix of patient-to-patient dissimilarity. Each patient is then represented as a node in the network (or a star in the galaxy), but node locations are optimized to preserve the dissimilarity matrix. The optimization is achieved with a self-organizing algorithm that iteratively minimizes the network energy. Experimental results showed that patients' locations converge as the representation error reaches a stable phase. The convergence is independent of initial locations of network nodes. Most importantly, 93 patients are automatically organized into 3 clusters of healthy control, LBBB, and infarction. Spatial coordinates of nodes (or patients) are evidently novel predictors that can be used in the computer-assisted detection of cardiac disorders. Self-organizing pattern matching is shown to have strong potentials for large-scale unsupervised learning of patient groups.
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Affiliation(s)
- Hui Yang
- Complex Systems Monitoring, Modeling and Control Laboratory, The Pennsylvania State University, University Park, PA, USA.
| | - Fabio Leonelli
- Cardiology Department, James A. Haley Veterans' Hospital, Tampa, FL 33620, USA
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18
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Hasan MA, Abbott D. A review of beat-to-beat vectorcardiographic (VCG) parameters for analyzing repolarization variability in ECG signals. ACTA ACUST UNITED AC 2016; 61:3-17. [DOI: 10.1515/bmt-2015-0005] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2014] [Accepted: 04/17/2015] [Indexed: 11/15/2022]
Abstract
AbstractElevated ventricular repolarization lability is believed to be linked to the risk of ventricular tachycardia/ventricular fibrillation. However, ventricular repolarization is a complex electrical phenomenon, and abnormalities in ventricular repolarization are not completely understood. To evaluate repolarization lability, vectorcardiography (VCG) is an alternative approach where the electrocardiographic (ECG) signal can be considered as possessing both magnitude and direction. Recent research has shown that VCG is advantageous over ECG signal analysis for identification of repolarization abnormality. One of the key reasons is that the VCG approach does not rely on exact identification of the T-wave offset, which improves the reproducibility of the VCG technique. However, beat-to-beat variability in VCG is an emerging area for the investigation of repolarization abnormality though not yet fully realized. Therefore, the purpose of this review is to explore the techniques, findings, and efficacy of beat-to-beat VCG parameters for analyzing repolarization lability, which may have potential utility for further study.
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Chen Y, Yang H. Wavelet packet analysis of disease-altered recurrence dynamics in the long-term spatiotemporal vectorcardiogram (VCG) signals. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2015; 2013:2595-8. [PMID: 24110258 DOI: 10.1109/embc.2013.6610071] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Vectorcardiogram (VCG) signals contain a wealth of dynamic information pertinent to space-time cardiac electrical activities. However, few, if any, previous investigations have studied disease-altered nonlinear dynamics in the spatiotemporal VCG signals. Most previous nonlinear dynamic methods considered the time-delay reconstructed state space from a single ECG trace. This paper presents a novel multiscale recurrence approach to not only explore VCG recurrence dynamics but also resolve the issue of recurrence computation for the large-scale datasets. As opposed to the traditional single-scale recurrence analysis, we characterize and quantify the recurrence behaviours in multiple wavelet scales. In addition, wavelet dyadic subsampling enables the large-scale recurrence analysis, but it is used to be highly expensive for a long-term time series. The classification experiments show that multiscale recurrence analysis detects the myocardial infarctions from 3-lead VCG with an average sensitivity of 96.8% and specificity of 92.8%, which show superior performance (i.e., 5.6% improvements) to the single-scale recurrence analysis.
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20
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Yang H, Chen Y. Heterogeneous recurrence monitoring and control of nonlinear stochastic processes. CHAOS (WOODBURY, N.Y.) 2014; 24:013138. [PMID: 24697400 DOI: 10.1063/1.4869306] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
Recurrence is one of the most common phenomena in natural and engineering systems. Process monitoring of dynamic transitions in nonlinear and nonstationary systems is more concerned with aperiodic recurrences and recurrence variations. However, little has been done to investigate the heterogeneous recurrence variations and link with the objectives of process monitoring and anomaly detection. Notably, nonlinear recurrence methodologies are based on homogeneous recurrences, which treat all recurrence states in the same way as black dots, and non-recurrence is white in recurrence plots. Heterogeneous recurrences are more concerned about the variations of recurrence states in terms of state properties (e.g., values and relative locations) and the evolving dynamics (e.g., sequential state transitions). This paper presents a novel approach of heterogeneous recurrence analysis that utilizes a new fractal representation to delineate heterogeneous recurrence states in multiple scales, including the recurrences of both single states and multi-state sequences. Further, we developed a new set of heterogeneous recurrence quantifiers that are extracted from fractal representation in the transformed space. To that end, we integrated multivariate statistical control charts with heterogeneous recurrence analysis to simultaneously monitor two or more related quantifiers. Experimental results on nonlinear stochastic processes show that the proposed approach not only captures heterogeneous recurrence patterns in the fractal representation but also effectively monitors the changes in the dynamics of a complex system.
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Affiliation(s)
- Hui Yang
- Complex Systems Monitoring, Modeling and Analysis Laboratory, University of South Florida, Tampa, Florida 33620, USA
| | - Yun Chen
- Complex Systems Monitoring, Modeling and Analysis Laboratory, University of South Florida, Tampa, Florida 33620, USA
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21
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Le TQ, Bukkapatnam STS, Benjamin BA, Wilkins BA, Komanduri R. Topology and random-walk network representation of cardiac dynamics for localization of myocardial infarction. IEEE Trans Biomed Eng 2013; 60:2325-31. [PMID: 23559021 DOI: 10.1109/tbme.2013.2255596] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
While detection of acute cardiac disorders such as myocardial infarction (MI) from electrocardiogram (ECG) and vectorcardiogram (VCG) has been widely reported, identification of MI locations from these signals, pivotal for timely therapeutic and prognostic interventions, remains a standing issue. We present an approach for MI localization based on representing complex spatiotemporal patterns of cardiac dynamics as a random-walk network reconstructed from the evolution of VCG signals across a 3-D state space. Extensive tests with signals from the PTB database of the PhysioNet databank suggest that locations of MI can be determined accurately (sensitivity of ∼88% and specificity of ∼92%) from tracking certain consistently estimated invariants of this random-walk representation.
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Affiliation(s)
- Trung Q Le
- School of Industrial Engineering and Management, Oklahoma State University, Stillwater, OK 74074, USA.
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Gang Liu, Hui Yang. Multiscale Adaptive Basis Function Modeling of Spatiotemporal Vectorcardiogram Signals. IEEE J Biomed Health Inform 2013; 17:484-92. [DOI: 10.1109/jbhi.2013.2243842] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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Chen Y, Yang H. Self-organized neural network for the quality control of 12-lead ECG signals. Physiol Meas 2012; 33:1399-418. [DOI: 10.1088/0967-3334/33/9/1399] [Citation(s) in RCA: 35] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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