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Background: Manually derived electrocardiographic (ECG) parameters were not associated with mortality in mechanically ventilated COVID‐19 patients in earlier studies, while increased high‐sensitivity cardiac troponin‐T (hs‐cTnT) and N‐terminal pro‐B‐type natriuretic peptide (NT‐proBNP) were. To provide evidence for vectorcardiography (VCG) measures as potential cardiac monitoring tool, we investigated VCG trajectories during critical illness. Methods: All mechanically ventilated COVID‐19 patients were included in the Maastricht Intensive Care Covid Cohort between March 2020 and October 2021. Serum hs‐cTnT and NT‐proBNP concentrations were measured daily. Conversion of daily 12‐lead ECGs to VCGs by a MATLAB‐based script provided QRS area, T area, maximal QRS amplitude, and QRS duration. Linear mixed‐effect models investigated trajectories in serum and VCG markers over time between non‐survivors and survivors, adjusted for confounders. Results: In 322 patients, 5461 hs‐cTnT, 5435 NT‐proBNP concentrations and 3280 ECGs and VCGs were analyzed. Non‐survivors had higher hs‐cTnT concentrations at intubation and both hs‐cTnT and NT‐proBNP significantly increased compared with survivors. In non‐survivors, the following VCG parameters decreased more when compared to survivors: QRS area (−0.27 (95% CI) (−0.37 to −0.16, p <.01) μVs per day), T area (−0.39 (−0.62 to −0.16, p <.01) μVs per day), and maximal QRS amplitude (−0.01 (−0.01 to −0.01, p <.01) mV per day). QRS duration did not differ. Conclusion: VCG‐derived QRS area and T area decreased in non‐survivors compared with survivors, suggesting that an increase in myocardial damage and tissue loss play a role in the course of critical illness and may drive mortality. These VCG markers may be used to monitor critically ill patients. [ABSTRACT FROM AUTHOR]

Background: The prognosis of Duchenne muscular dystrophy (DMD) is poor once it develops to the stage of cardiac impairment. Recent studies have demonstrated that electrocardiogram (ECG), which consists of general ECG and vectorcardiogram (VCG), retains an extremely powerful role in the assessment of patients with reduced left ventricular (LV) systolic dysfunction. However, data regarding VCG recordings in DMD and its prognostic value for reduced left ventricular ejection fraction (LVEF) of DMD have never been reported. This study aims to describe the characteristics of VCG in children with DMD and to explore the predictive value of VCG for reduced LVEF in children with DMD. Methods: A total of 306 patients with a known diagnosis of DMD confirmed by the genetic test were retrospectively enrolled at our hospital between August 2018 and August 2022. This resulted in a total study group of 486 VCG recordings. Among them, 75 DMD patients who underwent cardiac magnetic resonance (CMR) later after one year follow-up were prospectively enrolled. The trend of VCG parameters of DMD patients across the different age span were compared with age-matched normal children. Concordance statistic analysis was further performed to assess the validity of VCG parameters in predicting the occurrence of reduced LVEF in patients with DMD. Results: DMD patients have a significantly higher heart rate, R waves in V1, QRS loop percentage in the right anterior quadrant in the horizontal plane (horizontal quadrant II) and QRS loop percentage in the anterior superior quadrant in the sagittal plane (sagittal quadrant IV) than normal children. Concordance statistic (C-statistic) showed an area under the curve of quadrant IV in the sagittal plane of baseline was 0.704. The receiver operating characteristic (ROC) curve shows that quadrant IV in the sagittal plane of 7.57% was the optimal cutoff with a sensitivity of 53.3% and a specificity of 88.3% for predicting reduced LVEF in DMD patients. Conclusions: Our study firstly showed that QRS loop percentage in the right anterior quadrant in the horizontal plane (horizontal quadrant II) and QRS loop percentage in the anterior superior quadrant in the sagittal plane (sagittal quadrant IV) could be abnormal in DMD boys as early as before 5 years old. Evaluation of the myocardium by VCG in the early age to predict possible cardiac systolic dysfunction may have important implications for the ongoing management of DMD boys. [ABSTRACT FROM AUTHOR]

Abstract Background Manually derived electrocardiographic (ECG) parameters were not associated with mortality in mechanically ventilated COVID‐19 patients in earlier studies, while increased high‐sensitivity cardiac troponin‐T (hs‐cTnT) and N‐terminal pro‐B‐type natriuretic peptide (NT‐proBNP) were. To provide evidence for vectorcardiography (VCG) measures as potential cardiac monitoring tool, we investigated VCG trajectories during critical illness. Methods All mechanically ventilated COVID‐19 patients were included in the Maastricht Intensive Care Covid Cohort between March 2020 and October 2021. Serum hs‐cTnT and NT‐proBNP concentrations were measured daily. Conversion of daily 12‐lead ECGs to VCGs by a MATLAB‐based script provided QRS area, T area, maximal QRS amplitude, and QRS duration. Linear mixed‐effect models investigated trajectories in serum and VCG markers over time between non‐survivors and survivors, adjusted for confounders. Results In 322 patients, 5461 hs‐cTnT, 5435 NT‐proBNP concentrations and 3280 ECGs and VCGs were analyzed. Non‐survivors had higher hs‐cTnT concentrations at intubation and both hs‐cTnT and NT‐proBNP significantly increased compared with survivors. In non‐survivors, the following VCG parameters decreased more when compared to survivors: QRS area (−0.27 (95% CI) (−0.37 to −0.16, p

AimsVentricular activation patterns can aid clinical decision-making directly by providing spatial information on cardiac electrical activation or indirectly through derived clinical indices. The aim of this work was to derive an atlas of the major modes of variation of ventricular activation from model-predicted 3D bi-ventricular activation time distributions and to relate these modes to corresponding vectorcardiograms (VCGs). We investigated how the resulting dimensionality reduction can improve and accelerate the estimation of activation patterns from surface electrogram measurements.Methods and resultsAtlases of activation time (AT) and VCGs were derived using principal component analysis on a dataset of simulated electrophysiology simulations computed on eight patient-specific bi-ventricular geometries. The atlases provided significant dimensionality reduction, and the modes of variation in the two atlases described similar features. Utility of the atlases was assessed by resolving clinical waveforms against them and the VCG atlas was able to accurately reconstruct the patient VCGs with fewer than 10 modes. A sensitivity analysis between the two atlases was performed by calculating a compact Jacobian. Finally, VCGs generated by varying AT atlas modes were compared with clinical VCGs to estimate patient-specific activation maps, and the resulting errors between the clinical and atlas-based VCGs were less than those from more computationally expensive method.ConclusionAtlases of activation and VCGs represent a new method of identifying and relating the features of these high-dimensional signals that capture the major sources of variation between patients and may aid in identifying novel clinical indices of arrhythmia risk or therapeutic outcome.

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. [ABSTRACT FROM AUTHOR]

Background: The prognosis of Duchenne muscular dystrophy (DMD) is poor once it develops to the stage of cardiac impairment. Recent studies have demonstrated that electrocardiogram (ECG), which consists of general ECG and vectorcardiogram (VCG), retains an extremely powerful role in the assessment of patients with reduced left ventricular (LV) systolic dysfunction. However, data regarding VCG recordings in DMD and its prognostic value for reduced left ventricular ejection fraction (LVEF) of DMD have never been reported. This study aims to describe the characteristics of VCG in children with DMD and to explore the predictive value of VCG for reduced LVEF in children with DMD., Methods: A total of 306 patients with a known diagnosis of DMD confirmed by the genetic test were retrospectively enrolled at our hospital between August 2018 and August 2022. This resulted in a total study group of 486 VCG recordings. Among them, 75 DMD patients who underwent cardiac magnetic resonance (CMR) later after one year follow-up were prospectively enrolled. The trend of VCG parameters of DMD patients across the different age span were compared with age-matched normal children. Concordance statistic analysis was further performed to assess the validity of VCG parameters in predicting the occurrence of reduced LVEF in patients with DMD., Results: DMD patients have a significantly higher heart rate, R waves in V1, QRS loop percentage in the right anterior quadrant in the horizontal plane (horizontal quadrant II) and QRS loop percentage in the anterior superior quadrant in the sagittal plane (sagittal quadrant IV) than normal children. Concordance statistic (C-statistic) showed an area under the curve of quadrant IV in the sagittal plane of baseline was 0.704. The receiver operating characteristic (ROC) curve shows that quadrant IV in the sagittal plane of 7.57% was the optimal cutoff with a sensitivity of 53.3% and a specificity of 88.3% for predicting reduced LVEF in DMD patients., Conclusions: Our study firstly showed that QRS loop percentage in the right anterior quadrant in the horizontal plane (horizontal quadrant II) and QRS loop percentage in the anterior superior quadrant in the sagittal plane (sagittal quadrant IV) could be abnormal in DMD boys as early as before 5 years old. Evaluation of the myocardium by VCG in the early age to predict possible cardiac systolic dysfunction may have important implications for the ongoing management of DMD boys., Competing Interests: The authors declare no conflict of interest., (Copyright: © 2024 The Author(s). Published by IMR Press.)

Wilson assumed that the ventricular gradient (VG) is independent of the ventricular activation order. This paradigm has often been refuted and was never convincingly corroborated. We sought to validate Wilson’s concept by intra-individual comparison of the VG of sinus beats and ectopic beats, thus assessing the effects of both altered ventricular conduction (caused by the ectopic focus) and restitution (caused by ectopic prematurity). We studied standard diagnostic ECGs of 118 patients with accidental extrasystoles: normally conducted supraventricular ectopic beats (SN, N = 6) and aberrantly conducted supraventricular ectopic beats (SA, N = 20) or ventricular ectopic beats (V, N = 92). In each patient, we computed the VG vectors of the predominant beat, VGp→, of the ectopic beat, VGe→, and of the VG difference vector, ΔVGep→, and compared their sizes. VGe→ of the SA and V ectopic beats were significantly larger than VGp→ (53.7 ± 25.0 vs. 47.8 ± 24.6 mV∙ms, respectively; p < 0.001). ΔVGep→ were three times larger than the difference of VGe→ and VGp→ (19.94 ± 9.76 vs. 5.94 mV∙ms, respectively), demonstrating differences in the VGp→ and VGe→ spatial directions. The amount of ectopic prematurity was not correlated with ΔVGep→, although the larger VG difference vectors were observed for the more premature (

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.

The angle between theQRSandTvectors reflects the consistency or inconsistency of the processes of de- and repolarization of the ventricles of the heart and is considered one of the indicators of global electrical heterogeneity of myocardium. In recent years, the prognostic value of theQRS-Tangle has been demonstrated in relation to total and cardiovascular mortality, both in the population and in various groups of patients. The mechanisms of this phenomenon are not completely clear. The review analyses studies published over the past five years on the relationship between theQRS-Tangle and mortality, as well as coronary heart disease and heart failure. Possible mechanisms for increasing theQRS-Tangle are discussed. Data are given on the use of theQRS-Tangle in diagnostic and prognostic scales, including in combination with other indicators of global electrical heterogeneity of myocardium.

Introduction: Left bundle branch pacing (LBBP) is a rapidly growing conduction system pacing technique. However, little is known regarding the electrophysiological characteristics of different types of LBBP. We aimed to evaluate the electrophysiological characteristics and anatomic lead location with pacing different branches of the left bundle branch.Methods: Consecutive bradycardia patients with successful LBBP were enrolled and classified into groups according to the paced electrocardiogram and the lead location. Electrocardiogram, pacing properties, vectorcardiogram, and lead tip location were analyzed.Results: Ninety-one patients were enrolled, including 48 with the left bundle trunk pacing (LBTP) and 43 with the left bundle fascicular pacing (LBFP). The paced QRS duration in the LBTP group was significantly shorter than that in the LBFP group (108.1 ± 9.9 vs. 112.9 ± 11.2 ms, p = 0.03), with a more rightward QRS transition zone (p = 0.01). The paced QRS area in the LBTP group was similar to that during intrinsic rhythm (35.1 ± 15.8 vs. 34.7 ± 16.6 μVs, p = 0.98), whereas in the LBFP group, the paced QRS area was significantly larger compared to intrinsic rhythm (43.4 ± 15.8 vs. 35.7 ± 18.0 μVs, p = 0.01). The lead tip site for LBTP was located in a small fan-shaped area with the tricuspid valve annulus summit as the origin, whereas fascicular pacing sites were more likely in a larger and more distal area.Conclusions: Pacing the proximal left bundle main trunk produced better electrical synchrony compared with pacing the distal left bundle fascicles. A visualization technique can facilitate achieving LBTP.

Aims: Despite the highest prevalence of stroke, obesity, and diabetes across races/ethnicities, paradoxically, Hispanic/Latino populations have the lowest prevalence of atrial fibrillation and major Minnesota code-defined ECG abnormalities. We aimed to use Latent Profile Analysis in the Hispanic Community Health Study/Study of Latinos (HCHS/SOL) population to obtain insight into epidemiological discrepancies., Methods and Results: We conducted a cross-sectional analysis of baseline HCHS/SOL visit. Global electrical heterogeneity (GEH) was measured as spatial QRS-T angle (QRSTa), spatial ventricular gradient azimuth (SVGaz), elevation (SVGel), magnitude (SVGmag), and sum absolute QRST integral (SAIQRST). Statistical analysis accounted for the stratified two-stage area probability sample design. We fitted a multivariate latent profile generalized structural equation model adjusted for age, sex, ethnic background, education, hypertension, diabetes, smoking, dyslipidaemia, obesity, chronic kidney disease, physical activity, diet quality, average RR' interval, median beat type, and cardiovascular disease (CVD) to gain insight into the GEH profiles. Among 15 684 participants (age 41 years; 53% females; 6% known CVD), 17% had an increased probability of likely abnormal GEH profile (QRSTa 80 ± 27°, SVGaz -4 ± 21°, SVGel 72 ± 12°, SVGmag 45 ± 12 mVms, and SAIQRST 120 ± 23 mVms). There was a 23% probability for a participant of being in Class 1 with a narrow QRSTa (40.0 ± 10.2°) and large SVG (SVGmag 108.3 ± 22.6 mVms; SAIQRST 203.4 ± 39.1 mVms) and a 60% probability of being in intermediate Class 2., Conclusion: A substantial proportion (17%) in the Hispanic/Latino population had an increased probability of altered, likely abnormal GEH profile, whereas 83% of the population was resilient to harmful risk factors exposures., Competing Interests: Conflict of interest: none declared., (© The Author(s) 2024. Published by Oxford University Press on behalf of the European Society of Cardiology.)

Myocardial infarction (MI) occurs due to the decrease in the blood flow into one part of the heart, and it further causes damage to the heart muscle. The 12-channel electrocardiogram (ECG) has been widely used to detect and localize MI pathology in clinical studies. The vectorcardiogram (VCG) is a 3-channel recording system used to measure the heart's electrical activity in sagittal, transverse, and frontal planes. The VCG signals have advantages over the 12-channel ECG to localize posterior MI pathology. Detection and localization of MI using VCG signals are vital in clinical practice. This paper proposes a multi-channel multi-scale two-stage deep-learning-based approach to detect and localize MI using VCG signals. In the first stage, the multivariate variational mode decomposition (MVMD) decomposes the three-channel-based VCG signal beat into five components along each channel. The multi-channel multi-scale VCG tensor is formulated using the modes of each channel of VCG data, and it is used as the input to the deep convolutional neural network (CNN) to classify MI and normal sinus rhythm (NSR) classes. In the second stage, the multi-class deep CNN is used for the categorization of anterior MI (AMI), anterior-lateral MI (ALMI), anterior-septal MI (ASMI), inferior MI (IMI), inferior-lateral MI (ILMI), inferior-posterior-lateral (IPLMI) classes using MI detected multi-channel multi-scale VCG instances from the first stage. The proposed approach is developed using the VCG data obtained from a public database. The results reveal that the approach has obtained the accuracy, sensitivity, and specificity values of 99.58%, 99.18%, and 99.87%, respectively, for MI detection. Moreover, for MI localization, we have obtained the overall accuracy value of 99.86% in the second stage for our proposed network. The proposed approach has demonstrated superior classification performance compared to the existing VCG signal-based MI detection and localization techniques. [ABSTRACT FROM AUTHOR]

The aim of the work is to compare vectorcardiographic (VCG) variables - spatial QRS-T angle and electrocardiographic ventricular gradient (VG) with echocardiography (EchoCG) data in patients with idiopathic pulmonary hypertension (IPH) and chronic thromboembolic pulmonary hypertension (CTEPH). Materials and methods. In 40 patients with IPH and 40 patients with CTEPH at the age of 45±12 years, systolic pulmonary artery pressure (SPAP); the sizes of heart chambers, parameters of RV systolic and diastolic function were evaluated with EchoCG. The QRS-T and VG angles were calculated on the VCG, derived from 12-lead digital ECG. Results. In all patients SPAP was greater than 40 mm Hg (mean 83±18 mm Hg), EchoCG data indicated hypertrophy and dilatation of RV, its systolic and diastolic function; dilatation of the right atrium (RA). Prognostically unfavorable changes in EchoCG were observed: the presence of pericardial effusion in 35 (44%) patients, RA area greater than 26 cm2 in 18 (23%) patients; TAPSE less than 1.5 cm in 37 (46%) patients. EchoCG and VCG variables had statistically significant differences in patients with III-IV functional class in comparison with I-II functional class. Statistically significant moderate correlations between VCG and EchoCG variables were revealed. VCG variables allowed to separate patient groups with the presence and absence of prognostically unfavorable changes in EchoCG with sensitivity from 54 to 78% and specificity from 66 to 87%. Conclusion. In patients with IPH and CTEPH, changes of QRS-T angle and VG correlate with SPAP, the size of RV and RA, parameters of RV systolic and diastolic function. The possibility of the use of QRS-T angle and VG for the detection of patients with prognostically unfavorable echocardiographic changes in the general group of patients with IPH and CTEPH has been shown.

Background: In the prehospital triage of patients presenting with symptoms suggestive of acute myocardial ischemia, reliable myocardial ischemia detection in the electrocardiogram (ECG) is pivotal. Due to large interindividual variability and overlap between ischemic and nonischemic ECG-patterns, incorporation of a previous elective (reference) ECG may improve accuracy. The aim of the current study was to explore the potential value of serial ECG analysis using subtraction electrocardiography.Methods: SUBTRACT is a multicenter retrospective observational study, including patients who were prehospitally evaluated for acute myocardial ischemia. For each patient, an elective previously recorded reference ECG was subtracted from the ambulance ECG. Patients were classified as myocardial ischemia cases or controls, based on the in-hospital diagnosis. The diagnostic performance of subtraction electrocardiography was tested using logistic regression of 28 variables describing the differences between the reference and ambulance ECGs. The Uni-G ECG Analysis Program was used for state-of-the-art single-ECG interpretation of the ambulance ECG.Results: In 1,229 patients, the mean area-under-the-curve of subtraction electrocardiography was 0.80 (95%CI: 0.77-0.82). The performance of our new method was comparable to single-ECG analysis using the Uni-G algorithm: sensitivities were 66% versus 67% (p-value > .05), respectively; specificities were 80% versus 81% (p-value > .05), respectively.Conclusions: In our initial exploration, the diagnostic performance of subtraction electrocardiography for the detection of acute myocardial ischemia proved equal to that of state-of-the-art automated single-ECG analysis by the Uni-G algorithm. Possibly, refinement of both algorithms, or even integration of the two, could surpass current electrocardiographic myocardial ischemia detection. [ABSTRACT FROM AUTHOR]

Myocardial infarction (MI) occurs due to the decrease in the blood flow into one part of the heart, and it further causes damage to the heart muscle. The 12-channel electrocardiogram (ECG) has been widely used to detect and localize MI pathology in clinical studies. The vectorcardiogram (VCG) is a 3-channel recording system used to measure the heart’s electrical activity in sagittal, transverse, and frontal planes. The VCG signals have advantages over the 12-channel ECG to localize posterior MI pathology. Detection and localization of MI using VCG signals are vital in clinical practice. This paper proposes a multi-channel multi-scale two-stage deep-learning-based approach to detect and localize MI using VCG signals. In the first stage, the multivariate variational mode decomposition (MVMD) decomposes the three-channel-based VCG signal beat into five components along each channel. The multi-channel multi-scale VCG tensor is formulated using the modes of each channel of VCG data, and it is used as the input to the deep convolutional neural network (CNN) to classify MI and normal sinus rhythm (NSR) classes. In the second stage, the multi-class deep CNN is used for the categorization of anterior MI (AMI), anterior-lateral MI (ALMI), anterior-septal MI (ASMI), inferior MI (IMI), inferior-lateral MI (ILMI), inferior-posterior-lateral (IPLMI) classes using MI detected multi-channel multi-scale VCG instances from the first stage. The proposed approach is developed using the VCG data obtained from a public database. The results reveal that the approach has obtained the accuracy, sensitivity, and specificity values of 99.58%, 99.18%, and 99.87%, respectively, for MI detection. Moreover, for MI localization, we have obtained the overall accuracy value of 99.86% in the second stage for our proposed network. The proposed approach has demonstrated superior classification performance compared to the existing VCG signal-based MI detection and localization techniques.

Copyright of Medicina Neotloznyh Sostoanij is the property of Zaslavsky O.Yu and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Patients with Brugada syndrome (BrS) can show a leftward deviation of the frontal QRS-axis upon provocation with sodium channel blockers. The cause of this axis change is unclear. In this study, we aimed to determine (1) the prevalence of this left axis deviation and (2) to evaluate its cause, using the insights that could be derived from vectorcardiograms. Hence, from a large cohort of patients who underwent ajmaline provocation testing (n = 1430), we selected patients in whom a type-1 BrS-ECG was evoked (n = 345). Depolarization and repolarization parameters were analyzed for reconstructed vectorcardiograms and were compared between patients with and without a >30° leftward axis shift. We found (1) that the prevalence of a left axis deviation during provocation testing was 18% and (2) that this left axis deviation was not explained by terminal conduction slowing in the right ventricular outflow tract (4th QRS-loop quartile: +17 ± 14 ms versus +13 ± 15 ms, nonsignificant) but was associated with a more proximal conduction slowing (1st QRS-loop quartile: +12[8;18] ms versus +8[4;12] ms, p < 0.001 and 3rd QRS-loop quartile: +12 ± 10 ms versus +5 ± 7 ms, p < 0.001). There was no important heterogeneity of the action potential morphology (no difference in the ventricular gradient), but a left axis deviation did result in a discordant repolarization (spatial QRS-T angle: 122[59;147]° versus 44[25;91]°, p < 0.001). Thus, although the development of the type-1 BrS-ECG is characterized by a terminal conduction delay in the right ventricle, BrS-patients with a left axis deviation upon sodium channel blocker provocation have an additional proximal conduction slowing, which is associated with a subsequent discordant repolarization. Whether this has implications for risk stratification is still undetermined.

Advanced sensing and internet of things bring the big data, which provides an unprecedented opportunity for data-driven knowledge discovery. However, it is common that a large number of variables (or predictors, features) are involved in the big data. Complex interdependence structures among variables pose significant challenges on the traditional framework of predictive modeling. This paper presents a new methodology of self-organizing network to characterize the interrelationships among variables and cluster them into homogeneous subgroups for predictive modeling. Specifically, we develop a new approach, namely nonlinear coupling analysis to measure variable-to-variable interdependence structures. Further, each variable is represented as a node in the complex network. Nonlinear-coupling forces move these nodes to derive a self-organizing topology of the network. As such, variables are clustered into sub-network communities. Results of simulation experiments demonstrate that the proposed method not only outperforms traditional variable clustering algorithms such as hierarchical clustering and oblique principal component analysis, but also effectively identifies interdependent structures among variables and further improves the performance of predictive modeling. Additionally, real-world case study shows that the proposed method yields an average sensitivity of 96.80% and an average specificity of 92.62% in the identification of myocardial infarctions using sparse parameters of vectorcardiogram representation models. The proposed new idea of self-organizing network is generally applicable for predictive modeling in many disciplines that involve a large number of highly-redundant variables. [ABSTRACT FROM AUTHOR]

Copyright of Medicina Neotloznyh Sostoanij is the property of Zaslavsky O.Yu and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The detection of proarrhythmic effects of many current drugs has prompted the search for complementary non-invasive markers of cardiotoxicity risk. Indices based on studies of cardiac vector dynamics have emerged. We use quaternions to quantify symmetry changes in cardiac vector velocity during ventricular repolarization, which could signal the arrhythmia risk. Principal component analysis is used to homogenize the information and reduce the space to three dimensions. A comparison with the ratio of normalized areas of the repolarization loop (T-wave) and two standard measurements was made: QT and T peak-end intervals. Assessment was conducted by comparing 52 healthy subjects with patients undergoing treatment with Sotalol: 7 recordings with Torsade de Pointes events and 15 without arrhythmic effects. Significant differences (p ¡ 5E-4) were found in both phases of repolarization in terms of absolute velocities and areas. The ratio between the maximums of each parameter in both halves of the T-wave showed a trend towards 1 in the proximity of an arrhythmic event. Angular velocity ratios reached a sensitivity/specificity pair of 100 (95%CI: 59–100)/90 (95%CI: 79–97) with an AUC of 98.6 (95%CI: 95.7–100) in the comparison of healthy population with at-risk patients. Linear discriminant analysis improved the classification by reaching sensitivity and specificity values of 100 (95%CI: 59–100) and 96 (5%CI: 87–100), respectively. The high performance of the method exceeded the diagnostic power of standard measurements, thus showing its potential to contribute to drug evaluation methods. Further research with a larger number of events is required to generalize the method. • Study on cardiac vector velocity and non-invasive risk markers of Torsade de Pointes. • Principal component analysis homogenizes and reduces the information to three axes. • The angular velocity of T-wave shows a trend towards symmetry in at-risk patients. • Novel symmetry indices enhance the differences between at-risk and healthy subjects. [ABSTRACT FROM AUTHOR]

Aims: Left ventricular activation delay due to left bundle branch block (LBBB) is an important determinant of the severity of dyssynchronous heart failure (DHF). We investigated whether patient-specific computational models constructed from non-invasive measurements can provide measures of baseline dyssynchrony and its reduction after CRT that may explain the degree of long-term reverse ventricular remodelling.Methods and Results: LV end-systolic volume reduction (ΔESVLV) measured by 2D trans-thoracic echocardiography in eight patients following 6 months of CRT was significantly (P < 0.05) greater in responders (26 ± 20%, n = 4) than non-responders (11 ± 16%, n = 4). LV reverse remodelling did not correlate with baseline QRS duration or its change after biventricular pacing, but did correlate with baseline LV endocardial activation measured by electroanatomic mapping (R2 = 0.71, P < 0.01). Patient-specific models of LBBB ventricular activation with parameters obtained by matching model-computed vectorcardiograms (VCG) to those derived from standard patient ECGs yielded LV endocardial activation times that correlated well with those measured from endocardial maps (R2 = 0.90). Model-computed 3D LV activation times correlated strongly with the reduction in LVESV (R2 = 0.93, P < 0.001). Computed decreases due to simulated CRT in the time delay between LV septal and lateral activation correlated strongly with ΔESVLV (R2 = 0.92, P < 0.001). Models also suggested that optimizing VV delays may improve resynchronization by this measure of activation delay.Conclusions: Patient-specific computational models constructed from non-invasive measurements can compute estimates of LV dyssynchrony and their changes after CRT that may be as good as or better than electroanatomic mapping for predicting long-term reverse remodelling. [ABSTRACT FROM AUTHOR]

Wilson assumed that the ventricular gradient (VG) is independent of the ventricular activation order. This paradigm has often been refuted and was never convincingly corroborated. We sought to validate Wilson's concept by intra-individual comparison of the VG of sinus beats and ectopic beats, thus assessing the effects of both altered ventricular conduction (caused by the ectopic focus) and restitution (caused by ectopic prematurity). We studied standard diagnostic ECGs of 118 patients with accidental extrasystoles: normally conducted supraventricular ectopic beats (SN, N = 6) and aberrantly conducted supraventricular ectopic beats (SA, N = 20) or ventricular ectopic beats (V, N = 92). In each patient, we computed the VG vectors of the predominant beat, VGp→, of the ectopic beat, VGe→, and of the VG difference vector, ΔVGep→, and compared their sizes. VGe→ of the SA and V ectopic beats were significantly larger than VGp→ (53.7 ± 25.0 vs. 47.8 ± 24.6 mV∙ms, respectively; p < 0.001). ΔVGep→ were three times larger than the difference of VGe→ and VGp→ (19.94 ± 9.76 vs. 5.94 mV∙ms, respectively), demonstrating differences in the VGp→ and VGe→ spatial directions. The amount of ectopic prematurity was not correlated with ΔVGep→, although the larger VG difference vectors were observed for the more premature (<80%) extrasystoles. Electrical restitution properties and electrotonic interactions likely explain our findings. We conclude that the concept of a conduction-independent VG should be tested at equal heart rates and without including premature extrasystoles.

Early diagnosis and localization of myocardial infarction (MI) assist clinicians in saving numerous lives through the timely treatment for patients with MI. Vectorcardiogram (VCG) can reflect the characteristic changes of cardiac electrical activity in MI in detail. In this context, the present study reports a multi-branch weight sharing network model based on 2-D VCG constructed to realize the automatic localization of MI. The three-branch network extracted the spatial morphological features of the three planes of the 2-D VCG, respectively, and the weight-sharing part of the network obtained the spatial correlation information among the three planes. Subsequently, the Softmax classifier was employed to classify normal individuals and MI patients (11 class infarct sites). To evaluate the performance of the proposed method for MI localization, PTB(Physikalisch-Technische Bundesanstalt) diagnostic ECG database was employed. The localization accuracy, sensitivity, and specificity achieved using the proposed method were 99.87%, 99.92%, and 99.99%, respectively. Thus, the proposed scheme is expected to be useful in assisting cardiologists in interpreting VCG for clinical diagnosis. [ABSTRACT FROM AUTHOR]

The electrocardiogram is an important tool for the initial diagnostic suspicion of hypertrophic cardiomyopathy in any of its forms, both in symptomatic and in asymptomatic patients because it is altered in more than 90 percent of the cases. Electrocardiographic anomalies are more common in patients carriers of manifest hypertrophic cardiomyopathy and the electrocardiogram alterations are earlier and more sensitive than the increase in left ventricular wall thickness detected by the echocardiogram. Nevertheless, despite being the leading cause of sudden death among young competitive athletes there is no consensus over the need to include the method in the pre-participation screening. In apical hypertrophic cardiomyopathy the electrocardiographic hallmarks are the giant negative T waves in anterior precordial leads. In the vectorcardiogram, the QRS loop is located predominantly in the left anterior quadrant and T loop in the opposite right posterior quadrant, which justifies the deeply negative T waves recorded. The method allows estimating the left ventricular mass because it relates to the maximal spatial vector voltage of the left ventricle in the QRS loop. The recording on electrocardiogram or Holter monitoring of nonsustained monomorphic ventricular tachycardia in patients with syncope, recurrent syncope in young patient, hypotension induced by strain, bradyarrhythmia, or concealed conduction are markers of poor prognosis. The presence of rare sustained ventricular tachycardia is observed in mid-septal obstructive HCM with apical aneurysm. The presence of complete right bundle branch block pattern is frequent after the percutaneous treatment and complete left bundle branch block is the rule after myectomy. [ABSTRACT FROM AUTHOR]

Aims Previous studies have reported right bundle branch block in Brugada syndrome. Subsequent analysis of electrocardiograms (ECGs) found one-third of cases classified as right bundle branch block did not meet criteria of a wide final S wave in the left leads. We aimed to study the role of the vectorcardiogram to characterize Brugada type 1 ECG pattern. Methods and results Compare Frank-method vectorcardiogram in 11 patients with Brugada type 1 ECG pattern (BrS group) with vectorcardiogram of 20 healthy individuals with ECGs depicting incomplete right bundle branch block (IRBBB group) and 12 patients with complete right bundle branch block (CRBBB group). Initial 10–20 ms vector of the QRS loop in the horizontal plane (HP): BrS and IRBBB groups: Vector heading anterior and leftward. CRBBB group: Vector directed anterior and rightward. Conclusions Vectorcardiograms in patients with Brugada type 1 ECG pattern have distinctive characteristics compared with healthy individuals with incomplete and CRBBB. These differences relate to the spatial location of the end conduction delay (right superior and posterior quadrant in the BrS group) and the morphology, size, and velocity of inscription of afferent and efferent limbs of the T loop (circular, small, of symmetrical limbs) and with a 1:1 length/width ratio. [ABSTRACT FROM PUBLISHER]

Three-dimensional electrogram was used for analysis of ischemia manifestation in isolated hearts. Three parameters based on spherical coordination system were used in this study - amplitude of electrical heart vector, its azimuth and elevation. The parameters were presented as a trend. This approach reflected ischemic changes in a manner which can be easily observed and evaluated. Ischemia was analysed in seven isolated hearts of New Zealand white rabbits. It was found that (a) ischemia changes heart electric vector, (b) ischemic preconditioning has a protective effect, and (c) both of these findings can be clearly observed by the proposed method. [ABSTRACT FROM AUTHOR]

Aims The aim of this study was to assess the prognostic importance of the spatial QRS-T angle for fatal and non-fatal cardiac events.Methods and results Electrocardiograms (ECGs) were recorded in 6134 men and women aged 55 years and over from the prospective population-based Rotterdam Study. Spatial QRS-T angles were categorized as normal, borderline or abnormal. Using Cox's proportional hazards model, abnormal angles showed increased hazard ratios of cardiac death (age-and sex-adjusted hazard ratio 5.2 (95% CI 4.0–6.8)), non-fatal cardiac events (2.2 (1.5–3.1)), sudden death (5.6 (3.7–8.5)) and total mortality (2.3 (2.0–2.7)). None of the classical cardiovascular and ECG predictors provided larger hazard ratios. After adjustment for these predictors, the association of abnormal spatial QRS-T angles with all fatal study endpoints remained strong, but the association with non-fatal cardiac events disappeared. Computation of Akaike's information criterion showed that the angle contributed significantly to the prediction of all fatal endpoints by classical cardiovascular and ECG predictors.Conclusion The spatial QRS-T angle is a strong and independent predictor of cardiac mortality in the elderly. It is stronger than any of the classical cardiovascular risk factors and ECG risk indicators and provides additional value to them in predicting fatal cardiac events. [ABSTRACT FROM PUBLISHER]

• A novel approach to detect and classify PMI from derived VCG signal is proposed. • The FBSE-EWT method with principal component analysis is used to extract features. • Performance of SVM, DT, and KNN classifier is compared. • The KNN classifier resulted in better performance compared to other classifiers. • The proposed method has achieved classification accuracy of 97.92%. The early detection of posterior myocardial infarction (PMI) is an important task as it can cause cardiac failure. Due to the absence of extra posterior leads in the standard 12-lead electrocardiogram (ECG), the PMI detection sensitivity degrades. To improve it, additional posterior leads can be included in standard 12-lead ECG system. Another approach utilizing vectorcardiogram (VCG) has been in practice with specific 7 electrodes with one posterior lead. Although the VCG approach is promising, the arrangement of posterior lead causes patient discomfort. To overcome aforesaid issue, derived VCG (dVCG) obtained through the Dowers inverse transformation has been proposed, wherein, from the standard 12-lead ECG, a three lead VCG is derived, which then can be utilized for PMI detection. Relying on the dVCG approach, in the present paper, we have proposed a novel methodology for PMI detection using Fourier-Bessel series expansion based empirical wavelet transform (FBSE-EWT). The non-stationary behavior of dVCG has been captured using FBSE-EWT, followed by the principal component analysis employing eigenvalues of covariance matrix. The constructed feature space is then fed to decision tree (DT), support vector machine (SVM), and K-nearest neighbor (KNN) classifiers. The KNN classifier with inverse squared city block distance resulted in the overall classification accuracy of 97.92% with sensitivity and specificity of 96.63% and 98.50%, respectively. The experiments were performed over Physikalisch-Technische Bundesanstalt Diagnostic (PTBD) dataset. The proposed method has the potential to be utilized in accurate and robust PMI detection from dVCG signals in the clinical settings. [ABSTRACT FROM AUTHOR]

Introduction: Left bundle branch pacing (LBBP) is a rapidly growing conduction system pacing technique. However, little is known regarding the electrophysiological characteristics of different types of LBBP. We aimed to evaluate the electrophysiological characteristics and anatomic lead location with pacing different branches of the left bundle branch. Methods: Consecutive bradycardia patients with successful LBBP were enrolled and classified into groups according to the paced electrocardiogram and the lead location. Electrocardiogram, pacing properties, vectorcardiogram, and lead tip location were analyzed. Results: Ninety-one patients were enrolled, including 48 with the left bundle trunk pacing (LBTP) and 43 with the left bundle fascicular pacing (LBFP). The paced QRS duration in the LBTP group was significantly shorter than that in the LBFP group (108.1 ± 9.9 vs. 112.9 ± 11.2 ms, p = 0.03), with a more rightward QRS transition zone ( p = 0.01). The paced QRS area in the LBTP group was similar to that during intrinsic rhythm (35.1 ± 15.8 vs. 34.7 ± 16.6 μVs, p = 0.98), whereas in the LBFP group, the paced QRS area was significantly larger compared to intrinsic rhythm (43.4 ± 15.8 vs. 35.7 ± 18.0 μVs, p = 0.01). The lead tip site for LBTP was located in a small fan-shaped area with the tricuspid valve annulus summit as the origin, whereas fascicular pacing sites were more likely in a larger and more distal area. Conclusions: Pacing the proximal left bundle main trunk produced better electrical synchrony compared with pacing the distal left bundle fascicles. A visualization technique can facilitate achieving LBTP., Competing Interests: XZ is an employee of Medtronic. MGo receives consulting fees from Medtronic and Boston Scientific. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Liu, Gu, Niu, Chen, Cai, Zhao, Cai, Zhou, Gold, Zhang and Hua.)

• Inaccuracies of QRS and T-wave markers significantly impact QRS-Ta estimation. • These errors influence the classification of clinically relevant abnormal values. • Our algorithm provides robust measurements in the presence of inaccurate VCG markers. • We present for the first time, the distribution of the QRS-Ta in a large cohort. The spatial QRS-T angle (QRS-Ta) derived from the vectorcardiogram (VCG) is a strong risk predictor for ventricular arrhythmia and sudden cardiac death with potential use for mass screening. Accurate QRS-Ta estimation in the presence of ECG delineation errors is crucial for its deployment as a prognostic test. Our study assessed the effect of inaccurate QRS and T-wave marker placement on QRS-Ta estimation and proposes a robust method for its calculation. Reference QRS-Ta measurements were derived from 1,512 VCGs manually annotated by three expert reviewers. We systematically changed onset and offset timings of QRS and T-wave markers to simulate inaccurate placement. The QRS-Ta was recalculated using a standard approach and our proposed algorithm, which limits the impact of VCG marker inaccuracies by defining the vector origin as an interval preceding QRS-onset and redefines the beginning and end of QRS and T-wave loops. Using the standard approach, mean absolute errors (MAE) in peak QRS-Ta were >40% and sensitivity and precision in the detection of abnormality (>105°) were <80% and <65% respectively, when QRS-onset was delayed or QRS-offset anticipated >15 ms. Using our proposed algorithm, MAE for peak QRS-Ta were reduced to <4% and sensitivity and precision of abnormality were >94% for inaccuracies up to ±15 ms. Similar results were obtained for mean QRS-Ta. In conclusion, inaccuracies of QRS and T-wave markers can significantly influence the QRS-Ta. Our proposed algorithm provides robust QRS-Ta measurements in the presence of inaccurate VCG annotation, enabling its use in large datasets. [ABSTRACT FROM AUTHOR]