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Background Objectives Animals Methods Results Conclusions and Clinical Importance Reliable ECGs are crucial for diagnosing arrhythmias, yet a lack of standardization impedes arrhythmia diagnosis and treatment in horses.To objectively determine an optimal position of Einthoven's triangle for ECG recordings in horses at rest, which can form the basis for standardized ECG recording and improve diagnosis and treatment of arrhythmias.The study involved 72 healthy, warmblood horses aged between 3 and 20 years.In view of future 12‐lead studies and vectorcardiography, requiring an orthogonal system, Einthoven's triangle was positioned around the heart, in the transverse plane. Therefore, 11 electrodes were placed encircling the thorax behind the olecranon, to construct triangles with a horizontal base. Electrocardiogram recordings from different triangles were analyzed. Signal processing involved filtering, R peak detection, and median complex generation. Principal component analysis (PCA) and Euclidean distance measures were employed for data analysis.The left mid‐thoracic and ventral regions had high PCA scores, indicating high information content. Base‐down triangles exhibited higher summed Euclidean distances, contributing to enhanced diagnostic capabilities. A base‐down triangle, called “Delta (Δ) configuration” emerged as most informative, while meeting all criteria.The base‐down “Delta configuration” is the optimal Einthoven's triangle adapted for horses, providing large amplitudes and potential to provide basic insights into the mechanisms and origins of cardiac arrhythmias. Because the Delta configuration is positioned in the transverse plane, it forms the ideal basis for 12‐lead ECG recordings that provide vectorcardiograms in an orthogonal coordinate system. Standardizing electrode positioning could improve ECG data comparability in equine cardiology. [ABSTRACT FROM AUTHOR]

Context: Cardiogoniometry (CGM) is a noninvasive technique for quantitative three-dimensional vectorial analysis of myocardial depolarization and repolarization. We describe a method of surface electrophysiological cardiac assessment using CGM performed at rest to detect variables helpful in identifying coronary artery disease (CAD) and interplaying factors in patients who underwent coronary artery bypass graft (CABG). Materials and Methods: In this study, all patients with CAD undergoing CABG were enrolled. Echocardiography, CGM, and electrocardiography (ECG) were obtained before surgery and a month later. The ECG and CGM alterations were compared before and after CABG and the accuracy of those two methods was evaluated. Results: In this study, 50 patients were enrolled, of which 33 (66%) were male versus 17 (44%) female, with a mean age of 39.12 ± 60 years. The mean ejection fraction before and after surgery was 44.3 ± 7.9% (range: 20–55) and 40.9 ± 12.5% (20–50), respectively. Before surgery, all patients had sinus rhythm, five cases had left bundle-branch block (LBBB), and one case was with right bundle-branch block (RBBB). Subsequently, after surgery, five cases had LBBB, and two cases were found to have RBBB. The rate of postoperation bleeding was 20%. There was no significant change in echocardiographic characteristics before and after operation (P = 0.81); likewise, no significant change was seen in ECG criteria before and after operation (P = 0.96). A significant correlation was seen regarding CGM characteristics before and after CABG (P = 0.003). Conclusions: A significant association was seen regarding CGM characteristics before and after undergoing an operation. In addition, the specificity and sensitivity of the CGM and ECG before and after operation improved well.

Aims Typical electrocardiogram (ECG) features of apical hypertrophic cardiomyopathy (ApHCM) include tall R waves and deep or giant T-wave inversion in the precordial leads, but these features are not always present. The ECG is used as the gatekeeper to cardiac imaging for diagnosis. We tested whether explainable advanced ECG (A-ECG) could accurately diagnose ApHCM. Methods and results Advanced ECG analysis was performed on standard resting 12-lead ECGs in patients with ApHCM [ n = 75 overt, n = 32 relative (<15 mm hypertrophy); a subgroup of which underwent cardiovascular magnetic resonance (n = 92)], and comparator subjects (n = 2449), including healthy volunteers (n = 1672), patients with coronary artery disease (n = 372), left ventricular electrical remodelling (n = 108), ischaemic (n = 114) or non-ischaemic cardiomyopathy (n = 57), and asymmetrical septal hypertrophy HCM (n = 126). Multivariable logistic regression identified four A-ECG measures that together discriminated ApHCM from other diseases with high accuracy [area under the receiver operating characteristic (AUC) curve (bootstrapped 95% confidence interval) 0.982 (0.965–0.993)]. Linear discriminant analysis also diagnosed ApHCM with high accuracy [AUC 0.989 (0.986–0.991)]. Conclusion Explainable A-ECG has excellent diagnostic accuracy for ApHCM, even when the hypertrophy is relative, with A-ECG analysis providing incremental diagnostic value over imaging alone. The electrical (ECG) and anatomical (wall thickness) disease features do not completely align, suggesting that future diagnostic and management strategies may incorporate both features. [ABSTRACT FROM AUTHOR]

Abstract Background Coronary artery calcification (CAC) is associated with poor outcome in critically ill patients. A deterioration in cardiac conduction and loss of myocardial tissue could be an underlying cause. Vectorcardiography (VCG) and cardiac biomarkers provide insight into these underlying causes. The aim of this study was to investigate whether a high degree of CAC is associated with VCG-derived variables and biomarkers, including high-sensitivity troponin-T (hs-cTnT) and N-terminal pro-B-type natriuretic peptide (NT-proBNP). Methods Mechanically ventilated coronavirus-19 (COVID-19) patients with an available chest computed tomography (CT) and 12-lead electrocardiogram (ECG) were studied. CAC scores were determined using chest CT scans. Patients were categorized into 3 sex-specific tertiles: low, intermediate, and high CAC. Daily 12 leads-ECGs were converted to VCGs. Daily hs-cTnT and NT-proBNP levels were determined. Linear mixed-effects regression models examined the associations between CAC tertiles and VCG variables, and between CAC tertiles and hs-cTnT or NT-proBNP levels. Results In this study, 205 patients (73.2% men, median age 65 years [IQR 57.0; 71.0]) were included. Compared to the lowest CAC tertile, the highest CAC tertile had a larger QRS area at baseline (6.65 µVs larger [1.50; 11.81], p = 0.012), which decreased during admission (− 0.27 µVs per day [− 0.43; − 0.11], p = 0.001). Patients with the highest CAC tertile also had a longer QRS duration (12.02 ms longer [4.74; 19.30], p = 0.001), higher levels of log hs-cTnT (0.79 ng/L higher [0.40; 1.19], p

Background: Coronary artery calcification (CAC) is associated with poor outcome in critically ill patients. A deterioration in cardiac conduction and loss of myocardial tissue could be an underlying cause. Vectorcardiography (VCG) and cardiac biomarkers provide insight into these underlying causes. The aim of this study was to investigate whether a high degree of CAC is associated with VCG-derived variables and biomarkers, including high-sensitivity troponin-T (hs-cTnT) and N-terminal pro-B-type natriuretic peptide (NT-proBNP). Methods: Mechanically ventilated coronavirus-19 (COVID-19) patients with an available chest computed tomography (CT) and 12-lead electrocardiogram (ECG) were studied. CAC scores were determined using chest CT scans. Patients were categorized into 3 sex-specific tertiles: low, intermediate, and high CAC. Daily 12 leads-ECGs were converted to VCGs. Daily hs-cTnT and NT-proBNP levels were determined. Linear mixed-effects regression models examined the associations between CAC tertiles and VCG variables, and between CAC tertiles and hs-cTnT or NT-proBNP levels. Results: In this study, 205 patients (73.2% men, median age 65 years [IQR 57.0; 71.0]) were included. Compared to the lowest CAC tertile, the highest CAC tertile had a larger QRS area at baseline (6.65 µVs larger [1.50; 11.81], p = 0.012), which decreased during admission (− 0.27 µVs per day [− 0.43; − 0.11], p = 0.001). Patients with the highest CAC tertile also had a longer QRS duration (12.02 ms longer [4.74; 19.30], p = 0.001), higher levels of log hs-cTnT (0.79 ng/L higher [0.40; 1.19], p < 0.001) and log NT-proBNP (0.83 pmol/L higher [0.30; 1.37], p = 0.002). Conclusion: Patients with a high degree of CAC had the largest QRS area and higher QRS amplitude, which decreased more over time when compared to patients with a low degree of CAC. These results suggest that CAC might contribute to loss of myocardial tissue during critical illness. These insights could improve risk stratification and prognostication of patients with critical illness. [ABSTRACT FROM AUTHOR]

This paper reviews the literature on assessing electrical dyssynchrony for patient selection in cardiac resynchronization therapy (CRT). The guideline-recommended electrocardiographic (ECG) criteria for CRT are QRS duration and morphology, established through inclusion criteria in large CRT trials. However, both QRS duration and LBBB morphology have their shortcomings. Over the past decade, various alternative measures of ventricular dyssynchrony have been proposed, ranging from simple options such as vectorcardiography (VCG), ultra-high frequency ECG, and electrical dyssynchrony mapping to more advanced techniques such as ECG imaging electro-anatomic mapping. Despite promising results, none of these methods have yet been widely adopted in daily clinical practice. The VCG is a relatively cost-effective option for potential clinical implementation, as it can be reconstructed from the standard 12‑lead ECG.With the emergence of conduction system pacing, in addition to predicting the outcome of conventional biventricular CRT, the assessment of electrical dyssynchrony holds promise for defining and optimizing the type of resynchronization strategy. Additionally, artificial intelligence has the potential to reveal unknown features for CRT outcomes, and computer models can provide deeper insights into the underlying mechanisms of these features.

Vectorcardiography (VCG) is a valuable diagnostic tool that complements the standard 12-lead ECG by offering additional spatiotemporal information to clinicians. However, due to the need for additional measurement hardware and too many electrodes in a clinical scenario if performed along with a standard 12-lead, there is a need to find methods to derive the VCG from the ECG. We have evaluated the use of Long Short-term Memory (LSTM) neural networks to learn the transformation from 12-lead ECG to VCG that is applicable across subjects and for each subject. We refer to these networks as generalized and personalized, respectively. We calculated the Root Mean Square Error (RMSE), R2, and Pearson correlation coefficient to compare waveforms of derived and actual VCG. We also extracted and compared diagnostic parameters from VCG, namely the QRS-loop magnitude, T-loop magnitude, and QRS-T spatial angle, from actual and derived VCGs using the Pearson correlation coefficient and Bland Altman limits of agreement. The personalized models performed better than generalized models in waveform comparisons and in the error of extracted diagnostic parameters from VCG waveforms. The use of personalized transformations for the derivation of VCG from standard 12-lead has the potential to improve and augment the diagnostic yield and accuracy of a standard 12-lead interpretation.

OBJECTIVE Atherosclerosis and associated coronary artery disease may complicate the stenosis of larger sized arteries such as abdominal aorta, ileac and carotid arteries. Undetected stable coronary artery disease may increase the morbidity and mortality of such surgical interventions. In traditional medical approach, a coronary angiography is usually performed prior to peripheral arterial revascularization in order to rule out coronary arterial involvement. Herein, we would like to share two cases whom an artificial intelligence (AI) analyzed vectorcardiography was used for the abovementioned purpose. The sensitivity of AI-assisted Cardisiography in order to exclude stable coronary artery disease is 97% for males and 90% for females. METHODS Cardisiography focuses on spatial and temporal heterogeneity of cardiac excitation using vectorcardiography with a five-fold nested cross-validation in comparison to angiographic findings1. Two patients, one male with the diagnosis of Leriche Syndrome and one female with bilateral carotid artery stenosis were evaluated with AI-assisted Cardisiography prior to coronary angiography. Cardisiography revealed no significant probability with regard to vectorcardiography and AI based supervised machine learning algorhythms. Afterwards, conventional coronary angiographies were performed and any stenosis in the coronary vascular bed was excluded. Case-1: 42 years old male who had symptoms and clinical findings of Leriche Syndrome (claudication, erectile impotence and diminished peripheral pulses) (Figure-1). Following preoperative evaluation, an aorta-bifemoral bypass graft was interposed uneventfully. Case-2: 55 years old female patient was admitted to our clinic with dizziness and transient ischemic attack. Carotis artery angiography revealed critical stenosis at the left and moderate stenosis at the right internal carotid arteries (Figure-2). A carotid endarterectomy and saphenous vein patch plasty were uneventfully performed. RESULTS Coronary artery involvement in peripheral arterial disease is an important association, since ruling out a critical coronary artery stenosis is deemed mandatory prior to revascularization of carotid arteries, abdominal aorta, ileac arteries and distal branches. AI-assisted Cardisiography may be used as a noninvasive alternative method in the diagnosis of stable coronary artery disease, especially when surgery for carotid artery stenosis is based on Doppler ultrasonographic findings alone (i.e., without performing CT angiography or conventional angiography prior to surgery). CONCLUSIONS AI-assisted Cardisiography may be used as an alternative non-invasive method in order to rule out stable coronary artery disease in patients undergoing peripheral vascular surgery, decreasing the need for conventional angiography. Further large-scale clinical trials will be needed, before further assumptions can be drawn. [ABSTRACT FROM AUTHOR]

Vectorcardiography (VCG) is a valuable diagnostic tool that complements the standard 12-lead ECG by offering additional spatiotemporal information to clinicians. However, due to the need for additional measurement hardware and too many electrodes in a clinical scenario if performed along with a standard 12-lead, there is a need to find methods to derive the VCG from the ECG. We have evaluated the use of Long Short-term Memory (LSTM) neural networks to learn the transformation from 12-lead ECG to VCG that is applicable across subjects and for each subject. We refer to these networks as generalized and personalized, respectively. We calculated the Root Mean Square Error (RMSE), R2, and Pearson correlation coefficient to compare waveforms of derived and actual VCG. We also extracted and compared diagnostic parameters from VCG, namely the QRS-loop magnitude, T-loop magnitude, and QRS-T spatial angle, from actual and derived VCGs using the Pearson correlation coefficient and Bland Altman limits of agreement. The personalized models performed better than generalized models in waveform comparisons and in the error of extracted diagnostic parameters from VCG waveforms. The use of personalized transformations for the derivation of VCG from standard 12-lead has the potential to improve and augment the diagnostic yield and accuracy of a standard 12-lead interpretation. [ABSTRACT FROM AUTHOR]

Background: The spatial ventricular gradient (SVG) is a vectorcardiographic measurement that reflects cardiac loading conditions via electromechanical coupling. Objectives: We hypothesized that the SVG is correlated with right ventricular (RV) strain and is prognostic of adverse events in patients with acute pulmonary embolism (PE). Methods: Retrospective, single‐center study of patients with acute PE. Electrocardiogram (ECG), imaging, and outcome data were obtained. SVG components were regressed on tricuspid annular plane systolic excursion (TAPSE), qualitative RV dysfunction, and RV/left ventricular (LV) ratio. Odds of adverse outcomes (30‐day mortality, vasopressor requirement, or advanced therapy) after PE were regressed on demographics, RV/LV ratios, traditional ECG signs of RV dysfunction, and SVG components using a logit model. Results: ECGs from 317 patients (48% male, age 63.1 ± 16.6 years) with acute PE were analyzed; 36 patients (11.4%) experienced an adverse event. Worse RV hypokinesis, larger RV/LV ratio, and smaller TAPSE were associated with smaller SVG X and Y components, larger SVG Z components, and smaller SVG vector magnitude (p <.001 for all). In multivariable logistic regression, odds of adverse events after PE decreased with increasing SVG magnitude and TAPSE (OR 0.32 and 0.54 per standard deviation increase; p =.03 and p =.004, respectively). Receiver operating characteristic (ROC) analysis showed that, when combined with imaging, replacing traditional ECG criteria with the SVG significantly improved the area under the ROC from 0.70 to 0.77 (p =.01). Conclusion: The SVG is correlated with RV dysfunction and adverse outcomes in acute PE and has a better prognostic value than traditional ECG markers. [ABSTRACT FROM AUTHOR]

The vector concept in the analysis of the electrical signals of the heart began to be used at the dawn of the development of electrocardiology. For several decades, vectorcardiography has developed in parallel with electrocardiography; reached its peak in the 60s, and after a period of cooling experienced a resurgence since the early 90s, when it became possible to mathematically synthesize vectorcardiograms (VCG) from digital electrocardiograms in 12 leads. VCG reflects the same phenomena as electrocardiography, but allows you to calculate and visualize a number of three-dimensional characteristics of the electrical signals of the heart. The article describes the main milestones in the development of the VCG, the history of international cooperation in this area, the contribution of domestic scientists to this field of science. Modern promising areas of research related to the vector concept of the analysis of the electrical signals of the heart are briefly reflected.

Introduction: Cryotherapy is a therapeutic method used to reduce pain associated with, among others, injuries and inflammation by both young athletes and the elderly. Aim of the research: To investigate the effect of a single cryochamber session on basic ECG-derived parameters in adult non-athletes. Material and methods: A hundred and eight participants without significant cardiac disorders were included in the study. A single session in the cryochamber lasted for 3 min, and the temperature within the appropriate chamber ranged from -150°C to -160°C. When in the cryochamber, each participant wore a tank top, shorts, a surgical mask, a cap, woollen gloves, socks, and wooden clogs. ECG parameters, as well as systolic and diastolic blood pressure were recorded both before and immediately after the cryochamber session. Moreover, vectorcardiographic parameters were generated according to the inverse Dower matrix. Results: The study was performed on 72 females and 36 males with an average age of 50 years; no significant differences were demonstrated between these groups. We observed a statistically significant decrease in the heart rate and QTc interval after the cryochamber session, as well as a minor increase of QRS interval following the cryochamber session. No changes in the spatial QRS-T angle were observed. Conclusions: Based on the collected results, whole-body cryotherapy in a cryochamber seems to be a harmless additional treatment for non-athlete adults without known contraindications for such therapy. Long-term prospective studies are necessary to confirm these results and to prove the safety of cryochambers on a larger group of subjects. [ABSTRACT FROM AUTHOR]

Abstract Background The spatial ventricular gradient (SVG) is a vectorcardiographic measurement that reflects cardiac loading conditions via electromechanical coupling. Objectives We hypothesized that the SVG is correlated with right ventricular (RV) strain and is prognostic of adverse events in patients with acute pulmonary embolism (PE). Methods Retrospective, single‐center study of patients with acute PE. Electrocardiogram (ECG), imaging, and outcome data were obtained. SVG components were regressed on tricuspid annular plane systolic excursion (TAPSE), qualitative RV dysfunction, and RV/left ventricular (LV) ratio. Odds of adverse outcomes (30‐day mortality, vasopressor requirement, or advanced therapy) after PE were regressed on demographics, RV/LV ratios, traditional ECG signs of RV dysfunction, and SVG components using a logit model. Results ECGs from 317 patients (48% male, age 63.1 ± 16.6 years) with acute PE were analyzed; 36 patients (11.4%) experienced an adverse event. Worse RV hypokinesis, larger RV/LV ratio, and smaller TAPSE were associated with smaller SVG X and Y components, larger SVG Z components, and smaller SVG vector magnitude (p

Vectorcardiography (VCG) as an alternative form of 12-lead ECG is another method of measuring the electrical activity of the heart. The use of vectorcardiography in clinical practice is not common, but VCG leads can be derived from 12-lead ECG. VCG has proven to be a useful and more accurate tool for diagnosing various heart diseases within automated detection. This paper presents the application of four transformation methods namely: Kors regression, IDT, QLSV and Quasi-Orthogonal transformation to obtain a derived VCG. A total of 20 physiological and 20 records with the diagnosis of myocardial infarction were used. For physiological records, the Kors regression method achieved the best results in leads X and Y with relative deviation 99%. In lead Z, the QLSV method achieved the most accurate results with relative deviation 98% and percentage similarity >99%. For pathological records, the most accurate method in all leads was Kors regression with relative deviation 93% and percentage similarity >97%. From these results, there is the possibility of creating a new transformation method from the existing ones in order to obtain a more accurate transformation.

BackgroundWhile primarily a right heart disease, pulmonary arterial hypertension (PAH) can impact left heart function and aortic flow through a shifted interventricular septum from right ventricular pressure overload and reduced left ventricular preload, among other mechanisms. In this study, we used phase contrast (PC) MRI and a modest exercise challenge to examine the effects of PAH on systemic circulation. While exercise challenges are typically performed with ultrasound in the clinic, MRI exercise studies allow for more reproducible image alignment, more accurate flow quantification, and improved tissue contrast.MethodsSix PAH patients and fifteen healthy controls (8 older age-matched, 7 younger) exercised in the magnet bore with an MRI-compatible exercise device that allowed for scanning immediately following cessation of exercise. PC scans were performed in the ascending aorta during a breath hold immediately after modest exercise to non-invasively measure stroke volume (SV), cardiac output (CO), aortic peak systolic flow (PSF), and aortic wall stiffness via relative area change (RAC).ResultsImages following exercise showed mild blurring, but were high enough quality to allow for segmentation of the aorta. While SV was approximately 30% lower in PAH patients (SVPAH,rest = 67 ± 16 mL; SVPAH,stress = 90 ± 42 mL) than age-matched controls (SV,older,rest = 93 ± 16 mL; SVolder,stress = 133 ± 40 mL) at both rest and following exercise, CO was similar for both groups following exercise (COPAH,stress = 10.8 ± 5.7 L/min; COolder,stress = 11.8 ± 5.0 L/min). This was achieved through a compensatory increase in heart rate in the PAH subjects (74% increase as compared to 29% in age-matched controls). The PAH subjects also demonstrated reduced aortic peak systolic flow relative to the healthy controls (PSFPAH,rest = 309 ± 52 mL/s; PSFolder,rest = 416 ± 114 mL/s; PSFPAH,stress = 388 ± 113 mL/s; PSFolder,stress = 462 ± 176 mL/s). PAH patients and older controls demonstrated stiffer aortic walls when compared to younger controls (RACPAH,rest = 0.15 ± 0.05; RAColder,rest = 0.17 ± 0.05; RACyoung,rest = 0.28 ± 0.08).ConclusionsPC MRI following a modest exercise challenge was capable of detecting differences in left heart dynamics likely induced from PAH. These results demonstrated that PAH can have a significant influence on systemic flow, even when the patient has no prior left heart disease. Image quantification following exercise could likely be improved in future studies through the implementation of free-breathing or real-time MRI acquisitions.Trial registrationRetrospectively registered on 02/26/2018 (TRN: NCT03523910 ).

There are numerous sex-related differences in cardiac electrophysiology and arrhythmia propensity but very little knowledge about the reasons. Difference in body size has been proposed as one reason and was tested in this study of >20 cardiac electrophysiology parameters in 319 (158 women) apparently healthy 50- to 64-yr-old subjects from a randomly enrolled population sample, the pilot SCAPIS (Swedish Cardiopulmonary Bioimaging Study), using Frank vectorcardiography. We studied conventional conduction intervals, parameters reflecting electrical heterogeneity (dispersion) in the ventricles, QRS- and T-vector directions, spatial QRS-T angles, and T-vector loop morphology. Body surface area (BSA; 2 methods) and lean body mass (LBM), both estimated from body weight and height, were used as body size parameters. According to multivariable linear regression analysis adjusted for sex, there was no association between electrophysiological parameters and body size apart from QRS duration and QRSarea. In conclusion, most electrophysiological parameters assessed completely noninvasively and showing statistically significant differences between women and men on the group level show no association with BSA or LBM. Scaling (indexing) the electrophysiological parameters for body size parameters is therefore not an option. Consequently, the explanation for the sex-related electrophysiological differences should be sought along other lines. NEW & NOTEWORTHY We sought explanations for sex-related differences in >20 cardiac electrophysiology parameters including conventional conduction intervals in 319 (158 women) apparently healthy 50- to 64-yr-old subjects using Frank vectorcardiography, a novelty. Our hypothesis that body size was partly explanatory for such differences had to be refuted apart from QRS duration and QRSarea. Scaling (indexing) electrophysiological parameters for body size is therefore not an option and explanations for electrophysiological sex-related differences are to be sought elsewhere. [ABSTRACT FROM AUTHOR]

This paper deals with a wavelet-based algorithm for automatic detection of isoelectric coordinates of individual QRS loops of VCG record. Fiducial time instants of QRS peak, QRS onset, QRS end, and isoelectric PQ interval are evaluated on three VCG leads (X, Y, Z) together with global QRS boundaries of a record to spatiotemporal QRS loops alignment. The algorithm was developed and optimized on 161 VCG records of PTB diagnostic database of healthy control subjects (HC), patients with myocardial infarction (MI) and patients with bundle branch block (BBB) and validated on CSE multilead measurement database of 124 records of the same diagnostic groups. The QRS peak was evaluated correctly for all of 1,467 beats. QRS onset, QRS end were detected with standard deviation of 5,5 ms and 7,8 ms respectively from the referee annotation. The isoelectric 20 ms length PQ interval window was detected correctly between the P end and QRS onset for all the cases. The proposed algorithm complies the (2sCSE) limits for the QRS onset and QRS end detection and provides comparable or better results to other well-known algorithms. The algorithm evaluates well a wide QRS based on automated wavelet scale switching. The designed multi-lead approach QRS loop detector accomplishes diagnostic VCG processing, aligned QRS loops imaging and it is suitable for beat-to-beat variability assessment and further automatic VCG classification. [ABSTRACT FROM AUTHOR]

Theoretical grounds of integral vectors of ventricular depolarization and repolarization and their sum, i.e., the spatial ventricular gradient, have been studied. A systematic description and biophysical interpretation of these parameters are presented based on the distribution of cardiomyocyte action potential parameters in the inhomogeneous bidomain model of the myocardium. Recent medical studies have shown high efficiency and predictive value of the ventricular gradient, its constituents and related parameters, such as the angle between the con-stituents, the acceleration of repolarization, etc. Simple examples for a myocardial strip clarify the relationship between the action potential parameters and the resulting ventricular gradient. An explanation with graphic illustration is given for the very informative decartogram of repolarization acceleration. The results obtained here are useful in the modeling of vectorcardiograms for various pathological conditions of the heart ventricles and for various characteristics of the cardiomyocyte action potential, which determine its shape.

Objective: Vectorcardiography (VCG) as an alternative form of ECG provides important spatial information about the electrical activity of the heart. It achieves higher sensitivity in the detection of some pathologies such as myocardial infarction, ischemia and hypertrophy. However, vectorcardiography is not commonly measured in clinical practice, and for this reason mathematical transformations have been developed to obtain derived VCG leads, which in application in current systems and subsequent analysis can contribute to early diagnosis and obtaining other useful information about the electrical activity of the heart. Methods and procedures: The most frequently used transformation methods are compared, namely the Kors regression method, the Inverse Dower transformation, QLSV and the Quasi-orthogonal transformation. These transformation methods were used on 30 randomly selected records with a diagnosis of myocardial infarction from the Physikalisch-Technische Bundesanstalt (PTB) database and their accuracy was evaluated based on the calculation of the mean square error (MSE). MSE was subjected to statistical evaluation at a significance level of 0.05. Results: Based on statistical testing using the nonparametric multiselective Kruskall-Wallis test and subsequent post-hoc analysis using the Dunn method, the Kors regression as a whole method achieved the most accurate transformation. Conclusion: The results of statistical analysis provide an evaluation of the accuracy of several transformation methods for deriving orthogonal leads, for possible application in measuring and evaluation systems, which may contribute to the correct choice of method for subsequent analysis of electrical activity of the heart at orthogonal leads to predict various diseases.

Background. The incidence of arterial hypertension continues to grow. By 2025 the number of patients will increase to 1.5 billion that makes it necessary to develop and implement effective approaches in the diagnosis and treatment of hypertension in practical health care. Aim. Evaluate the effectiveness of two-level diagnostics of left ventricular hypertrophy (LVH). Materials and methods. The distance of the first level of LVH diagnostics in outpatient departments with a territorial is more than 20 km. It was performed by remote electrocardiography, including medical examination of the population. Automatic conclusion of the devices was sent to the personal office of the functional diagnostics doctor. At the second level, the electrocardiograph software was used to convert ECG-12 into a vectorcardiogram (VKG) to clarify LVH. The ECG reports were returned to the device of the doctor/paramedic for further work with the patient. When using scalar ECG criteria for LVH, the most sensitive index was the SokolovLyon index 24.2% of cases, then the Cornell product 19.8% of cases. In the second level of ECG diagnostics, the sensitivity of the method in detecting LVH increased more than 3 times: in terms of the maximum loop vector modulus (M QRS 1.8 mV), 72%, the sensitivity of the total index Rx+Sz (2.7 mV) was 58% of cases, the deviation of the spatial angle QRS-T90 was 42.5%, the spatial area of the loop QRS (S QRSxyz 2.7 mV2), the area of the loop QRS in the horizontal plane (S QRSxz2.2 mV2) 41.5%, the orientation angle of the maximum loop vector t in the horizontal plane (angle Txz70) was detected in 38% of patients. These indicators were found with the same frequency in both groups of examined individuals. Conclusion. The use of remote two-level ECG and ECG diagnostics allowed to increase the detection of LVH in rural and urban populations by 3 times.

This paper deals with a wavelet-based algorithm for automatic detection of isoelectric coordinates of individual QRS loops of VCG record. Fiducial time instants of QRS peak, QRS onset, QRS end, and isoelectric PQ interval are evaluated on three VCG leads (X, Y, Z) together with global QRS boundaries of a record to spatiotemporal QRS loops alignment. The algorithm was developed and optimized on 161 VCG records of PTB diagnostic database of healthy control subjects (HC), patients with myocardial infarction (MI) and patients with bundle branch block (BBB) and validated on CSE multilead measurement database of 124 records of the same diagnostic groups. The QRS peak was evaluated correctly for all of 1,467 beats. QRS onset, QRS end were detected with standard deviation of 5,5 ms and 7,8 ms respectively from the referee annotation. The isoelectric 20 ms length PQ interval window was detected correctly between the P end and QRS onset for all the cases. The proposed algorithm complies the (2σCSE) limits for the QRS onset and QRS end detection and provides comparable or better results to other well-known algorithms. The algorithm evaluates well a wide QRS based on automated wavelet scale switching. The designed multi-lead approach QRS loop detector accomplishes diagnostic VCG processing, aligned QRS loops imaging and it is suitable for beat-to-beat variability assessment and further automatic VCG classification.

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. [ABSTRACT FROM AUTHOR]

BACKGROUND: Arrhythmogenic right ventricular dysplasia/cardiomyopathy (ARVD/C) carries a risk of sudden death. We aimed to assess whether vectorcardiographic (VCG) parameters directed toward the right heart and a measured angle of the S-wave would help differentiate ARVD/C with otherwise normal electrocardiograms from controls. METHODS: Task Force 2010 definite ARVD/C criteria were met for all patients. Those who did not fulfill Task Force depolarization or repolarization criteria (-ECG) were compared with age and gender-matched control subjects. Electrocardiogram measures of a 3-dimentional spatial QRS-T angle, a right-precordial-directed orthogonal QRS-T (RPD) angle, a root mean square of the right sided depolarizing forces (RtRMS-QRS), QRS duration (QRSd) and the corrected QT interval (QTc), and a measured angle including the upslope and downslope of the S-wave (S-wave angle) were assessed. RESULTS: Definite ARVD/C was present in 155 patients by 2010 Task Force criteria (41.7 ± 17.6 years, 65.2% male). -ECG ARVD/C patients (66 patients) were compared to 66 control patients (41.7 ± 17.6 years, 65.2% male). All parameters tested except the QRSd and QTc significantly differentiated -ECG ARVD/C from control patients (p

Vectorcardiography (VCG) as an alternative form of 12-lead ECG is another method of measuring the electrical activity of the heart. The use of vectorcardiography in clinical practice is not common, but VCG leads can be derived from 12-lead ECG. VCG has proven to be a useful and more accurate tool for diagnosing various heart diseases within automated detection. This paper presents the application of four transformation methods namely: Kors regression, IDT, QLSV and Quasi-Orthogonal transformation to obtain a derived VCG. A total of 20 physiological and 20 records with the diagnosis of myocardial infarction were used. For physiological records, the Kors regression method achieved the best results in leads X and Y with relative deviation < 1%, correlation and percentage similarity > 99%. In lead Z, the QLSV method achieved the most accurate results with relative deviation < 1%, correlation > 98% and percentage similarity > 99%. For pathological records, the most accurate method in all leads was Kors regression with relative deviation < 2.2%, correlation > 93% and percentage similarity > 97%. From these results, there is the possibility of creating a new transformation method from the existing ones in order to obtain a more accurate transformation. [ABSTRACT FROM AUTHOR]

BACKGROUND: Patients with tetralogy of Fallot (TOF) have increased risk of atrial arrhythmias. HYPOTHESIS: A measure of atrial dispersion, the P-wave vector magnitude (Pvm), can identify patients at risk for perioperative atrial flutter (AFL) or intra-atrial re-entrant tachycardia (IART) in a large TOF cohort. METHODS: We performed a blinded, retrospective analysis of 158 TOF patients undergoing pulmonary valve replacement between 1997 and 2015. History of AFL/IART was documented using electrocardiogram, Holter monitor, exercise stress test, implanted cardiac device, and electrophysiology study. P-R intervals, Pvm, QRS duration, and QRS vector magnitude were assessed from resting sinus-rhythm 12-lead electrocardiograms and identification of those with AFL/IART was determined. RESULTS: Fourteen patients (8.9%) were found to have AFL/IART. Pvm, QRS duration, and QRS vector magnitude significantly differentiated those with AFL/IART from those without on univariate analysis: 0.09 ± 0.04 vs 0.18 ± 0.07 mV, 161.3 ± 21.9 vs 137.7 ± 31.4 ms, and 1.2 (interquartile range, 1.0-1.2) vs 1.6 mV (1.0-2.3), respectively (P < 0.05 for each). The Pvm had the highest area under the ROC curve (0.88) and was the only significant predictor on multivariate analysis, with odds ratio of 0.02 (95% confidence interval: 0.01-0.53). P-R duration, MRI volumes, and right-heart hemodynamics did not significantly differentiate those with vs those without AFL/IART. CONCLUSIONS: In TOF patients undergoing pulmonary valve replacement, Pvm has significant value in predicting those with perioperative AFL/IART. These clinical features may help further evaluate TOF patients at risk for perioperative atrial arrhythmias. Prospective studies are warranted.

Vectorcardiography (VCG) is another useful method that provides us with useful spatial information about the electrical activity of the heart. The use of vectorcardiography in clinical practice is not common nowadays, mainly due to the well-established 12-lead ECG system. However, VCG leads can be derived from standard 12-lead ECG systems using mathematical transformations. These derived or directly measured VCG records have proven to be a useful tool for diagnosing various heart diseases such as myocardial infarction, ventricular hypertrophy, myocardial scars, long QT syndrome, etc., where standard ECG does not achieve reliable accuracy within automated detection. With the development of computer technology in recent years, vectorcardiography is beginning to come to the forefront again. In this review we highlight the analysis of VCG records within the extraction of functional parameters for the detection of heart disease. We focus on methods of processing VCG functionalities and their use in given pathologies. Improving or combining current or developing new advanced signal processing methods can contribute to better and earlier detection of heart disease. We also focus on the most commonly used methods to derive a VCG from 12-lead ECG. [ABSTRACT FROM AUTHOR]

Cardiac resynchronization therapy (CRT) is a good treatment for heart failure accompanied by ventricular conduction abnormalities. Current ECG criteria in international guidelines seem to be suboptimal to select heart failure patients for CRT. The criteria QRS duration and left bundle branch block (LBBB) QRS morphology insufficiently detect left ventricular activation delay, which is required for benefit from CRT. Additionally, there are various definitions for LBBB, in which each one has a different association with CRT benefit and is prone to subjective interpretation. Recent studies have shown that the objectively measured vectorcardiographic QRS area identifies left ventricular activation delay with higher accuracy than any of the current ECG criteria. Indeed, various studies have consistently shown that a high QRS area prior to CRT predicts both echocardiographic and clinical improvement after CRT. The beneficial relation of QRS area with CRT-outcome was largely independent from QRS morphology, QRS duration, and patient characteristics known to affect CRT-outcome including ischemic etiology and sex. On top of QRS area prior to CRT, the reduction in QRS area after CRT further improves benefit. QRS area is easily obtainable from a standard 12-lead ECG though it currently requires off-line analysis. Clinical applicability will be significantly improved when QRS area is automatically determined by ECG equipment. [ABSTRACT FROM AUTHOR]

Background: Women seem to derive more benefit from cardiac resynchronization therapy (CRT) than men, even after accounting for the higher burden of risk factors for nonresponse often observed in men. Objective: To assess for sex-specific differences in left ventricular (LV) electrical dyssynchrony as a contributing electrophysiological explanation for the greater degree of CRT benefit among women. Methods: We compared the extent of baseline LV electrical dyssynchrony, as measured by the QRS area (QRSA), among men and women with left bundle branch block (LBBB) undergoing CRT at Duke University (n = 492, 35% women) overall and in relation to baseline QRS characteristics using independent sample t tests and Pearson correlation coefficients. Cox regression analyses were used to relate sex, QRSA, and QRS characteristics to the risk of cardiac transplantation, LV assist device implant, or death. Results: Although the mean QRS duration (QRSd) did not differ by sex, QRSA was greater for women vs men (113.8 μVs vs 98.2 μVs, P < .001), owing to differences in the QRSd

Vectorcardiography (VCG) is another useful method that provides us with useful spatial information about the electrical activity of the heart. The use of vectorcardiography in clinical practice is not common nowadays, mainly due to the well-established 12-lead ECG system. However, VCG leads can be derived from standard 12-lead ECG systems using mathematical transformations. These derived or directly measured VCG records have proven to be a useful tool for diagnosing various heart diseases such as myocardial infarction, ventricular hypertrophy, myocardial scars, long QT syndrome, etc., where standard ECG does not achieve reliable accuracy within automated detection. With the development of computer technology in recent years, vectorcardiography is beginning to come to the forefront again. In this review we highlight the analysis of VCG records within the extraction of functional parameters for the detection of heart disease. We focus on methods of processing VCG functionalities and their use in given pathologies. Improving or combining current or developing new advanced signal processing methods can contribute to better and earlier detection of heart disease. We also focus on the most commonly used methods to derive a VCG from 12-lead ECG.

Resumen: Introducción: El ángulo QRS-T espacial es la diferencia entre el vector del QRS y la T. Se conoce el valor diagnóstico y pronóstico de este marcador en enfermedades cardíacas. Es prioritario determinar si estas mediciones son reproducibles con confiabilidad en nuestro medio. Métodos: En 30 adultos se obtuvieron electrocardiogramas, independientemente del diagnóstico. Las mediciones las realizaron dos estudiantes de Medicina de quinto año, un médico interno y un cardiólogo, posterior a un reentrenamiento sobre la medición del ángulo por el método de transformación visual.Con identificación cegada, ingresaron en un aplicativo web las mediciones del QRS y la T. El ángulo fue estimado a partir de la plantilla de Cortez y colaboradores. Sobre el 20% de las lecturas se estimó un acuerdo intraobservador y en el 100% de las lecturas un acuerdo interobservador. Los puntos de corte para estimar el acuerdo Kappa fueron 135° (anormal). Resultados: Se excluyó un ECG por calidad deficiente. El rango del ángulo estuvo entre 72.9° y 176.2°. El acuerdo interobservador entre los 4 evaluadores fue kappa = 0.786 (IC95% 0.728-0.823, p 135° (abnormal). Results: One ECG was excluded due to poor quality. The range of the angle was between 72.9° and 176.2°. The inter-observer agreement between the 4 evaluators gave a kappa = 0.786 (95% CI; 0.728-0.823, P

Background: One of the most common cardiovascular diseases (CVDs) in the world is myocardial infarction (MI). By analyzing electrocardiogram and vectorcardiography (VCG) signals, it is possible to identify and characterize heart diseases such as MI. One of the new methods of detection is the use of spatio-temporal parameters of VCG signals. This study aimed to correctly distinguish healthy signals from patients, achieve acceptable accuracy, and show the benefits of VCG and its application as a method to cover the shortcoming of electrocardiography. Methods: In this study, in addition to applying electrocardiogram signals in the time domain, spatio-temporal patterns of VCG signals were used to identify 80 patients with MI, and differentiate them from 80 healthy individuals. Findings: When combining the 12-lead electrocardiography (ECG) and the 3-lead VCG features applied to the Feedforward Neural Network classifier input, an accuracy of 91.2%, specificity of 92.6%, and specificity of 90% were obtained. The results were in higher values than when applied separately. Conclusion: The observations indicate that combined ECG and VCG methods can be effective in distinguishing MI cases from healthy cases. It is hoped that this method may be useful in the clinical evaluation and heart failure diagnosis.

Abstract Background The risk of sudden cardiac death (SCD) is known to be dynamic. However, the accuracy of a dynamic SCD prediction is unknown. We aimed to measure the dynamic predictive accuracy of ECG biomarkers of SCD and competing non-sudden cardiac death (non-SCD). Methods Atherosclerosis Risk In Community study participants with analyzable ECGs in sinus rhythm were included (n = 15,716; 55% female, 73% white, age 54.2 ± 5.8 y). ECGs of 5 follow-up visits were analyzed. Global electrical heterogeneity and traditional ECG metrics (heart rate, QRS, QTc) were measured. Adjudicated SCD was the primary outcome; non-SCD was the competing outcome. Time-dependent area under the receiver operating characteristic curve (ROC(t) AUC) analysis was performed to assess the prediction accuracy of a continuous biomarker in a period of 3,6,9 months, and 1,2,3,5,10, and 15 years using a survival analysis framework. Reclassification improvement as compared to clinical risk factors (age, sex, race, diabetes, hypertension, coronary heart disease, stroke) was measured. Results Over a median 24.4 y follow-up, there were 577 SCDs (incidence 1.76 (95%CI 1.63–1.91)/1000 person-years), and 829 non-SCDs [2.55 (95%CI 2.37–2.71)]. No ECG biomarkers predicted SCD within 3 months after ECG recording. Within 6 months, spatial ventricular gradient (SVG) elevation predicted SCD (AUC 0.706; 95%CI 0.526–0.886), but not a non-SCD (AUC 0.527; 95%CI 0.303–0.75). SVG elevation more accurately predicted SCD if the ECG was recorded 6 months before SCD (AUC 0.706; 95%CI 0.526–0.886) than 2 years before SCD (AUC 0.608; 95%CI 0.515–0.701). Within the first 3 months after ECG recording, only SVG azimuth improved reclassification of the risk beyond clinical risk factors: 18% of SCD events were reclassified from low or intermediate risk to a high-risk category. QRS-T angle was the strongest long-term predictor of SCD (AUC 0.710; 95%CI 0.668–0.753 for ECG recorded within 10 years before SCD). Conclusion Short-term and long-term predictive accuracy of ECG biomarkers of SCD differed, reflecting differences in transient vs. persistent SCD substrates. The dynamic predictive accuracy of ECG biomarkers should be considered for competing SCD risk scores. The distinction between markers predicting short-term and long-term events may represent the difference between markers heralding SCD (triggers or transient substrates) versus markers identifying persistent substrate.

Objective: Accurate screening for coronary artery disease both enable necessary intervention and may avoid unnecessary invasive procedures. For this purpose, stress ECG, MPS and CT angiography can be used. All methods may have its own limitations and side effects. Another new test developed for screening purposes is the artificial intelligence analysed vectorcardiography method. The sensitivity was found to be >90%, promising a high accuracy rate for ischaemia testing (1). Method: A 52-year-old male patient with no known disease presented with anginal symptoms. CT angiography was performed due to a family history of CAD; and reported as LMCA normal, LAD proximal 50% lesion, Cx normal, RCA normal calcium score 116. At this stage, the patient also examined with artificial intelligence analysed vectorcardiography, "Cardisiography". Cardisiography revealed no sign of perfusion impairment. Indeed, when coronary angiography performed, found LAD with plaque, Cx with plaque, and RCA normal (figure 1). Results: In our case, we think that coronary CT angiography was false positive due to high calcification. Cardisiography gave negative results consistent with the findings of coronary angiography. Despite its high diagnostic value, coronary CT angiography has limitations such as low temporal resolution, motion-related artefacts, the need for heart rate reduction and high false positive rates due to severe calcification (2). And also has some risks such as radiation exposure and contrast nephropathy. Cardisiography is an artificial intelligence analysed vectorcardiography method that focuses on the spatial and temporal heterogeneity of cardiac excitation, developed by training with angiographic findings (1). It is a diagnostic method that estimates the risk of perfusion impairment by analysing ischaemic findings reflected on vectorcardiography. It is not an anatomical imaging method. In this case, it was able to overcome the chaleng in the assessment due to high calcium load and make a correct prediction. Conclusion: Cardisiography may be a promising method in CAD screening with high sensitivity. It may prevent unnecessary angiography in selected patient groups. [ABSTRACT FROM AUTHOR]

OBJECTIVE: Cardiovascular diseases are still the leading cause of death worldwide. High-sensitivity screening for CAD may lead to a significant reduction in cardiovascular mortality and morbidity. Cardisiography is a new diagnostic method with artificial intelligence analysis aiming to detect CAD early. The sensitivity was found to be >90%, promising a high accuracy rate for ischaemia testing (1). METHODS A 60-year-old woman with known DM and HT was admitted to outpatient clinic with stable angina pectoris. ECG was SR, EF was 60% on echocardiography. No findings of ischaemia/infarction on MPS. At this stage, the patient was also examined with a new diagnostic method "Cardisiography". Cardisiography revealed a high risk for perfusion abnormality (figure 1). Although MPS was negative, coronary angiography was performed because the patient had typical symptoms. Angiography revealed proximal70% mid70% lesions in LAD, proximal30% distal subtotal lesions in Cx, osteal80% lesions in high-Om-1, proximal60% lesions in RCA. RESULTS Cardisiography is a supervised machine learning algorithm combined with 5-lead vectorcardiography. Vectorcardiography allows three-dimensional evaluation of the excitation signals of the heart. Cardiography measurement is performed at rest, 290 parameters are analysed with artificial intelligence and a detailed report is obtained (1). P-index was detected 0,701 in the Cardisiography report of the case. This value indicates that the patient's parameters are 70% correlated with the positive cases for CAD in the train data of the algorithm. The test showed positivity for myocardial perfusion impairment. Although MPS is one of the most sensitive non-invasive tests for the detection of CAD, cases of normal MPS have been reported in patients with angiographically significant coronary artery disease (2). Side branch stenosis, Cx artery stenosis, inadequate exercise, caffeine intake and balanced ischaemia may be the causes of false negativity (3,4). We think that the MPS negativity in our case was due to balanced ischaemia caused by multivessel disease. Since the Cardisiography algorithm is trained to detect patients with >50% stenosis on coronary angiography, it is likely to overcome the challenge of detecting coronary ischaemia even in the presence of balanced ischaemia. CONCLUSİONS: Conventional non-invasive diagnostic methods used in the screening of CAD have important limitations such as low sensitivity, stress requirement, and expertise in interpretation. Cardisiography may be a promising diagnostic method due to its advantages compared to conventional tests. [ABSTRACT FROM AUTHOR]

Objective: Ultra-high-field B0 ≥ 7 tesla (7T) cardiovascular magnetic resonance (CMR) offers increased resolution. However, electrocardiogram (ECG) gating is impacted by the magnetohydrodynamic effect distorting the ECG trace. We explored the technical feasibility of a 7T magnetic resonance scanner using an ECG trigger learning algorithm to quantitatively assess cardiac volumes and vascular flow. Methods: 7T scans were performed on 10 healthy volunteers on a whole-body research MRI MR scanner (Siemens Healthineers, Erlangen, Germany) with 8 channel Tx/32 channels Rx cardiac coils (MRI Tools GmbH, Berlin, Germany). Vectorcardiogram ECG was performed using a learning phase outside of the magnetic field, with a trigger algorithm overcoming severe ECG signal distortions. Vectorcardiograms were quantitatively analyzed for false negative and false positive events. Cine CMR was performed after 3rd-order B0 shimming using a high-resolution breath-held ECG-retro-gated segmented spoiled gradient echo, and 2D phase contrast flow imaging. Artefacts were assessed using a semi-quantitative scale. Results: 7T CMR scans were acquired in all patients (100%) using the vectorcardiogram learning method. 3,142 R-waves were quantitatively analyzed, yielding sensitivity of 97.6% and specificity of 98.7%. Mean image quality score was 0.9, sufficient to quantitate both cardiac volumes, ejection fraction, and aortic and pulmonary blood flow. Mean left ventricular ejection fraction was 56.4%, right ventricular ejection fraction was 51.4%. Conclusion: Reliable cardiac ECG triggering is feasible in healthy volunteers at 7T utilizing a state-of-the-art three-lead trigger device despite signal distortion from the magnetohydrodynamic effect. This provides sufficient image quality for quantitative analysis. Other ultra-high-field imaging applications such as human brain functional MRI with physiologic noise correction may benefit from this method of ECG triggering. [ABSTRACT FROM AUTHOR]

Abstract Background Serial electrocardiography aims to contribute to electrocardiogram (ECG) diagnosis by comparing the ECG under consideration with a previously made ECG in the same individual. Here, we present a novel algorithm to construct dedicated deep-learning neural networks (NNs) that are specialized in detecting newly emerging or aggravating existing cardiac pathology in serial ECGs. Methods We developed a novel deep-learning method for serial ECG analysis and tested its performance in detection of heart failure in post-infarction patients, and in the detection of ischemia in patients who underwent elective percutaneous coronary intervention. Core of the method is the repeated structuring and learning procedure that, when fed with 13 serial ECG difference features (intra-individual differences in: QRS duration; QT interval; QRS maximum; T-wave maximum; QRS integral; T-wave integral; QRS complexity; T-wave complexity; ventricular gradient; QRS-T spatial angle; heart rate; J-point amplitude; and T-wave symmetry), dynamically creates a NN of at most three hidden layers. An optimization process reduces the possibility of obtaining an inefficient NN due to adverse initialization. Results Application of our method to the two clinical ECG databases yielded 3-layer NN architectures, both showing high testing performances (areas under the receiver operating curves were 84% and 83%, respectively). Conclusions Our method was successful in two different clinical serial ECG applications. Further studies will investigate if other problem-specific NNs can successfully be constructed, and even if it will be possible to construct a universal NN to detect any pathologic ECG change.

Aims: Ventricular conduction disorders can induce arrhythmias and impair cardiac function. Bundle branch blocks (BBBs) are diagnosed by 12-lead electrocardiogram (ECG), but discrimination between BBBs and normal tracings can be challenging. CineECG computes the temporo-spatial trajectory of activation waveforms in a 3D heart model from 12-lead ECGs. Recently, in Brugada patients, CineECG has localized the terminal components of ventricular depolarization to right ventricle outflow tract (RVOT), coincident with arrhythmogenic substrate localization detected by epicardial electro-anatomical maps. This abnormality was not found in normal or right BBB (RBBB) patients. This study aimed at exploring whether CineECG can improve the discrimination between left BBB (LBBB)/RBBB, and incomplete RBBB (iRBBB).Methods and Results: We utilized 500 12-lead ECGs from the online Physionet-XL-PTB-Diagnostic ECG Database with a certified ECG diagnosis. The mean temporo-spatial isochrone trajectory was calculated and projected into the anatomical 3D heart model. We established five CineECG classes: 'Normal', 'iRBBB', 'RBBB', 'LBBB', and 'Undetermined', to which each tracing was allocated. We determined the accuracy of CineECG classification with the gold standard diagnosis. A total of 391 ECGs were analysed (9 ECGs were excluded for noise) and 240/266 were correctly classified as 'normal', 14/17 as 'iRBBB', 55/55 as 'RBBB', 51/51 as 'LBBB', and 31 as 'undetermined'. The terminal mean temporal spatial isochrone contained most information about the BBB localization.Conclusion: CineECG provided the anatomical localization of different BBBs and accurately differentiated between normal, LBBB and RBBB, and iRBBB. CineECG may aid clinical diagnostic work-up, potentially contributing to the difficult discrimination between normal, iRBBB, and Brugada patients. [ABSTRACT FROM AUTHOR]

Background: Women are less likely to receive cardiac resynchronization therapy, yet, they are more responsive to the therapy and respond at shorter QRS duration. The present study hypothesized that a relatively larger left ventricular (LV) electrical dyssynchrony in smaller hearts contributes to the better cardiac resynchronization therapy response in women. For this, the vectorcardiography-derived QRS area is used, since it allows for a more detailed quantification of electrical dyssynchrony compared with conventional electrocardiographic markers.Methods: Data from a multicenter registry of 725 cardiac resynchronization therapy patients (median follow-up, 4.2 years [interquartile range, 2.7-6.1]) were analyzed. Baseline electrical dyssynchrony was evaluated using the QRS area and the corrected QRS area for heart size using the LV end-diastolic volume (QRSarea/LVEDV). Impact of the QRSarea/LVEDV ratio on the association between sex and LV reverse remodeling (LV end-systolic volume change) and sex and the composite outcome of all-cause mortality, LV assist device implantation, or heart transplantation was assessed.Results: At baseline, women (n=228) displayed larger electrical dyssynchrony than men (QRS area, 132±55 versus 123±58 μVs; P=0.043), which was even more pronounced for the QRSarea/LVEDV ratio (0.76±0.46 versus 0.57±0.34 μVs/mL; P<0.001). After multivariable analyses, female sex was associated with LV end-systolic volume change (β=0.12; P=0.003) and a lower occurrence of the composite outcome (hazard ratio, 0.59 [0.42-0.85]; P=0.004). A part of the female advantage regarding reverse remodeling was attributed to the larger QRSarea/LVEDV ratio in women (25-fold change in β from 0.12 to 0.09). The larger QRSarea/LVEDV ratio did not contribute to the better survival observed in women. In both volumetric responders and nonresponders, female sex remained strongly associated with a lower risk of the composite outcome (adjusted hazard ratio, 0.59 [0.36-0.97]; P=0.036; and 0.55 [0.33-0.90]; P=0.018, respectively).Conclusions: Greater electrical dyssynchrony in smaller hearts contributes, in part, to more reverse remodeling observed in women after cardiac resynchronization therapy, but this does not explain their better long-term outcomes. [ABSTRACT FROM AUTHOR]

Aims: Typical electrocardiogram (ECG) features of apical hypertrophic cardiomyopathy (ApHCM) include tall R waves and deep or giant T-wave inversion in the precordial leads, but these features are not always present. The ECG is used as the gatekeeper to cardiac imaging for diagnosis. We tested whether explainable advanced ECG (A-ECG) could accurately diagnose ApHCM., Methods and Results: Advanced ECG analysis was performed on standard resting 12-lead ECGs in patients with ApHCM [n = 75 overt, n = 32 relative (<15 mm hypertrophy); a subgroup of which underwent cardiovascular magnetic resonance (n = 92)], and comparator subjects (n = 2449), including healthy volunteers (n = 1672), patients with coronary artery disease (n = 372), left ventricular electrical remodelling (n = 108), ischaemic (n = 114) or non-ischaemic cardiomyopathy (n = 57), and asymmetrical septal hypertrophy HCM (n = 126). Multivariable logistic regression identified four A-ECG measures that together discriminated ApHCM from other diseases with high accuracy [area under the receiver operating characteristic (AUC) curve (bootstrapped 95% confidence interval) 0.982 (0.965-0.993)]. Linear discriminant analysis also diagnosed ApHCM with high accuracy [AUC 0.989 (0.986-0.991)]., Conclusion: Explainable A-ECG has excellent diagnostic accuracy for ApHCM, even when the hypertrophy is relative, with A-ECG analysis providing incremental diagnostic value over imaging alone. The electrical (ECG) and anatomical (wall thickness) disease features do not completely align, suggesting that future diagnostic and management strategies may incorporate both features., (© The Author(s) 2024. Published by Oxford University Press on behalf of the European Society of Cardiology.)

Background: Wide QRS‐T angles and inflammatory activity are markers of future cardiovascular events including sudden cardiac death (SCD). The association between wide QRS‐T angles and inflammatory activation is however not fully understood. Methods: 1,094 study participants of both sexes, 50–64 years old, were included from a randomly selected population‐based cohort as a part of the Swedish CArdioPulmonary bioImage Study (SCAPIS) pilot study. Serum samples were analyzed for markers of inflammation, cardiac wall stress/injury, and the metabolic syndrome. Wide QRS‐T angles were defined using Frank vectorcardiography. Variables were analyzed through unsupervised principal component analysis (PCA) as well as Orthogonal Projections to Latent Structures (OPLS) modeling. In addition, a subset of study participants was analyzed in a post hoc matched group design. Results: Wide QRS‐T angles correlated positively with markers of inflammation, cardiac wall stress/injury, the metabolic syndrome, and male sex in both PCA and OPLS models. In the matched post hoc analysis, participants with wide QRS‐T angles had significantly higher counts of white blood cells (WBC) and neutrophils in comparison with matched controls. WBC as well as the number of neutrophils, monocytes, basophils, eosinophils and levels of C‐reactive protein, IL‐1, IL‐4, IL‐6, TNF‐α, and NT‐pro‐BNP were also significantly higher in comparison with healthy controls. Conclusions: Markers of inflammatory activation and cardiac injury/wall stress were significantly higher in the presence of wide QRS‐T angles. These results corroborate an association between abnormal electrophysiological function and inflammatory activation and may have implications for the prediction of SCD. [ABSTRACT FROM AUTHOR]

Background Predicting clinical outcomes after cardiac resynchronization therapy (CRT) and its optimization remain a challenge. We sought to determine whether pre‐ and postimplantation QRS area (QRSarea) predict clinical outcomes after CRT. Methods and Results In this retrospective study, QRSarea, derived from pre‐ and postimplantation vectorcardiography, were assessed in relation to the primary end point of cardiac mortality after CRT with or without defibrillation. Other end points included total mortality, total mortality or heart failure (HF) hospitalization, total mortality or major adverse cardiac events, and the arrhythmic end point of sudden cardiac death or ventricular arrhythmias with or without a shock. In patients (n=380, age 72.0±12.4 years, 68.7% male) undergoing CRT over 7.7 years (median follow‐up: 3.8 years [interquartile range 2.3–5.3]), preimplantation QRSarea ≥102 μVs predicted cardiac mortality (HR: 0.36; P

Directional data emerges in a wide array of applications, ranging from atmospheric sciences to medical imaging. Modeling such data, however, poses unique challenges by virtue of their being constrained to non-Euclidean spaces like manifolds. Here, we present a unified Bayesian framework for inference on the Stiefel manifold using the Matrix Langevin distribution. Specifically, we propose a novel family of conjugate priors and establish a number of theoretical properties relevant to statistical inference. Conjugacy enables translation of these properties to their corresponding posteriors, which we exploit to develop the posterior inference scheme. For the implementation of the posterior computation, including the posterior sampling, we adopt a novel computational procedure for evaluating the hypergeometric function of matrix arguments that appears as normalization constants in the relevant densities. [ABSTRACT FROM AUTHOR]

Background: The duration of ventricular repolarization (VR) and its spatial and temporal heterogeneity are central elements in arrhythmogenesis. We studied the adaptation of VR duration and dispersion and their relationship in healthy human subjects during atrial pacing.Methods: Patients 20-50 years of age who were scheduled for ablation of supraventricular tachycardia without preexcitation but otherwise healthy were eligible. Vectorcardiography recordings with Frank leads were used for data collection. Incremental atrial pacing from a coronary sinus electrode was performed by decrements of 10ms/cycle from just above sinus rate, and then kept at a fixed heart rate (HR) just below the Wenckebach rate for ≥5min and then stopped. VR duration was measured as QT and VR dispersion as T area, T amplitude and ventricular gradient. The primary measure (T90 End) was the time to reach 90% change from baseline to the steady state value during and after pacing.Results: A complete study protocol was accomplished in 9 individuals (6 women). VR duration displayed a monophasic adaptation during HR acceleration lasting on average 20s. The median (Q1-Q3) T90 End for QT was 85s (51-104), a delay by a factor >4. All dispersion measures displayed a tri-phasic response pattern during HR acceleration and T90 End was 3-5 times shorter than for VR duration.Conclusions: Even during close to "physiological" conditions, complex and differing response patterns in VR duration and dispersion measures followed changes in HR. Extended knowledge about these responses in disease conditions might assist in risk evaluation and finding therapeutic alternatives. [ABSTRACT FROM AUTHOR]

Tight blood glucose control reduces the risk of microvascular and macrovascular complications in patients with type 1 diabetes. However, this is very difficult due to the large intra-individual variability and other factors that affect glycaemic control. The main limiting factor to achieve strict control of glucose levels in patients on intensive insulin therapy is the risk of severe hypoglycaemia. Therefore, hypoglycaemia is the main safety problem in the treatment of type 1 diabetes, negatively affecting the quality of life of patients suffering from this disease. Decision support tools based on machine learning methods have become a viable way to enhance patient safety by anticipating adverse glycaemic events. This study proposes the application of four machine learning algorithms to tackle the problem of safety in diabetes management: (1) grammatical evolution for the mid-term continuous prediction of blood glucose levels, (2) support vector machines to predict hypoglycaemic events during postprandial periods, (3) artificial neural networks to predict hypoglycaemic episodes overnight, and (4) data mining to profile diabetes management scenarios. The proposal consists of the combination of prediction and classification capabilities of the implemented approaches. The resulting system significantly reduces the number of episodes of hypoglycaemia, improving safety and providing patients with greater confidence in decision-making. [ABSTRACT FROM AUTHOR]

Aim. To investigate on the efficacy of thiotriazoline (TTZ) in complex management of acute myocardial infarction (AMI) patients with hepatic steatosis.Material and methods. Totally, 32 left ventricle AMI patients included, comorbid with non-alcoholic fatty liver disease. Assessment of electrical activity of myocardium was done on improved polygraph with electroand vectorcardiogram registration before and post three week basic therapy in 16 patients (13 males and 3 females, mean age — 60,5±3,74 y.o.) and management with added TTZ (8 males and 8 females, mean age – 62,94±2,71 y.o.).Results. On basic therapy, there was ongoing decline in electromotive force of the heart with slower impulse conduction and disordered repolarization in atrial myocardium, with decreasing area of damage in the left ventricle and atrial overload. Concomitant TTZ usage with basic therapy led to minimal positive shifts in electromotive force of the heart. Also, there was increase of ischemia zone due to decrease of myocardial damage area, more prominent normalization of left atrium compartment and stabilization of repolarization processes in atria.Conclusion. Under the combination treatment by TTZ there was significant change of the grade of electrical activity of the heart electrical activity disorder comparing to basic therapy.

Abstract Background Arrhythmogenic right ventricular dysplasia/cardiomyopathy (ARVD/C) carries a risk of sudden death. We aimed to assess whether vectorcardiographic (VCG) parameters directed toward the right heart and a measured angle of the S-wave would help differentiate ARVD/C with otherwise normal electrocardiograms from controls. Methods Task Force 2010 definite ARVD/C criteria were met for all patients. Those who did not fulfill Task Force depolarization or repolarization criteria (−ECG) were compared with age and gender-matched control subjects. Electrocardiogram measures of a 3-dimentional spatial QRS-T angle, a right-precordial-directed orthogonal QRS-T (RPD) angle, a root mean square of the right sided depolarizing forces (RtRMS-QRS), QRS duration (QRSd) and the corrected QT interval (QTc), and a measured angle including the upslope and downslope of the S-wave (S-wave angle) were assessed. Results Definite ARVD/C was present in 155 patients by 2010 Task Force criteria (41.7 ± 17.6 years, 65.2% male). -ECG ARVD/C patients (66 patients) were compared to 66 control patients (41.7 ± 17.6 years, 65.2% male). All parameters tested except the QRSd and QTc significantly differentiated -ECG ARVD/C from control patients (p

We propose a novel framework for performing inhomogeneous template adaptation on temporal three-dimensional data. Templates of interest are first globally translated, scaled and rotated using statistical methods in the form of Procrustes analysis. In this globally adapted form, the data is parameterized to a minimum bounding rectangular prism using a parameterization method known as free-form deformation. The parameterized model is then reduced to a linear least squares problem, which subsequently allows for a robust local template adaptation scheme utilizing a non-parametric formulation. To account for complex noise behaviour encountered in quasi-periodic signals, we ensue to complete the framework with an adaptive noise model. We demonstrate the power of the proposed framework using three applications: firstly, we quantify the ability of the proposed framework to track geometric features of heart disease on a synthetic dataset containing three-dimensional electrocardiogram signals. Next we track these features on a publicly available real dataset (PTB database) to demonstrate its potential in heart disease diagnostics. Lastly, we apply the proposed framework to map three-dimensional velocity profiles using the publicly available Complex Upper-Limb Movements database. [ABSTRACT FROM AUTHOR]

Background: The risk of sudden cardiac death (SCD) is known to be dynamic. However, the accuracy of a dynamic SCD prediction is unknown. We aimed to measure the dynamic predictive accuracy of ECG biomarkers of SCD and competing non-sudden cardiac death (non-SCD).Methods: Atherosclerosis Risk In Community study participants with analyzable ECGs in sinus rhythm were included (n = 15,716; 55% female, 73% white, age 54.2 ± 5.8 y). ECGs of 5 follow-up visits were analyzed. Global electrical heterogeneity and traditional ECG metrics (heart rate, QRS, QTc) were measured. Adjudicated SCD was the primary outcome; non-SCD was the competing outcome. Time-dependent area under the receiver operating characteristic curve (ROC(t) AUC) analysis was performed to assess the prediction accuracy of a continuous biomarker in a period of 3,6,9 months, and 1,2,3,5,10, and 15 years using a survival analysis framework. Reclassification improvement as compared to clinical risk factors (age, sex, race, diabetes, hypertension, coronary heart disease, stroke) was measured.Results: Over a median 24.4 y follow-up, there were 577 SCDs (incidence 1.76 (95%CI 1.63-1.91)/1000 person-years), and 829 non-SCDs [2.55 (95%CI 2.37-2.71)]. No ECG biomarkers predicted SCD within 3 months after ECG recording. Within 6 months, spatial ventricular gradient (SVG) elevation predicted SCD (AUC 0.706; 95%CI 0.526-0.886), but not a non-SCD (AUC 0.527; 95%CI 0.303-0.75). SVG elevation more accurately predicted SCD if the ECG was recorded 6 months before SCD (AUC 0.706; 95%CI 0.526-0.886) than 2 years before SCD (AUC 0.608; 95%CI 0.515-0.701). Within the first 3 months after ECG recording, only SVG azimuth improved reclassification of the risk beyond clinical risk factors: 18% of SCD events were reclassified from low or intermediate risk to a high-risk category. QRS-T angle was the strongest long-term predictor of SCD (AUC 0.710; 95%CI 0.668-0.753 for ECG recorded within 10 years before SCD).Conclusion: Short-term and long-term predictive accuracy of ECG biomarkers of SCD differed, reflecting differences in transient vs. persistent SCD substrates. The dynamic predictive accuracy of ECG biomarkers should be considered for competing SCD risk scores. The distinction between markers predicting short-term and long-term events may represent the difference between markers heralding SCD (triggers or transient substrates) versus markers identifying persistent substrate. [ABSTRACT FROM AUTHOR]

Rheumatoid arthritis (RA) is an autoimmune rheumatic disease characterized by damage not only to the joints, but also to the viscera, including the heart. The cardiovascular risk in patients with RA is 1.5 times higher than that in the general population. In this connection, the treatment should be aimed not only at joint diseases, but should also prevent fatal heart failure. Rituximab (RTM) is a biological drug that has proven to be highly effective in treating different diseases, including RA.Objective: to investigate the effect of RTM on cardiac electrophysiological parameters in patients with RA.Subjects and methods. Examinations were made in 83 patients with seropositive RA, of whom 21 patients received RTM at a dose of 1000 mg according to the scheme for 6–12 months and oral methotrexate at a dose of up to 25 mg weekly (Group 1) and 62 took oral methotrexate at a dose of up to 25 mg weekly for at least 12 months (Group 2). In addition to standard diagnostic methods, the investigators performed electrocardiography to calculate Q–T dispersion and vectorcardiography to determine the loop areas of P, QRS and T, as well as MV vector, MV azimuth, MV elevation.Results and discussion. In Group 1, Q–T dispersion was lower than that in Group 2 (p