Vectorcardiographic diagnostic & prognostic information derived from the 12-lead electrocardiogram: Historical review and clinical perspective

Abnormal QRS-T angles in 5796 women and men aged 50-64: an electrocardiographic analysis providing mechanistic insights

INTRODUCTION

Abnormal QRS-T angles are prognostic markers for cardiovascular deaths including sudden cardiac death. They occur in ∼5-6% of population-based cohorts but in ∼20% of patients with diabetes. The mechanistic background, electrical activation and/or recovery disturbances, is not known and the topic of this study.

METHODS

Applying Frank vectorcardiography (VCG) and simultaneously recorded scalar 12‑lead ECG, electrical activation and recovery of abnormal QRS-T angles were studied in 311 participants (5.4%) from a population-based cohort of 5796 women and men in the main Swedish CArdio-Pulmonary bio-Imaging Study (SCAPIS) in Gothenburg. Cut-off values for the peak and mean QRS-T angles were > 124° and > 119°, based on the >95th percentile among all 1080 participants in the pilot SCAPIS and reference values for normal directions (Q1-Q3) from 319 apparently healthy (30%) of them.

RESULTS

Of 311 cases 17% had known cardiac disease. Deviations of QRS and QRSarea-vectors from reference limits (90%) were significantly more common than deviations of T- and Tarea-vectors (65%). Standard ECG signs suggested pathophysiology in 20%; left bundle branch block (LBBB) and voltage criteria for left ventricular hypertrophy being most frequent (9-10%) each. Sub-group analysis of the 30 with LBBB showed very large variability in vector directions.

CONCLUSIONS

Our observations provide mechanistic insights about abnormal QRS-T angles of potential value for future prognostic and interventional studies. The results also have potential implications for LBB area pacing and the approach to left ventricular hypertrophy.

Vectorcardiography Predicts Heart Failure in Patients Following ST Elevation Myocardial Infarction

BACKGROUND

Modeling outcomes, such as onset of heart failure (HF) or mortality, in patients following ST elevation myocardial infarction (STEMI) is challenging but clinically very useful. The acute insult following a myocardial infarction and chronic degeneration seen in HF involve a similar process where a loss of cardiomyocytes and abnormal remodeling lead to pump failure. This process may alter the strength and direction of the heart's net depolarization signal. We hypothesize that changes over time in unique parameters extracted using vectorcardiography (VCG) have the potential to predict outcomes in patients post-STEMI and could eventually be used as a noninvasive and cost-effective surveillance tool for characterizing the severity and progression of HF to guide evidence-based therapies.

METHODS

We identified 162 patients discharged from Michigan Medicine between 2016 and 2021 with a diagnosis of acute STEMI. For each patient, a single 12-lead ECG > 1 week pre-STEMI and > 1 week post-STEMI were collected. A set of unique VCG parameters were derived by analyzing features of the QRS complex. We used LASSO regression analysis incorporating clinical variables and VCG parameters to create a predictive model for HF, mortality, or the composite at 90, 180, and 365 days post-STEMI.

RESULTS

The VCG model is most predictive for HF onset at 90 days with a robust AUC. Variables from the HF model mitigating or driving risk, at a p < 0.05, were primarily parameters that assess the area swept by the depolarization vector including the 3D integral and convex hull in select spatial octants and quadrants.

An interpretable ensemble trees method with joint analysis of static and dynamic features for myocardial infarction detection

Objective.In recent years, artificial intelligence-based electrocardiogram (ECG) methods have been massively applied to myocardial infarction (MI). However, the joint analysis of static and dynamic features to achieve accurate and interpretable MI detection has not been comprehensively addressed.Approach.This paper proposes a simplified ensemble tree method with a joint analysis of static and dynamic features to solve this issue for MI detection. Initially, the dynamic features are extracted by modeling the intrinsic dynamics of ECG via dynamic learning in addition to extracting classical static features. Secondly, a two-stage feature selection strategy is designed to identify a few significant features, which substitute the original variables that are employed in constructing the ensemble tree. This approach enhances the discriminative ability by selecting significant static and dynamic features. Subsequently, this paper presents an interpretable classification method named StackTree by introducing a stacked ensemble scheme to modify the ensemble tree simplification algorithm. The representative rules of the raw ensemble trees are selected as the intermediate training data that is used to retrain a decision tree with performance close to that of the source ensemble model. Using this scheme, the significant precision and interpretability of MI detection are thus comprehensively addressed.Main results.The effectiveness of our method in detecting MI is evaluated using the Physikalisch-Technische Bundesanstalt (PTB) and clinical database. The findings suggest that our algorithm outperforms the traditional methods based on a single type of feature. Additionally, it is comparable to the conventional random forest, achieving 97.1% accuracy under the inter-patient framework on the PTB database. Furthermore, feature subsets trained on PTB are validated using the clinical database, resulting in an accuracy of 84.5%. The chosen important features demonstrate that both static and dynamic information have crucial roles in MI detection. Crucially, the proposed method provides clear internal workings in an easy-to-understand visual manner.

A novel method combining gated SPECT and vectorcardiography to guide left ventricular lead placement to improve response to cardiac resynchronization therapy: A proof of concept study

BACKGROUND

The segment of the latest mechanical contraction (LMC) does not always overlap with the site of the latest electrical activation (LEA). By integrating both mechanical and electrical dyssynchrony, this proof-of-concept study aimed to propose a new method for recommending left ventricular (LV) lead placements, with the goal of enhancing response to cardiac resynchronization therapy (CRT).

METHODS

The LMC segment was determined by single-photon emission computed tomography myocardial perfusion imaging (SPECT MPI) phase analysis. The LEA site was detected by vectorcardiogram. The recommended segments for LV lead placement were as follows: (1) the LMC viable segments that overlapped with the LEA site; (2) the LMC viable segments adjacent to the LEA site; (3) If no segment met either of the above, the LV lateral wall was recommended. The response was defined as ≥15% reduction in left ventricular end-systolic volume (LVESV) 6-months after CRT. Patients with LV lead located in the recommended site were assigned to the recommended group, and those located in the non-recommended site were assigned to the non-recommended group.

RESULTS

The cohort comprised of 76 patients, including 54 (71.1%) in the recommended group and 22 (28.9%) in the non-recommended group. Among the recommended group, 74.1% of the patients responded to CRT, while 36.4% in the non-recommended group were responders (P = .002). Compared to pacing at the non-recommended segments, pacing at the recommended segments showed an independent association with an increased response by univariate and multivariable analysis (odds ratio 5.00, 95% confidence interval 1.73-14.44, P = .003; odds ratio 7.33, 95% confidence interval 1.53-35.14, P = .013). Kaplan-Meier curves showed that pacing at the recommended LV lead position demonstrated a better long-term prognosis.

CONCLUSION

Our findings indicate that pacing at the recommended segments, by integrating of mechanical and electrical dyssynchrony, is significantly associated with an improved CRT response and better long-term prognosis.

Intelligent assessment of atrial fibrillation gradation based on sinus rhythm electrocardiogram and baseline information

BACKGROUND

Atrial fibrillation (AF) is a progressive arrhythmia that significantly affects a patient's quality of life. The 4S-AF scheme is clinically recommended for AF management; however, the evaluation process is complex and time-consuming. This renders its promotion in primary medical institutions challenging. This retrospective study aimed to simplify the evaluation process and present an objective assessment model for AF gradation.

METHODS

In total, 189 12-lead electrocardiogram (ECG) recordings from 64 patients were included in this study. The data were annotated into two groups (mild and severe) according to the 4S-AF scheme. Using a preprocessed ECG during the sinus rhythm (SR), we obtained a synthesized vectorcardiogram (VCG). Subsequently, various features were calculated from both signals, and age, sex, and medical history were included as baseline characteristics. Different machine learning models, including support vector machines, random forests (RF), and logistic regression, were finally tested with a combination of feature selection techniques.

RESULTS

The proposed method demonstrated excellent performance in the classification of AF gradation. With an optimized feature set of VCG and baseline features, the RF model achieved accuracy, sensitivity, and specificity of 83.02 %, 80.56 %, and 88.24 %, respectively, under the inter-patient paradigm.

CONCLUSION

Our results demonstrate the value of physiological signals in AF gradation evaluation, and VCG signals were effective in identifying mild and severe AF. Considering its low computational complexity and high assessment performance, the proposed model is expected to serve as a useful prognostic tool for clinical AF management.

Present results and methods of vectorcardiographic diagnostics of ischemic heart disease

This article presents an overview of existing approaches to perform vectorcardiographic (VCG) diagnostics of ischemic heart disease (IHD). Individual methodologies are divided into categories to create a comprehensive and clear overview of electrical cardiac activity measurement, signal pre-processing, features extraction and classification procedures. An emphasis is placed on methods describing the electrical heart space (EHS) by several features extraction techniques based on spatiotemporal characteristics or signal modelling and signal transformations. Performance of individual methodologies are compared depending on classification of extent of ischemia, acute forms - myocardial infarction (MI) and myocardial scars localization. Based on a comparison of imaging methods, the advantages of VCG over the standard 12-leads ECG such as providing a 3D orthogonal leads imaging, better performance, and appropriate computer processing are highlighted. The issues of electrical cardiac activity measurements on body surface, the lack of VKG databases supported by a more accurate imaging method, possibility of comparison with the physiology of individual cases are outlined as potential reserves for future research.

BRAVEHEART: Open-source software for automated electrocardiographic and vectorcardiographic analysis

BACKGROUND AND OBJECTIVES

Electrocardiographic (ECG) and vectorcardiographic (VCG) analyses are used to diagnose current cardiovascular disease and for risk stratification for future adverse cardiovascular events. With increasing use of digital ECGs, research into novel ECG/VCG parameters has increased, but widespread computer-based ECG/VCG analysis is limited because there are no currently available, open-source, and easily customizable software packages designed for automated and reproducible analysis.

METHODS AND RESULTS

We present BRAVEHEART, an open-source, modular, customizable, and easy to use software package implemented in the MATLAB programming language, for scientific analysis of standard 12-lead ECGs acquired in a digital format. BRAVEHEART accepts a wide variety of digital ECG formats and provides complete and automatic ECG/VCG processing with signal denoising to remove high- and low-frequency artifact, non-dominant beat identification and removal, accurate fiducial point annotation, VCG construction, median beat construction, customizable measurements on median beats, and output of measurements and results in numeric and graphical formats.

CONCLUSIONS

The BRAVEHEART software package provides easily customizable scientific analysis of ECGs and VCGs. We hope that making BRAVEHART available will allow other researchers to further the field of ECG/VCG analysis without having to spend significant time and resources developing their own ECG/VCG analysis software and will improve the reproducibility of future studies. Source code, compiled executables, and a detailed user guide can be found at http://github.com/BIVectors/BRAVEHEART. The source code is distributed under the GNU General Public License version 3.

An expert review of the inverse problem in electrocardiographic imaging for the non-invasive identification of atrial fibrillation drivers

BACKGROUND AND OBJECTIVE

Electrocardiographic imaging (ECGI) has emerged as a non-invasive approach to identify atrial fibrillation (AF) driver sources. This paper aims to collect and review the current research literature on the ECGI inverse problem, summarize the research progress, and propose potential research directions for the future.

METHODS AND RESULTS

The effectiveness and feasibility of using ECGI to map AF driver sources may be influenced by several factors, such as inaccuracies in the atrial model due to heart movement or deformation, noise interference in high-density body surface potential (BSP), inconvenient and time-consuming BSP acquisition, errors in solving the inverse problem, and incomplete interpretation of the AF driving source information derived from the reconstructed epicardial potential. We review the current research progress on these factors and discuss possible improvement directions. Additionally, we highlight the limitations of ECGI itself, including the lack of a gold standard to validate the accuracy of ECGI technology in locating AF drivers and the challenges associated with guiding AF ablation based on post-processed epicardial potentials due to the intrinsic difference between epicardial and endocardial potentials.

CONCLUSIONS

Before performing ablation, ECGI can provide operators with predictive information about the underlying locations of AF driver by non-invasively and globally mapping the biatrial electrical activity. In the future, endocardial catheter mapping technology may benefit from the use of ECGI to enhance the diagnosis and ablation of AF.

Intra-Individual Comparison of Sinus and Ectopic Beats Probing the Ventricular Gradient's Activation Dependence

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.

A Deep Learning Architecture Using 3D Vectorcardiogram to Detect R-Peaks in ECG with Enhanced Precision

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.

A dynamic learning-based ECG feature extraction method for myocardial infarction detection

Objective.Myocardial infarction (MI) is one of the leading causes of human mortality in all cardiovascular diseases globally. Currently, the 12-lead electrocardiogram (ECG) is widely used as a first-line diagnostic tool for MI. However, visual inspection of pathological ECG variations induced by MI remains a great challenge for cardiologists, since pathological changes are usually complex and slight.Approach.To have an accuracy of the MI detection, the prominent features extracted from in-depth mining of ECG signals need to be explored. In this study, a dynamic learning algorithm is applied to discover prominent features for identifying MI patients via mining the hidden inherent dynamics in ECG signals. Firstly, the distinctive dynamic features extracted from the multi-scale decomposition of dynamic modeling of the ECG signals effectively and comprehensibly represent the pathological ECG changes. Secondly, a few most important dynamic features are filtered through a hybrid feature selection algorithm based on filter and wrapper to form a representative reduced feature set. Finally, different classifiers based on the reduced feature set are trained and tested on the public PTB dataset and an independent clinical data set.Main results.Our proposed method achieves a significant improvement in detecting MI patients under the inter-patient paradigm, with an accuracy of 94.75%, sensitivity of 94.18%, and specificity of 96.33% on the PTB dataset. Furthermore, classifiers trained on PTB are verified on the test data set collected from 200 patients, yielding a maximum accuracy of 84.96%, sensitivity of 85.04%, and specificity of 84.80%.Significance.The experimental results demonstrate that our method performs distinctive dynamic feature extraction and may be used as an effective auxiliary tool to diagnose MI patients.

Early detection of myocardial ischemia in 12-lead ECG using deterministic learning and ensemble learning

BACKGROUND AND OBJECTIVE

Early detection of myocardial ischemia is a necessary but difficult problem in cardiovascular diseases. Approaches that exclusively rely on classical ST and T wave changes on the standard 12-lead electrocardiogram (ECG) lack sufficient accuracy in detecting myocardial ischemia. This study aims to construct generalizable models for the detection of myocardial ischemia in patients with subtle ECG waveform changes (namely non-diagnostic ECG) using ensemble learning to integrate ECG dynamic features acquired via deterministic learning.

METHODS

First, cardiodynamicsgram (CDG), a noninvasive spatiotemporal electrocardiographic method, is generated through dynamic modeling of ECG signals using the deterministic learning algorithm. Then, the spectral fitting exponent, Lyapunov exponent, and Lempel-Ziv complexity are extracted from CDG. Subsequently, the bagging-based heterogeneous ensemble algorithm is applied on CDG features to generate diverse base classifiers and aggregate them with weighted voting to obtain an ensemble model for myocardial ischemia detection. Finally, we train and test the proposed heterogeneous ensemble model on a real-world clinical dataset. This dataset consists of 499 non-diagnostic 12-lead ECG records from 499 patients collected from three independent medical centers, including 383 patients with myocardial ischemia and 116 patients without ischemia.

RESULTS

With 10-times 5-fold cross-validation technology, our proposed method achieves an average accuracy of 89.10%, sensitivity of 91.72%, and specificity of 82.69% using the heterogeneous ensemble algorithm on the real-world clinical dataset. On three independent medical centers, our ensemble model also achieves accuracy performance over 82% for patients with non-diagnostic ECG. Furthermore, our ensemble model trained with real-world clinical data yields promising results of 91.11% accuracy, 90.49% sensitivity, and 92.88% specificity on the external test set of the public PTB dataset.

CONCLUSION

The experimental results demonstrate that the proposed model combining ensemble learning and deterministic learning presents excellent diagnostic accuracy and generalization in clinical practice, and could be implemented as a complement to the standard ECG in the clinical diagnosis of myocardial ischemia.

On the history of vectorcardiography: past, present, future

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.

A novel and effective ECG method to differentiate right from left ventricular outflow tract arrhythmias: Angle-corrected V2S

Background and aims

Standard 12-lead electrocardiogram (ECG) patterns combined with the anatomical cardiac long-axis angle revealed by chest X-ray can prevent the influence of cardiac rotation, physical shape, and lead position, so it may be an ideal means to predict the origin of the outflow tract (OT) ventricular arrhythmias (OTVAs) for ablation procedures. The study explores the value of this strategy in identifying the origin of OTVA.

Methods

This study was conducted using a retrospective cohort and a prospective cohort of consecutive patients at two centers. The anatomical cardiac long-axis angle was calculated by measuring the angle between the cardiac long-axis (a line joining the apex to the midpoint of the mitral annulus) and the horizontal plane on a chest X-ray. The V2S angle was calculated as the V2S amplitude times the angle. We ultimately enrolled 147 patients with symptomatic OTVAs who underwent successful radiofrequency catheter ablation (RFCA) (98 women (66.7%); mean age 46.9 ± 14.7 years; 126 right ventricular OT (RVOT) origins, 21 left ventricular OT (LVOT) origins) as a development cohort. The new algorithm was validated in 48 prospective patients (12 men (25.0%); mean age 48.0 ± 15.8 years; 36 RVOT, 12 LVOT origins).

Results

Patients with RVOT VAs had greater V2S, long-axis angle, and V2S angle than patients with LVOT VA (all P < 0.001). The cut-off V2S angle obtained by receiver operating characteristic (ROC) curve analysis was 58.28 mV° for the prediction of RVOT origin (sensitivity: 85.7%; specificity: 95.2%; positive predictive value: 99.1%; negative predictive value: 52.6%). The AUC achieved using the V2S angle was 0.888 (P < 0.001), which was the highest among all indexes (V2S/V3R: 0.887 (P < 0.016); TZ index: 0.858 (P < 0.001); V1-2 SRd: 0.876 (P < 0.001); V3 transition: 0.651 (P < 0.001)). In the prospective cohort, the V2S angle had a high overall accuracy of 93.8% and decreased the procedure time (P = 0.002).

Conclusion

V2S angle can be a novel measure that can be used to accurately differentiate RVOT from LVOT origins. It could help decrease ablation duration and radiation exposure.

Deep-Learning-Based Estimation of the Spatial QRS-T Angle from Reduced-Lead ECGs

The spatial QRS-T angle is a promising health indicator for risk stratification of sudden cardiac death (SCD). Thus far, the angle is estimated solely from 12-lead electrocardiogram (ECG) systems uncomfortable for ambulatory monitoring. Methods to estimate QRS-T angles from reduced-lead ECGs registered with consumer healthcare devices would, therefore, facilitate ambulatory monitoring. (1) Objective: Develop a method to estimate spatial QRS-T angles from reduced-lead ECGs. (2) Approach: We designed a deep learning model to locate the QRS and T wave vectors necessary for computing the QRS-T angle. We implemented an original loss function to guide the model in the 3D space to search for each vector's coordinates. A gradual reduction of ECG leads from the largest publicly available dataset of clinical 12-lead ECG recordings (PTB-XL) is used for training and validation. (3) Results: The spatial QRS-T angle can be estimated from leads {I, II, aVF, V2} with sufficient accuracy (absolute mean and median errors of 11.4° and 7.3°) for detecting abnormal angles without sacrificing patient comfortability. (4) Significance: Our model could enable ambulatory monitoring of spatial QRS-T angles using patch- or textile-based ECG devices. Populations at risk of SCD, like chronic cardiac and kidney disease patients, might benefit from this technology.

An Exploratory Study on Vectorcardiographic Identification of the Site of Origin of Focally Induced Premature Depolarizations in Horses, Part I: The Atria

In human cardiology, the anatomical origin of atrial premature depolarizations (APDs) is derived from P wave characteristics on a 12-lead electrocardiogram (ECG) and from vectorcardiography (VCG). The objective of this study is to differentiate between anatomical locations of APDs and to differentiate APDs from sinus rhythm (SR) based upon VCG characteristics in seven horses without cardiovascular disease. A 12-lead ECG was recorded under general anaesthesia while endomyocardial atrial pacing was performed (800−1000 ms cycle length) at the left atrial free wall and septum, right atrial free wall, intervenous tubercle, as well as at the junction with the cranial and caudal vena cava. Catheter positioning was guided by 3D electro-anatomical mapping and transthoracic ultrasound. The VCG was calculated from the 12-lead ECG using custom-made algorithms and was used to determine the mean electrical axis of the first and second half of the P wave. An ANOVA for spherical data was used to test if the maximal directions between each paced location and the maximal directions between every paced location and SR were significantly (p < 0.05) different. Atrial pacing data were not available from the LA septum in three horses, the intervenous tubercle in two horses, and from the LA free wall in one horse. The directions of the maximal electrical axes showed significant differences between all paced locations and between the paced locations and SR. The current results suggest that VCG is useful for identifying the anatomical origin of an atrial ectopy.

An Exploratory Study on Vectorcardiographic Identification of the Site of Origin of Focally Induced Premature Depolarizations in Horses, Part II: The Ventricles

In human cardiology, the anatomical origin of ventricular premature depolarizations (VPDs) is determined by the characteristics of a 12-lead electrocardiogram (ECG). Former studies in horses had contradictory results regarding the diagnostic value of the 12-lead ECG and vectorcardiography (VCG), which results were attributed to the different cardiac conduction system in this species. The objective of this study was to determine if the anatomical origin of pacing-induced VPDs could be differentiated in horses based upon VCG characteristics. A 12-lead ECG was recorded in seven horses under general anesthesia while right and left ventricular endomyocardial pacing was performed (800−1000 ms cycle length) at the apex, mid and high septum and mid and high free wall, and at the right ventricular outflow tract. Catheter positioning was guided by 3D electro-anatomical mapping and echocardiography. A median complex, obtained from four consecutive complexes, was calculated for each pacing location and sinus rhythm. The VCG was calculated from the 12-lead ECG-derived median complexes using custom-made algorithms and was used to determine the initial and maximum electrical axes of the QRS complex. An ANOVA for spherical data was used to test if VCGs between each paced location and between pacing and sinus rhythm were significantly (p < 0.05) different. The model included the radius, azimuth and elevation of each electrical axis. Pacing induced significantly different initial and maximum electrical axes between different locations and between pacing and sinus rhythm. The current results suggest that VCG is a useful technique to identify the anatomical origin of ventricular ectopy in horses.

Association between vectorcardiographic QRS area and incident heart failure diagnosis and mortality among patients with left bundle branch block: A register-based cohort study

BACKGROUND

QRS duration and morphology including left bundle branch block (LBBB) are the most widely used electrocardiogram (ECG) markers for assessing ventricular dyssynchrony and predicting heart failure (HF). However, the vectorcardiographic QRS area may more accurately identify delayed left ventricular activation and HF development.

OBJECTIVE

We investigated the association between QRS area and incident HF risk in patients with LBBB.

METHODS

By crosslinking data from Danish nationwide registries, we identified patients with a first-time digital LBBB ECG between 2001 and 2015. The vectorcardiographic QRS area was derived from a 12‑lead ECG using the Kors transformation method and grouped into quartiles. The endpoint was a composite of HF diagnosis, filled prescriptions for loop diuretics, or death from HF. Cause-specific multivariable Cox regression was used to compute hazard ratios(HR) with 95% confidence intervals(CI).

RESULTS

We included 3316 patients with LBBB free from prior HF-related events (median age, 72 years; male, 40%). QRS area quartiles comprised Q1, 36-98 μVs; Q2, 99-119 μVs; Q3, 120-145 μVs; and Q4, 146-295 μVs. During a 5-year follow-up, 31% of patients reached the composite endpoint, with a rate of 39% in the highest quartile Q4. A QRS area in quartile Q4 was associated with increased hazard of the composite endpoint (HR:1.48, 95%CI:1.22-1.80) compared with Q1.

CONCLUSIONS

Among primary care patients with newly discovered LBBB, a large vectorcardiographic QRS area (146-295 μVs) was associated with an increased risk of incident HF diagnosis, filling prescriptions for loop diuretics, or dying from HF within 5-years.

Multiplex recurrence networks from multi-lead ECG data

We present an integrated approach to analyze the multi-lead electrocardiogram (ECG) data using the framework of multiplex recurrence networks (MRNs). We explore how their intralayer and interlayer topological features can capture the subtle variations in the recurrence patterns of the underlying spatio-temporal dynamics of the cardiac system. We find that MRNs from ECG data of healthy cases are significantly more coherent with high mutual information and less divergence between respective degree distributions. In cases of diseases, significant differences in specific measures of similarity between layers are seen. The coherence is affected most in the cases of diseases associated with localized abnormality such as bundle branch block. We note that it is important to do a comprehensive analysis using all the measures to arrive at disease-specific patterns. Our approach is very general and as such can be applied in any other domain where multivariate or multi-channel data are available from highly complex systems.

Dispersion of ventricular repolarization: Temporal and spatial

Repolarization heterogeneity (RH) is an intrinsic property of ventricular myocardium and the reason for T-wave formation on electrocardiogram (ECG). Exceeding the physiologically based RH level is associated with appearance of life-threatening ventricular arrhythmias and sudden cardiac death. In this regard, an accurate and comprehensive evaluation of the degree of RH parameters is of importance for assessment of heart state and arrhythmic risk. This review is devoted to comprehensive consideration of RH phenomena in terms of electrophysiological processes underlying RH, cardiac electric field formation during ventricular repolarization, as well as clinical significance of RH and its reflection on ECG parameters. The formation of transmural, apicobasal, left-to-right and anterior-posterior gradients of action potential durations and end of repolarization times resulting from the heterogenous distribution of repolarizing ion currents and action potential morphology throughout the heart ventricles, and the different sensitivity of myocardial cells in different ventricular regions to the action of pharmacological agents, temperature, frequency of stimulation, etc., are being discussed. The review is focused on the fact that RH has different aspects – temporal and spatial, global and local; ECG reflection of various RH aspects and their clinical significance are being discussed. Strategies for comprehensive assessment of ventricular RH using different ECG indices reflecting various RH aspects are presented.

Automatic identification of a stable QRST complex for non-invasive evaluation of human cardiac electrophysiology

BACKGROUND

A vectorcardiography approach to electrocardiology contributes to the non-invasive assessment of electrical heterogeneity in the ventricles of the heart and to risk stratification for cardiac events including sudden cardiac death. The aim of this study was to develop an automatic method that identifies a representative QRST complex (QRSonset to Tend) from a Frank vectorcardiogram (VCG). This method should provide reliable measurements of morphological VCG parameters and signal when such measurements required manual scrutiny.

METHODS

Frank VCG was recorded in a population-based sample of 1094 participants (550 women) 50-65 years old as part of the Swedish CArdioPulmonary bioImage Study (SCAPIS) pilot. Standardized supine rest allowing heart rate stabilization and adaptation of ventricular repolarization preceded a recording period lasting ≥5 minutes. In the Frank VCG a recording segment during steady-state conditions and with good signal quality was selected based on QRST variability. In this segment a representative signal-averaged QRST complex from cardiac cycles during 10s was selected. Twenty-eight morphological parameters were calculated including both conventional conduction intervals and VCG-derived parameters. The reliability and reproducibility of these parameters were evaluated when using completely automatic and automatic but manually edited annotation points.

RESULTS

In 1080 participants (98.7%) our automatic method reliably selected a representative QRST complex where its instability measure effectively identified signal variability due to both external disturbances ("noise") and physiologic and pathophysiologic variability, such as e.g. sinus arrhythmia and atrial fibrillation. There were significant sex-related differences in 24 of 28 VCG parameters. Some VCG parameters were insensitive to the instability value, while others were moderately sensitive.

CONCLUSION

We developed an automatic process for identification of a signal-averaged QRST complex suitable for morphologic measurements which worked reliably in 99% of participants. This process is applicable for all non-invasive analyses of cardiac electrophysiology including risk stratification for cardiac death based on such measurements.

Spatial peak and mean QRS-T angles: A comparison of similar but different emerging risk factors for cardiac death

BACKGROUND

The spatial peak and mean QRS-T angles are scientifically but not clinically established risk factors for cardiovascular events including cardiac death. The study aims were to compare these angles, assess their association with hypertension (HT) and diabetes mellitus (DM), and explore the relation between the mean QRS-T angle and the ventricular gradient (VG; reflecting electrical heterogeneity), which both are derived from the QRSarea and Tarea vectors.

METHODS

Altogether 1094 participants (aged 50-65 years, 550 women) from the pilot of the population-based Swedish CArdioPulmonary bioImage Study with Frank vectorcardiographic recordings were included and divided into 5 subgroups: apparently healthy n = 320; HT n = 311; DM n = 33; DM + HT n = 53; miscellaneous conditions n = 377. Abnormal peak and mean QRS-T angles were defined as >95th percentile.

RESULTS

Peak QRS-T angles were generally narrower than the mean QRS-T angles; both were narrower in women than in men. Abnormal peak (>124°) and/or mean (>119°) QRS-T angles were found in 73 participants (6.7%). The concordance regarding abnormal versus normal-borderline QRS-T angles was good (Cohen's kappa 0.61). The prevalence of abnormal angles varied from 2.5% in healthy to 21.2% in DM. There was an inverse logarithmical relation between the mean QRS-T angle and the VG.

CONCLUSIONS

The peak and mean QRS-T angles are not interchangeable but complementary. DM, HT, sex and absence of disease are important determinants of both QRS-T angles. The mean QRS-T angle and the VG relationship is complex. All three VCG derived measures reflect related but differing electrophysiological properties and have potential prognostic value vis-à-vis cardiovascular events.

Adaptation of ventricular repolarization duration and dispersion during changes in heart rate induced by atrial stimulation

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.

Applicability of the Electro-Vectorcardiogram in Current Clinical Practice

The electrocardiogram (ECG) has been reinvigorated by the identification of electrical alterations that were not definitely clarified before. In this context, and mainly regarding the definition of arrhythmogenic substrates, the association of the ECG with the vectorcardiogram (VCG) has gathered much more information about the cardiac electrical phenomena, thus allowing us to differentiate potentially fatal cases from benign ones. Obtaining a VCG concomitantly with the performance of an ECG has led to a significant gain in the definition of extremely sophisticated pathologies, which function suffer some type of structural or dynamic alterations, involving either the reduction or enhancement of ionic channels and currents.

The classic aspects of the ECG/VCG association in the differential diagnosis of myocardial infarctions, conduction disorders, atrial and ventricular hypertrophies, and the correlations between these electrical disorders are still valid and assertive. The association of these pathologies is further clarified when they are seen through the ECG/VCG dyad.

The three-dimensional spatial orientation of both the atrial and the ventricular activity provides a far more complete observation tool than the ECG linear form. The modern analysis of the ECG and its respective VCG, simultaneously obtained by the recent technique called electro-vectorcardiogram (ECG/VCG), brought a significant gain for the differential diagnosis of some pathologies. Therefore, we illustrate how this type of analysis can elucidate some of the most important diagnoses found in our daily clinical practice as cardiologists.

Toward a novel semi-invasive activation mapping tool for the diagnosis of supraventricular arrhythmias from the esophagus

AIMS

Supraventricular arrhythmia diagnosis using the surface electrocardiogram (sECG) is often cumbersome due to limited atrial signal quality. In some instances, use of esophageal electrocardiography (eECG) may facilitate the diagnosis. Here, we present a novel approach to reconstruct cardiac activation maps from eECG recordings.

METHODS

eECGs and sECGs were recorded from 19 individuals using standard acquisition tools. From the recordings, algorithms were developed to estimate the esophageal ECG catheter's position and to reconstruct high-resolution mappings of the cardiac electric activity projected in the esophagus over time.

RESULTS

Esophageal two-dimensional activation maps were created for five healthy individuals and 14 patients suffering from different arrhythmias. The maps are displayed as time-dependent contour plots, which not only show voltage over time as conventional ECGs, but also the location, direction, and projected propagation speed of the cardiac depolarization wavefront in the esophagus. Representative examples of sinus rhythm, atrial flutter, and ventricular pre-excitation are shown.

CONCLUSION

The methodology presented in this report provides a high-resolution view of the cardiac electric field in the esophagus. It is the first step toward a three-dimensional mapping system, which shall be able to reconstruct a three-dimensional view of the cardiac activation from recordings within the esophagus.

Electrical remodeling after percutaneous atrial septal defect closure in pediatric and adult patients

BACKGROUND

Several studies have reported changes in electrocardiographic variables after atrial septal defect (ASD) closure. However no temporal electro-and vectorcardiographic changes have been described from acute to long-term follow-up at different ages. We aimed to study electrical remodeling after percutaneous ASD closure in pediatric and adult patients.

METHODS

ECGs of 69 children and 75 adults (median age 6 [IQR 4-11] years and 45 [IQR 33-54] years, respectively) were retrospectively selected before percutaneous ASD closure and at acute (1-7 days), intermediate (4-14 weeks) and late (6-18 months) follow-up. Apart from electrocardiographic variables, spatial QRS-T angle and ventricular gradient (VG) were derived from mathematically-synthesized vectorcardiograms.

RESULTS

In both pediatric and adult patients, the heart rate decreased immediately post-closure, which persisted to late follow-up. The P-wave amplitude also decreased acutely post-closure, but remained unchanged at later follow-up. The PQ duration shortened immediately in children and at intermediate follow-up in adults. The QRS duration and QTc interval decreased at intermediate-term follow-up in both children and adults. In both groups the spatial QRS-T angle decreased at late follow-up. The VG magnitude increased at intermediate follow-up in children and at late follow-up in adults, after an initial decrease in children.

CONCLUSION

In both pediatric and adult ASD patients, electrocardiographic changes mainly occurred directly after ASD closure except for shortening of QRS duration and QTc interval, which occurred at later follow-up. Adults also showed late changes in PQ duration. At 6-to-18 month post-closure, the spatial QRS-T angle decreased, reflecting increased electrocardiographic concordance. The initial acute decrease in VG in children, which was followed by a significant increase, may be the effect of action potential duration dynamics directly after percutaneous ASD closure.

Serial electrocardiography to detect newly emerging or aggravating cardiac pathology: a deep-learning approach

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.

Normal values of the ventricular gradient and QRS-T angle, derived from the pediatric electrocardiogram

BACKGROUND

Normal values of the mathematically-synthesized vectorcardiogram (VCG) are lacking for children. Therefore, the objective of this study was to assess normal values of the pediatric synthesized VCG (spatial QRS-T angle [SA] and ventricular gradient [VG]).

METHODS

Electrocardiograms (ECGs) of 1263 subjects (0-24 years) with a normal heart were retrospectively selected. VCGs were synthesized by the Kors matrix. Normal values (presented as 2nd and 98th percentiles) were assessed by quantile regression with smoothing by splines.

RESULTS

Our results show that heart rate decreased over age, QRS duration increased and QTc interval remained constant. The SA initially decreased and increased again from the age of 8 years. The VG magnitude was relatively stable until the age of 2 years, after which it increased.

CONCLUSION

Normal values of the pediatric ECG and VCG (VG and SA) were established. These normal values could be important for future studies using VG and SA for risk stratification in heart disease in children.

Mechanism of electrocardiographic T-wave flattening in diabetes mellitus: experimental and simulation study

In the present study we investigated the contribution of ventricular repolarization time (RT) dispersion (the maximal difference in RT) and RT gradients (the differences in RT in apicobasal, anteroposterior and interventricular directions) to T-wave flattening in a setting of experimental diabetes mellitus. In 9 healthy and 11 diabetic (alloxan model) open-chest rabbits, we measured RT in ventricular epicardial electrograms. To specify the contributions of apicobasal, interventricular and anteroposterior RT gradients and RT dispersion to the body surface potentials we determined T-wave voltage differences between modified upper- and lower-chest precordial leads (T-wave amplitude dispersions, TWAD). Expression of RT gradients and RT dispersion in the correspondent TWAD parameters was studied by computer simulations. Diabetic rabbits demonstrated flattened T-waves in precordial leads associated with increased anteroposterior and decreased apicobasal RT gradients (P<0.05) due to RT prolongation at the apex. For diabetics, simulations predicted the preserved T-vector length and altered sagittal and longitudinal TWAD proven by experimental measurements. T-wave flattening in the diabetic rabbits was not due to changes in RT dispersion, but reflected the redistributed ventricular repolarization pattern with prolonged apical repolarization resulting in increased anteroposterior and decreased apicobasal RT gradients.

Mechanisms of exercise-recovery hysteresis in the ECG: ISCE 2015 paper

This paper gives an overview of multiple factors, like the mechanisms governing rate adaptation of ventricular action potentials and autonomic mechanisms, which play a role in the genesis of exercise-recovery hysteresis in the ECG. It also discusses the possible association between exercise-recovery ECG hysteresis and arrhythmogeneity.