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Introduction: Measurement of the spatial ventricular gradient (SVG), spatial QRST angles, and other vectorcardiographic measures of myocardial electrical heterogeneity have emerged as novel risk stratification methods for sudden cardiac death and other adverse cardiovascular events. Prior studies of normal limits of these measurements included primarily young, healthy, White volunteers, but normal limits in older patients are unknown. The influence of race and body mass index (BMI) on these measurements is also unclear. Methods: Normal 12‐lead electrocardiograms (ECGs) from a single center were identified. Patients with abnormal cardiovascular, pulmonary, or renal history (assessed by International Classification of Disease [ICD‐9/ICD‐10] codes) or abnormal cardiovascular imaging were excluded. The SVG and QRST angles were measured and stratified by age, sex, and race. Multivariable linear regression was used to assess the influence of age, BMI, and heart rate (HR) on these measurements. Results: Among 3292 patients, observed ranges of SVG and QRST angles (peak and mean) differed significantly based on sex, age, and race. Sex differences attenuated with increasing age. Men tended to have larger SVG magnitude (60.4 [46.1–77.8] vs. 52.5 [41.3–65.8] mv*ms, p <.0001) and elevation, and more anterior/negative SVG azimuth (−14.8 [−25.1 to −4.3] vs. 1.3 [−9.8 to 10.5] deg, p <.0001) compared to women. Men also had wider QRST angles. Observed ranges varied significantly with BMI and HR. SVG and QRST angle measurements were robust to different filtering bandwidths and moderate fiducial point annotation errors, but were heavily affected by changes in baseline correction. Conclusions: Age, sex, race, BMI, and HR significantly affect the range of SVG and QRST angles in patients with normal ECGs and no known cardiovascular disease, and should be accounted for in future studies. An online calculator for prediction of these "normal limits" given demographics is provided at https://bivectors.github.io/gehcalc/. [ABSTRACT FROM AUTHOR]

Introduction We hypothesized that very high-density mapping of typical atrial flutter (AFL) would facilitate a more complete understanding of its circuit. Such very high-density mapping was performed with the RhythmiaTM (Boston Scientific) mapping system using its 64 electrode basket catheter. Methods and results Data were acquired from 13 patients in AFL. Functional anatomy of the right atrium (RA) was readily identified during mapping including the Crista Terminalis and Eustachian ridge. The leading edge of the activation wavefront was identified without interruption and its conduction velocity (CV) was calculated. CV was not different at the cavotricuspid isthmus (CTI) compared to the remainder of the RA (1.02 vs. 1.03 m/s, P = 0.93). The sawtooth pattern of the surface electrocardiogram (EKG) flutter waves was compared to the position of the dominant wavefront. The downslope of the surface EKG flutter waves represented on average 73% ± 9% of the total flutter cycle length. During the downslope, the activation wavefront traveled significantly further than during the upslope (182 ± 21 milliseconds vs. 68 ± 29 milliseconds, P < 0.0001) with no change in CV between the two phases (0.88 vs. 0.91 m/s, P = 0.79). Conclusion CV at the CTI is not slower than other RA regions during typical AFL. The gradual downslope of the sawtooth EKG is not due to slow conduction at the CTI suggesting that success of ablation at this site relates to anatomical properties rather than the presence of a 'slow isthmus.' [ABSTRACT FROM AUTHOR]

Syncope is a common symptom with many causes. Affecting a large proportion of the population, both young and old, it represents a significant healthcare burden. The diagnostic approach to syncope should be focused on the initial evaluation, which includes a detailed clinical history, physical examination and 12-lead electrocardiogram. Following the initial evaluation, patients should be risk-stratified into high or low-risk groups in order to guide further investigations and management. Patients with high-risk features should be investigated further to exclude significant structural heart disease or arrhythmia. The ideal currently-available investigation should allow ECG recording during a spontaneous episode of syncope, and when this is not possible, an implantable loop recorder may be considered. In the emergency room setting, acute causes of syncope must also be considered including severe cardiovascular compromise due to pulmonary, cardiac or vascular pathology. While not all patients will receive a conclusive diagnosis, risk-stratification in patients to guide appropriate investigations in the context of a diagnostic algorithm should allow a benign prognosis to be maintained. [ABSTRACT FROM AUTHOR]

There is increasing focus on applying deep learning methods to electrocardiograms (ECGs), with recent studies showing that neural networks (NNs) can predict future heart failure or atrial fibrillation from the ECG alone. However, large numbers of ECGs are needed to train NNs, and many ECGs are currently only in paper format, which are not suitable for NN training. We developed a fully-automated online ECG digitisation tool to convert scanned paper ECGs into digital signals. Using automated horizontal and vertical anchor point detection, the algorithm automatically segments the ECG image into separate images for the 12 leads and a dynamical morphological algorithm is then applied to extract the signal of interest. We then validated the performance of the algorithm on 515 digital ECGs, of which 45 were printed, scanned and redigitised. The automated digitisation tool achieved 99.0% correlation between the digitised signals and the ground truth ECG (n = 515 standard 3-by-4 ECGs) after excluding ECGs with overlap of lead signals. Without exclusion, the performance of average correlation was from 90 to 97% across the leads on all 3-by-4 ECGs. There was a 97% correlation for 12-by-1 and 3-by-1 ECG formats after excluding ECGs with overlap of lead signals. Without exclusion, the average correlation of some leads in 12-by-1 ECGs was 60–70% and the average correlation of 3-by-1 ECGs achieved 80–90%. ECGs that were printed, scanned, and redigitised, our tool achieved 96% correlation with the original signals. We have developed and validated a fully-automated, user-friendly, online ECG digitisation tool. Unlike other available tools, this does not require any manual segmentation of ECG signals. Our tool can facilitate the rapid and automated digitisation of large repositories of paper ECGs to allow them to be used for deep learning projects. [ABSTRACT FROM AUTHOR]