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Electrocardiographic alternans, consisting of P-wave alternans (PWA), QRS-complex alternans (QRSA) and Twave alternans (TWA), is an index of cardiac risk. However, only automated TWA measurement methods have been proposed so far. Here, we presented the enhanced adaptive matched filter (EAMF) method and tested its reliability in both simulated and experimental conditions. Our methodological novelty consists in the introduction of a signal enhancement procedure according to which all sections of the electrocardiogram (ECG) but the wave of interest are set to baseline, and in the extraction of the alternans area (AAr) in addition to the standard alternans amplitude (AAm). Simulated data consisted of 27 simulated ECGs representing all combinations of PWA, QRSA and TWA of low (10 mu V) and high (100 mu V) amplitude. Experimental data consisted of exercise 12-lead ECGs from 266 heart failure patients with an implanted cardioverter defibrillator for primary prevention. EAMF was able to accurately identify and measure all kinds of simulated alternans (absolute maximum error equal to 2%). Moreover, different alternans kinds were simultaneously present in the experimental data and EAMF was able to identify and measure all of them (AAr: 545 mu V x ms, 762 mu V x ms and 1382 mu V x ms; AAm: 5 mu V, 9 mu V and 7 mu V; for PWA, QRSA and TWA, respectively) and to discriminate TWA as the prevalent one (with the highest AAr). EAMF accurately identifies and measures all kinds of electrocardiographic alternans. EAMF may support determination of incremental clinical utility of PWA and QRSA with respect to TWA only.

Background Early prehospital recognition of critical conditions such as ST‐segment–elevation myocardial infarction (STEMI) has prognostic relevance. Current international electrocardiographic STEMI thresholds are predominantly based on individuals of Western European descent. However, because of ethnic electrocardiographic variability both in health and disease, there is a need to reevaluate diagnostic ST‐segment elevation thresholds for different populations. We hypothesized that fulfillment of ST‐segment elevation thresholds of STEMI criteria (STE‐ECGs) in apparently healthy individuals is ethnicity dependent. Methods and Results HELIUS (Healthy Life in an Urban Setting) is a multiethnic cohort study including 10 783 apparently healthy subjects of 6 different ethnicities (African Surinamese, Dutch, Ghanaian, Moroccan, South Asian Surinamese, and Turkish). Prevalence of STE‐ECGs across ethnicities, sexes, and age groups was assessed with respect to the 2 international STEMI thresholds: sex and age specific versus sex specific. Mean prevalence of STE‐ECGs was 2.8% to 3.4% (age/sex‐specific and sex‐specific thresholds, respectively), although with large ethnicity‐dependent variability. Prevalences in Western European Dutch were 2.3% to 3.0%, but excessively higher in young (45% STE‐ECGs. Conclusions The accuracy of diagnostic tests partly relies on background prevalence in healthy individuals. In apparently healthy subjects, there is a highly variable ethnicity‐dependent prevalence of ECGs with ST‐segment elevations exceeding STEMI thresholds. This has potential consequences for STEMI evaluations in individuals who are not of Western European descent, putatively resulting in adverse outcomes with both over‐ and underdiagnosis of STEMI.

Introduction: The YEARS algorithm was successfully developed to reduce the number of computed tomography pulmonary angiography (CTPA) investigations in the diagnostic management of patients with suspected pulmonary embolism (PE), although half of patients still needed to be referred for CTPA. We hypothesized that ECG derived ventricular gradient optimized for right ventricular pressure overload (VG-RVPO), an easy to use tool for detecting PE-induced pulmonary hypertension (PH), may further improve the efficiency of the YEARS algorithm.Methods: In this post-hoc analysis of the Years study, ECGs of 479 patients with suspected PE managed according to the YEARS algorithm were available for analysis. The diagnostic performance of VG-RVPO was assessed and likelihood ratios were calculated.Results: PE was diagnosed in 88 patients (18%). In patients with confirmed PE, 34% had an abnormal VG-RVPO versus 24% of those without PE (odds ratio 1.6; 95%CI 0.94-2.6). The mean VG-RVPO was -22 +/- 13 and did not differ between the two patient groups (-22 versus-20; mean difference - 2, 95% CI -4.8 to 1.3). The sensitivity of VG-RVPO for PE was 24% (95%CI 34-45), the specificity 76% (95%CI 71-80) and the c-statistic 0.45 (95% CI 0.38-0.51). When combined with the YEARS algorithm, the likelihood ratios of VG-RVPO remained close to 1.0. Ruling out PE in patients with an indication for CTPA based on a normal VG-RVPO would have resulted in 58 missed cases.Conclusions: The VG-RVPO has no diagnostic value for suspected acute PE, either as stand-alone diagnostic test or combined with the YEARS algorithm.Condensed abstract: This post-hoc analysis of the YEARS study failed to demonstrate incremental diagnostic value of VG-RVPO for acute PE, either as stand-alone diagnostic test or combined with the YEARS algorithm. Nevertheless, the role of VG-RVPO recorded on admission could potentially be valuable in the risk stratification of PE during hospitalization, although this remains to be studied. (C) 2020 Published by Elsevier Inc.

Atrial fibrillation (AF) is a common cardiac disorder that can cause severe complications. AF diagnosis is typically based on the electrocardiogram (ECG) evaluation in hospitals or in clinical facilities. The aim of the present work is to propose a new artificial neural network for reliable AF identification in ECGs acquired through portable devices. A supervised fully connected artificial neural network (RSL_ANN), receiving 19 ECG features (11 morphological, 4 on F waves and 4 on heart-rate variability (HRV)) in input and discriminating between AF and non-AF classes in output, was created using the repeated structuring and learning (RSL) procedure. RSL_ANN was created and tested on 8028 (training: 4493, validation: 1125, testing: 2410) annotated ECGs belonging to the &ldquo, AF Classification from a Short Single Lead ECG Recording&rdquo, database and acquired with the portable KARDIA device by AliveCor. RSL_ANN performance was evaluated in terms of area under the curve (AUC) and confidence intervals (CIs) of the received operating characteristic. RSL_ANN performance was very good and very similar in training, validation and testing datasets. AUC was 91.1% (CI: 89.1%&ndash, 93.0%), 90.2% (CI: 86.2%&ndash, 94.3%) and 90.8% (CI: 88.1%&ndash, 93.5%) for the training, validation and testing datasets, respectively. Thus, RSL_ANN is a promising tool for reliable identification of AF in ECGs acquired by portable devices.

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

Electrocardiographic Decision Support – Myocardial Ischaemia (EDS-MI) is a graphical decision support for detection and localization of acute transmural ischaemia. A recent study indicated that EDS-MI performs well for detection of acute transmural ischaemia. However, its performance has not been tested in patients with non-ischaemic ST-deviation. We aimed to optimize the diagnostic accuracy of EDS-MI in patients with verified acute coronary occlusion as well as patients with non-ischaemic ST deviation and compare its performance with STEMI criteria. We studied 135 patients with non-ischaemic ST deviation (perimyocarditis, left ventricular hypertrophy, takotsubo cardiomyopathy and early repolarization) and 117 patients with acute coronary occlusion. In 63 ischaemic patients, the extent and location of the ischaemic area (myocardium at risk) was assessed by both cardiovascular magnetic resonance imaging and EDS-MI. Sensitivity and specificity of ST elevation myocardial infarction criteria were 85% (95% confidence interval (CI) 77, 90) and 44% (95% CI 36, 53) respectively. Using EDS-MI, sensitivity and specificity increased to 92% (95% CI 85, 95) and 81% (95% CI 74, 87) respectively (p=0.035 and p

Patients with systemic sclerosis (SSc) are at risk for developing pulmonary hypertension (PH) which is a major cause of death in this population. Echocardiographic (TTE) derived pulmonary arterial pressure (PAP) can be unreliable for the early detection of PH. Previous studies demonstrate that the ECG derived ventricular gradient optimized for right ventricular pressure overload (VG-RVPO) can detect PH in a heterogeneous population suspected of PH. The aim of this study is to assess the use of the VG-RVPO as a screening and monitoring instrument of early PH in SSc patients.Serial ECGs and TTEs from twenty-seven SSc patients who underwent right heart catheterization (RHC) were retrospectively analyzed. The changes in PAP and VG-RVPO over time were studied in patients with and without diagnosed PH.Twenty-four patients (52.5% female, mean age 58.4 years SD 14.3) were studied. In eleven patients PH was confirmed with RHC. In these patients VG-RVPO was significantly higher -8 ± 19 than in patients without PH -23 ± 10 mV·ms, (P 0.05). In addition, in PH patients the VG-RVPO increased over time in contrast to patients without PH (P 0.01). The VG was more sensitive to detect disease progression in earlier stages of disease as compared to echocardiographic derived PAP.The VG-RVPO is a sensitive, non-invasive and cost effective tool for early detection of PH in SSc patients. Serial measurements indicate that the VG-RVPO can be used as a follow-up instrument and outperforms TTE to detect early changes in right ventricular pressure over time.

This paper describes a substantial part of the international mentoring network of students and young investigators in electrocardiology that developed around Dr. Galen Wagner (1939–2016), including many experiences of his mentees and co-mentors. The paper is meant to stimulate thinking about international mentoring as a means to achieve important learning experiences and personal development of young investigators, to intensify international scientific cooperation, and to stimulate scientific production.

Background Risk-stratification in pulmonary embolism (PE) includes clinical decision rules, biomarkers and signs of right ventricular (RV) overload. The vector electrocardiogram is a diagnostic tool in which the ECG-derived ventricular gradient optimized for right ventricular pressure overload (VG-RVPO) can be used to detect patients with increased pulmonary pressure. The primary aim of this study was to assess the association of VG-RVPO and CT-assessed RV/LV diameter ratio as well as the prognostic value of an abnormal VG-RVPO for early adverse events in PE patients. Methods In this single-center retrospective study, adult patients with acute PE were identified via the hospital’s administrative system. Adverse events were defined as the combined outcome of 30-day overall mortality, recurrent venous thromboembolism, the need for mechanical ventilation, the need for inotropic or vasopressive therapy and/or cardiac resuscitation. Results VG-RVPO analysis was available for 164 patients diagnosed with PE between December 2015 and September 2018. Abnormal VG-RVPO was associated with a CTPA-assessed RV/LV diameter ratio >1.0 (OR 2.0; 95%CI 1.0–3.9). The adverse 30-day composite outcome occurred in 16 of 66 patients (24%) with abnormal VG-RVPO compared to 22 of 98 patients (22%) with normal VG-RVPO (OR 1.1, 95%CI 0.53–2.3). The net reclassification improvement of VG-RVPO on top of RV dilatation for predicting early adverse events was -12%, indicating no additional prognostic value of VG-RVPO on top of RV/LV diameter ratio. Conclusions Although we observed an association between RV dilatation, abnormal ECG-derived VG-RVPO was not associated with acute PE associated adverse events.

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. Electronic supplementary material The online version of this article (10.1186/s12938-019-0630-9) contains supplementary material, which is available to authorized users.

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.

Background. Larger one-time values of spatial QRS-T angle (SA) are associated with risk. However, experience how serial changes in SA $(\Delta SA)$ should be interpreted is lacking. Even within normal limits, any $\Delta SA$ likely signifies electrical remodeling. This study aimed to assess the impact of choosing either $\Delta SA$ or $\vert \Delta SA\vert$ as one of a set of serial ECG difference features that constitute the input for our deep learning serial-ECG classifier (DLSEC). Methods. DLSEC was trained and tested to detect emerging pathology in two serial ECG databases: a heart failure database and an acute ischemia database. Either $\Delta SA$ or $\vert \Delta SA\vert$ were among 13 features of serial-ECG differences. DLSEC was dynamically generated during learning, and testing area under the curve (AUC) of the receiver operating characteristic was computed. Results. The DLSECs performed well in emerging heart failure as well as in acute ischemia: testing AUCs were 72% and 84% for the heart failure database and 77% and 83% for the ischemia database, for $\Delta SA$ or $\vert \Delta SA\vert$ among the features, respectively. Conclusion. $\vert \Delta SA\vert$ among the features was superior to $\Delta SA$ in discriminating cases and controls. Our study supports the concept that any $\Delta SA$ , irrespective of its sign, indicates a worsening clinical condition. Further corroboration requires studies in other clinical situations.