Clinically interpretable multiclass neural network for discriminating cardiac diseases.

Background: Deep-learning applications in cardiology typically perform trivial binary classification and are able to discriminate between subjects affected or not affected by a specific cardiac disease. However, this working scenario is very different from the real one, where clinicians are required to recognize the occurrence of one cardiac disease among the several possible ones, performing a multiclass classification. The present work aims to create a new interpretable deep-learning tool able to perform a multiclass classification and, thus, discriminate among several different cardiac diseases.

Neurocardiology: Major mechanisms and effects.

Neurocardiology is a broad interdisciplinary specialty investigating how the cardiovascular and nervous systems interact. In this brief introductory review, we describe several key aspects of this interaction with specific attention to cardiovascular effects. The review introduces basic anatomy and discusses physiological mechanisms and effects that play crucial roles in the interaction of the cardiovascular and nervous systems, namely: the cardiac neuraxis, the taxonomy of the nervous system, integration of sensory input in the brainstem, influences of the autonomic nervous system (ANS) on heart and vasculature, the neural pathways and functioning of the arterial baroreflex, receptors and ANS effects in the walls of blood vessels, receptors and ANS effects in excitable cells in the heart, ANS effects on heart rate and sympathovagal balance, endo-epicardial inhomogeneity, ANS effects with a balanced vagal and sympathetic stimulation, sympathovagal interaction, arterial baroreflex, baroreflex sensitivity and heart rate variability, arrhythmias and the arterial baroreflex, the cardiopulmonary baroreflex, the exercise pressor reflex, exercise-recovery hysteresis, mental stress, cardiac-cardiac reflexes, the cardiac sympathetic afferent reflex (CSAR), and neuromodulation.

Impact of QRS misclassifications on heart-rate-variability parameters (results from the CARLA cohort study).

Background: Heart rate variability (HRV), an important marker of autonomic nervous system activity, is usually determined from electrocardiogram (ECG) recordings corrected for extrasystoles and artifacts. Especially in large population-based studies, computer-based algorithms are used to determine RR intervals. The Modular ECG Analysis System MEANS is a widely used tool, especially in large studies.

Context-independent identification of myocardial ischemia in the prehospital ECG of chest pain patients.

Non-traumatic chest pain is a frequent reason for an urgent ambulance visit of a patient by the emergency medical services (EMS). Chest pain (or chest pain-equivalent symptoms) can be innocent, but it can also signal an acute form of severe pathology that may require prompt intervention. One of these pathologies is cardiac ischemia, resulting from a disbalance between blood supply and demand.

Post hoc labeling an acute ECG as ischemic or non-ischemic based on clinical data: A necessary challenge.

The ECG is crucial in the prehospital (and early inhospital) phase of patients with symptoms suggestive of myocardial ischemia. Therefore, new algorithms for ECG-based myocardial ischemia detection are continuously being researched. Development and validation of these algorithms require a database of acute ECGs (from the prehospital or emergency department setting) including a representative mix of cases (ischemia present) and controls (no ischemia present).

Advanced repeated structuring and learning procedure to detect acute myocardial ischemia in serial 12-lead ECGs.

. Acute myocardial ischemia in the setting of acute coronary syndrome (ACS) may lead to myocardial infarction. Therefore, timely decisions, already in the pre-hospital phase, are crucial to preserving cardiac function as much as possible.

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).

Repeated Structuring & Learning Procedure for Detection of Myocardial Ischemia: a Robustness Analysis.

Myocardial ischemia, consisting in a reduction of blood flow to the heart, may cause sudden cardiac death by myocardial infarction or trigger serious abnormal rhythms. Thus, its timely identification is crucial. The Repeated Structuring and Learning Procedure (RS&LP), an innovative constructive algorithm able to dynamically create neural networks (NN) alternating structuring and learning phases, was previously found potentially useful for myocardial ischemia detection.

Artificial Neural Network for Atrial Fibrillation Identification in Portable Devices.

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.

Prevalence of ECGs Exceeding Thresholds for ST-Segment-Elevation Myocardial Infarction in Apparently Healthy Individuals: The Role of Ethnicity.

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.

Corrigendum: Normal Values of Corrected Heart-Rate Variability in 10-Second Electrocardiograms for All Ages.

[This corrects the article DOI: 10.3389/fphys.2018.

An initial exploration of subtraction electrocardiography to detect myocardial ischemia in the prehospital setting.

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.

Normal Values of QT Variability in 10-s Electrocardiograms for all Ages.

QT variability is a promising electrocardiographic marker. It has been studied as a screening tool for coronary artery disease and left ventricular hypertrophy, and increased QT variability is a known risk factor for sudden cardiac death. Considering that comprehensive normal values for QT variability were lacking, we set out to establish these in standard 10-s electrocardiograms (ECGs) covering both sexes and all ages.

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.

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.

Normal Values of Corrected Heart-Rate Variability in 10-Second Electrocardiograms for All Ages.

Heart-rate variability (HRV) measured on standard 10-s electrocardiograms (ECGs) has been associated with increased risk of cardiac and all-cause mortality, but age- and sex-dependent normal values have not been established. Since heart rate strongly affects HRV, its effect should be taken into account. We determined a comprehensive set of normal values of heart-rate corrected HRV derived from 10-s ECGs for both children and adults, covering both sexes.

Diagnostic Accuracy Of The Electrocardiographic Decision Support - Myocardial Ischaemia (EDS-MI) Algorithm In Detection Of Acute Coronary Occlusion.

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.

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.

Erratum: Genetic loci associated with heart rate variability and their effects on cardiac disease risk.

This corrects the article DOI: 10.1038/ncomms15805.

Genetic loci associated with heart rate variability and their effects on cardiac disease risk.

Reduced cardiac vagal control reflected in low heart rate variability (HRV) is associated with greater risks for cardiac morbidity and mortality. In two-stage meta-analyses of genome-wide association studies for three HRV traits in up to 53,174 individuals of European ancestry, we detect 17 genome-wide significant SNPs in eight loci. HRV SNPs tag non-synonymous SNPs (in NDUFA11 and KIAA1755), expression quantitative trait loci (eQTLs) (influencing GNG11, RGS6 and NEO1), or are located in genes preferentially expressed in the sinoatrial node (GNG11, RGS6 and HCN4).

Longitudinal association of short-term, metronome-paced heart rate variability and echocardiographically assessed cardiac structure at a 4-year follow-up: results from the prospective, population-based CARLA cohort.

Aims: To assess the value of cardiac structure/function in predicting heart rate variability (HRV) and the possibly predictive value of HRV on cardiac parameters.

Methods And Results: Baseline and 4-year follow-up data from the population-based CARLA cohort were used (790 men, 646 women, aged 45-83 years at baseline and 50-87 years at follow-up). Echocardiographic and HRV recordings were performed at baseline and at follow-up.