QRS-T Angles as Markers for Heart Sphericity in Subjects With Intrauterine Growth Restriction: A Simulation Study.

Changes induced by intrauterine growth restriction (IUGR) in cardiovascular anatomy and function that persist throughout life have been associated with a higher predisposition to heart disease in adulthood. Together with cardiac morphological remodelling, evaluated through the ventricular sphericity index, alterations in cardiac electrical function have been reported by characterization of the depolarization and repolarization loops, and their angular relationship, measured from the vectorcardiogram. The underlying relationship between the morphological remodelling and the angular variation of QRS and T-wave dominant vectors, if any, has not been explored.

Inference of ventricular activation properties from non-invasive electrocardiography.

The realisation of precision cardiology requires novel techniques for the non-invasive characterisation of individual patients' cardiac function to inform therapeutic and diagnostic decision-making. Both electrocardiography and imaging are used for the clinical diagnosis of cardiac disease. The integration of multi-modal datasets through advanced computational methods could enable the development of the cardiac 'digital twin', a comprehensive virtual tool that mechanistically reveals a patient's heart condition from clinical data and simulates treatment outcomes.

Development, calibration, and validation of a novel human ventricular myocyte model in health, disease, and drug block.

Human-based modelling and simulations are becoming ubiquitous in biomedical science due to their ability to augment experimental and clinical investigations. Cardiac electrophysiology is one of the most advanced areas, with cardiac modelling and simulation being considered for virtual testing of pharmacological therapies and medical devices. Current models present inconsistencies with experimental data, which limit further progress.

High arrhythmic risk in antero-septal acute myocardial ischemia is explained by increased transmural reentry occurrence.

Acute myocardial ischemia is a precursor of sudden arrhythmic death. Variability in its manifestation hampers understanding of arrhythmia mechanisms and challenges risk stratification. Our aim is to unravel the mechanisms underlying how size, transmural extent and location of ischemia determine arrhythmia vulnerability and ECG alterations.

Machine learning in the electrocardiogram.

The electrocardiogram is the most widely used diagnostic tool that records the electrical activity of the heart and, therefore, its use for identifying markers for early diagnosis and detection is of paramount importance. In the last years, the huge increase of electronic health records containing a systematised collection of different type of digitalised medical data, together with new tools to analyse this large amount of data in an efficient way have re-emerged the field of machine learning in healthcare innovation. This review describes the most recent machine learning-based systems applied to the electrocardiogram as well as pros and cons in the use of these techniques.

MRI-Based Computational Torso/Biventricular Multiscale Models to Investigate the Impact of Anatomical Variability on the ECG QRS Complex.

Aims: Patient-to-patient anatomical differences are an important source of variability in the electrocardiogram, and they may compromise the identification of pathological electrophysiological abnormalities. This study aims at quantifying the contribution of variability in ventricular and torso anatomies to differences in QRS complexes of the 12-lead ECG using computer simulations.

Methods: A computational pipeline is presented that enables computer simulations using human torso/biventricular anatomically based electrophysiological models from clinically standard magnetic resonance imaging (MRI).

Electrocardiogram phenotypes in hypertrophic cardiomyopathy caused by distinct mechanisms: apico-basal repolarization gradients vs. Purkinje-myocardial coupling abnormalities.

Aims: To identify key structural and electrophysiological features explaining distinct electrocardiogram (ECG) phenotypes in hypertrophic cardiomyopathy (HCM).

Methods And Results: Human heart-torso anatomical models were constructed from cardiac magnetic resonance (CMR) images of HCM patients, representative of ECG phenotypes identified previously. High performance computing simulations using bidomain models were conducted to dissect key features explaining the ECG phenotypes with increased HCM Risk-SCD scores, namely Group 1A, characterized by normal QRS but inverted T waves laterally and coexistence of apical and septal hypertrophy; and Group 3 with marked QRS abnormalities (deep and wide S waves laterally) and septal hypertrophy.

Distinct ECG Phenotypes Identified in Hypertrophic Cardiomyopathy Using Machine Learning Associate With Arrhythmic Risk Markers.

Ventricular arrhythmia triggers sudden cardiac death (SCD) in hypertrophic cardiomyopathy (HCM), yet electrophysiological biomarkers are not used for risk stratification. Our aim was to identify distinct HCM phenotypes based on ECG computational analysis, and characterize differences in clinical risk factors and anatomical differences using cardiac magnetic resonance (CMR) imaging. High-fidelity 12-lead Holter ECGs from 85 HCM patients and 38 healthy volunteers were analyzed using mathematical modeling and computational clustering to identify phenotypic subgroups.

Computational techniques for ECG analysis and interpretation in light of their contribution to medical advances.

Widely developed for clinical screening, electrocardiogram (ECG) recordings capture the cardiac electrical activity from the body surface. ECG analysis can therefore be a crucial first step to help diagnose, understand and predict cardiovascular disorders responsible for 30% of deaths worldwide. Computational techniques, and more specifically machine learning techniques and computational modelling are powerful tools for classification, clustering and simulation, and they have recently been applied to address the analysis of medical data, especially ECG data.

Sudden cardiac death and pump failure death prediction in chronic heart failure by combining ECG and clinical markers in an integrated risk model.

Background: Sudden cardiac death (SCD) and pump failure death (PFD) are common endpoints in chronic heart failure (CHF) patients, but prevention strategies are different. Currently used tools to specifically predict these endpoints are limited. We developed risk models to specifically assess SCD and PFD risk in CHF by combining ECG markers and clinical variables.

T-Wave Morphology Restitution Predicts Sudden Cardiac Death in Patients With Chronic Heart Failure.

Background: Patients with chronic heart failure are at high risk of sudden cardiac death (SCD). Increased dispersion of repolarization restitution has been associated with SCD, and we hypothesize that this should be reflected in the morphology of the T-wave and its variations with heart rate. The aim of this study is to propose an electrocardiogram (ECG)-based index characterizing T-wave morphology restitution (TMR), and to assess its association with SCD risk in a population of chronic heart failure patients.

Electrophysiological properties of computational human ventricular cell action potential models under acute ischemic conditions.

Acute myocardial ischemia is one of the main causes of sudden cardiac death. The mechanisms have been investigated primarily in experimental and computational studies using different animal species, but human studies remain scarce. In this study, we assess the ability of four human ventricular action potential models (ten Tusscher and Panfilov, 2006; Grandi et al.

Human ventricular activation sequence and the simulation of the electrocardiographic QRS complex and its variability in healthy and intraventricular block conditions.

Aims: To investigate how variability in activation sequence and passive conduction properties translates into clinical variability in QRS biomarkers, and gain novel physiological knowledge on the information contained in the human QRS complex.

Methods And Results: Multiscale bidomain simulations using a detailed heart-torso human anatomical model are performed to investigate the impact of activation sequence characteristics on clinical QRS biomarkers. Activation sequences are built and validated against experimentally-derived ex vivo and in vivo human activation data.

Early afterdepolarizations promote transmural reentry in ischemic human ventricles with reduced repolarization reserve.

Aims: Acute ischemia is a major cause of sudden arrhythmic death, further promoted by potassium current blockers. Macro-reentry around the ischemic region and early afterdepolarizations (EADs) caused by electrotonic current have been suggested as potential mechanisms in animal and isolated cell studies. However, ventricular and human-specific arrhythmia mechanisms and their modulation by repolarization reserve remain unclear.

Human-based approaches to pharmacology and cardiology: an interdisciplinary and intersectorial workshop.

Both biomedical research and clinical practice rely on complex datasets for the physiological and genetic characterization of human hearts in health and disease. Given the complexity and variety of approaches and recordings, there is now growing recognition of the need to embed computational methods in cardiovascular medicine and science for analysis, integration and prediction. This paper describes a Workshop on Computational Cardiovascular Science that created an international, interdisciplinary and inter-sectorial forum to define the next steps for a human-based approach to disease supported by computational methodologies.

Mechanisms of pro-arrhythmic abnormalities in ventricular repolarisation and anti-arrhythmic therapies in human hypertrophic cardiomyopathy.

Introduction: Hypertrophic cardiomyopathy (HCM) is a cause of sudden arrhythmic death, but the understanding of its pro-arrhythmic mechanisms and an effective pharmacological treatment are lacking. HCM electrophysiological remodelling includes both increased inward and reduced outward currents, but their role in promoting repolarisation abnormalities remains unknown. The goal of this study is to identify key ionic mechanisms driving repolarisation abnormalities in human HCM, and to evaluate anti-arrhythmic effects of single and multichannel inward current blocks.

Automatic SVM classification of sudden cardiac death and pump failure death from autonomic and repolarization ECG markers.

Background: Considering the rates of sudden cardiac death (SCD) and pump failure death (PFD) in chronic heart failure (CHF) patients and the cost-effectiveness of their preventing treatments, identification of CHF patients at risk is an important challenge. In this work, we studied the prognostic performance of the combination of an index potentially related to dispersion of repolarization restitution (Δα), an index quantifying T-wave alternans (IAA) and the slope of heart rate turbulence (TS) for classification of SCD and PFD.

Methods: Holter ECG recordings of 597 CHF patients with sinus rhythm enrolled in the MUSIC study were analyzed and Δα, IAA and TS were obtained.

Deriving respiration from photoplethysmographic pulse width.

A method for deriving respiration from the pulse photoplethysmographic (PPG) signal is presented. This method is based on the pulse width variability (PWV), and it exploits the respiratory information present in the pulse wave velocity and dispersion. It allows to estimate respiration signal from only a pulse oximeter which is a cheap and comfortable sensor.

ECG-based detection of body position changes using a Laplacian noise model.

Body position changes (BPC), which are often manifested in the ECG as shifts in the electrical axis of the heart, result in ST changes, and thus, may be misclassified as ischemic events during ambulatory monitoring. We have developed a BPC detector by modeling shifts as changes in the Karhunen-Loève transform coefficients of the QRS complex and the ST-T waveform. The noise is assumed to have a Laplacian distribution.

Quantification of restitution dispersion from the dynamic changes of the T-wave peak to end, measured at the surface ECG.

Action potential duration restitution (APDR) curves present spatial variations due to the electrophysiological heterogeneities present in the heart. Enhanced spatial APDR dispersion in ventricle has been suggested as an arrhythmic risk marker. In this study, we propose a method to noninvasively quantify dispersion of APDR slopes at tissue level by making only use of the surface electrocardiogram (ECG).

Discrimination between demand and supply ischemia episodes in Holter recordings.

ST segment changes provide a sensitive marker in the diagnosis of myocardial ischemia in Holter recordings. However not only the mechanisms of ischemia result in ST segment deviation but also heart rate related events. The very similar signature of ST modifications in ischemia and heart rate related events have driven us to look for other ECG indexes allowing to discriminate between them.