3,039 research outputs found

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

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    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). The ventricular model includes membrane kinetics represented by the biophysically detailed O’Hara Rudy model modified for tissue heterogeneity and includes fiber orientation based on the Streeter rule. A population of 265 torso/biventricular models was generated by combining ventricular and torso anatomies obtained from clinically standard MRIs, augmented with a statistical shape model of the body. 12-lead ECGs were simulated on the 265 human torso/biventricular electrophysiology models, and QRS morphology,duration and amplitude were quantified in each ECG lead for each of the human torso-biventricular models. Results:QRS morphologies in limb leads are mainly determined by ventricular anatomy,while in the precordial leads, and especially V1 to V4, they are determined by heart position within the torso. Differences in ventricular orientation within the torso can explain morphological variability from monophasic to biphasic QRS complexes. QRS duration ismainly influenced by myocardial volume, while it is hardly affected by the torso anatomyor position. An average increase of 0.12±0.05 ms in QRS duration is obtained for eachcm3of myocardial volume across all the leads while it hardly changed due to changes in torso volume. Conclusion:Computer simulations using populations of human torso/biventricular models based on clinical MRI enable quantification of anatomical causes of variability in the QRS complex of the 12-lead ECG. The human models presented also pave theway toward their use as testbeds in silico clinical trial

    Deep Learning in Cardiology

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    The medical field is creating large amount of data that physicians are unable to decipher and use efficiently. Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or for creating insights using big data. Deep learning has emerged as a more accurate and effective technology in a wide range of medical problems such as diagnosis, prediction and intervention. Deep learning is a representation learning method that consists of layers that transform the data non-linearly, thus, revealing hierarchical relationships and structures. In this review we survey deep learning application papers that use structured data, signal and imaging modalities from cardiology. We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table

    Indications and management of implantable cardioverter-defibrillator therapy in childhood hypertrophic cardiomyopathy

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    Sudden cardiac death is the most common mode of death during childhood and adolescence in hypertrophic cardiomyopathy, and identifying those individuals at highest risk is a major aspect of clinical care. The mainstay of preventative therapy is the implantable cardioverter-defibrillator, which has been shown to be effective at terminating malignant ventricular arrhythmias in children with hypertrophic cardiomyopathy but can be associated with substantial morbidity. Accurate identification of those children at highest risk who would benefit most from implantable cardioverter-defibrillator implantation while minimising the risk of complications is, therefore, essential. This position statement, on behalf of the Association for European Paediatric and Congenital Cardiology (AEPC), reviews the currently available data on established and proposed risk factors for sudden cardiac death in childhood-onset hypertrophic cardiomyopathy and current approaches for risk stratification in this population. It also provides guidance on identification of individuals at risk of sudden cardiac death and optimal management of implantable cardioverter-defibrillators in children and adolescents with hypertrophic cardiomyopathy

    Electrical and Mechanical Ventricular Activation During Left Bundle Branch Block and Resynchronization

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    Cardiac resynchronization therapy (CRT) aims to treat selected heart failure patients suffering from conduction abnormalities with left bundle branch block (LBBB) as the culprit disease. LBBB remained largely underinvestigated until it became apparent that the amount of response to CRT was heterogeneous and that the therapy and underlying pathology were thus incompletely understood. In this review, current knowledge concerning activation in LBBB and during biventricular pacing will be explored and applied to current CRT practice, highlighting novel ways to better measure and treat the electrical substrate

    Electrocardiogram Monitoring Wearable Devices and Artificial-Intelligence-Enabled Diagnostic Capabilities: A Review

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    Worldwide, population aging and unhealthy lifestyles have increased the incidence of high-risk health conditions such as cardiovascular diseases, sleep apnea, and other conditions. Recently, to facilitate early identification and diagnosis, efforts have been made in the research and development of new wearable devices to make them smaller, more comfortable, more accurate, and increasingly compatible with artificial intelligence technologies. These efforts can pave the way to the longer and continuous health monitoring of different biosignals, including the real-time detection of diseases, thus providing more timely and accurate predictions of health events that can drastically improve the healthcare management of patients. Most recent reviews focus on a specific category of disease, the use of artificial intelligence in 12-lead electrocardiograms, or on wearable technology. However, we present recent advances in the use of electrocardiogram signals acquired with wearable devices or from publicly available databases and the analysis of such signals with artificial intelligence methods to detect and predict diseases. As expected, most of the available research focuses on heart diseases, sleep apnea, and other emerging areas, such as mental stress. From a methodological point of view, although traditional statistical methods and machine learning are still widely used, we observe an increasing use of more advanced deep learning methods, specifically architectures that can handle the complexity of biosignal data. These deep learning methods typically include convolutional and recurrent neural networks. Moreover, when proposing new artificial intelligence methods, we observe that the prevalent choice is to use publicly available databases rather than collecting new data

    The Atrium in Atrial Fibrillation - A Clinical Review on How to Manage Atrial Fibrotic Substrates

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    Atrial fibrillation (AF) is the most common sustained arrhythmia in the population and is associated with a significant clinical and economic burden. Rigorous assessment of the presence and degree of an atrial arrhythmic substrate is essential for determining treatment options, predicting long-term success after catheter ablation, and as a substrate critical in the pathophysiology of atrial thrombogenesis. Catheter ablation of AF has developed into an essential rhythm-control strategy. Nowadays is one of the most common cardiac ablation procedures performed worldwide, with its success inversely related to the extent of atrial structural disease. Although atrial substrate evaluation remains complex, several diagnostic resources allow for a more comprehensive assessment and quantification of the extent of left atrial structural remodeling and the presence of atrial fibrosis. In this review, we summarize the current knowledge on the pathophysiology, etiology, and electrophysiological aspects of atrial substrates promoting the development of AF. We also describe the risk factors for its development and how to diagnose its presence using imaging, electrocardiograms, and electroanatomic voltage mapping. Finally, we discuss recent data regarding fibrosis biomarkers that could help diagnose atrial fibrotic substrates.info:eu-repo/semantics/publishedVersio

    Artificial Intelligence in the Differential Diagnosis of Cardiomyopathy Phenotypes

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    Artificial intelligence (AI) is rapidly being applied to the medical field, especially in the cardiovascular domain. AI approaches have demonstrated their applicability in the detection, diagnosis, and management of several cardiovascular diseases, enhancing disease stratification and typing. Cardiomyopathies are a leading cause of heart failure and life-threatening ventricular arrhythmias. Identifying the etiologies is fundamental for the management and diagnostic pathway of these heart muscle diseases, requiring the integration of various data, including personal and family history, clinical examination, electrocardiography, and laboratory investigations, as well as multimodality imaging, making the clinical diagnosis challenging. In this scenario, AI has demonstrated its capability to capture subtle connections from a multitude of multiparametric datasets, enabling the discovery of hidden relationships in data and handling more complex tasks than traditional methods. This review aims to present a comprehensive overview of the main concepts related to AI and its subset. Additionally, we review the existing literature on AI-based models in the differential diagnosis of cardiomyopathy phenotypes, and we finally examine the advantages and limitations of these AI approaches
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