557 research outputs found

    Advances in Electrocardiograms

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    Electrocardiograms have become one of the most important, and widely used medical tools for diagnosing diseases such as cardiac arrhythmias, conduction disorders, electrolyte imbalances, hypertension, coronary artery disease and myocardial infarction. This book reviews recent advancements in electrocardiography. The four sections of this volume, Cardiac Arrhythmias, Myocardial Infarction, Autonomic Dysregulation and Cardiotoxicology, provide comprehensive reviews of advancements in the clinical applications of electrocardiograms. This book is replete with diagrams, recordings, flow diagrams and algorithms which demonstrate the possible future direction for applying electrocardiography to evaluating the development and progression of cardiac diseases. The chapters in this book describe a number of unique features of electrocardiograms in adult and pediatric patient populations with predilections for cardiac arrhythmias and other electrical abnormalities associated with hypertension, coronary artery disease, myocardial infarction, sleep apnea syndromes, pericarditides, cardiomyopathies and cardiotoxicities, as well as innovative interpretations of electrocardiograms during exercise testing and electrical pacing

    International criteria for electrocardiographic interpretation in athletes: Consensus statement.

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    Sudden cardiac death (SCD) is the leading cause of mortality in athletes during sport. A variety of mostly hereditary, structural or electrical cardiac disorders are associated with SCD in young athletes, the majority of which can be identified or suggested by abnormalities on a resting 12-lead electrocardiogram (ECG). Whether used for diagnostic or screening purposes, physicians responsible for the cardiovascular care of athletes should be knowledgeable and competent in ECG interpretation in athletes. However, in most countries a shortage of physician expertise limits wider application of the ECG in the care of the athlete. A critical need exists for physician education in modern ECG interpretation that distinguishes normal physiological adaptations in athletes from distinctly abnormal findings suggestive of underlying pathology. Since the original 2010 European Society of Cardiology recommendations for ECG interpretation in athletes, ECG standards have evolved quickly, advanced by a growing body of scientific data and investigations that both examine proposed criteria sets and establish new evidence to guide refinements. On 26-27 February 2015, an international group of experts in sports cardiology, inherited cardiac disease, and sports medicine convened in Seattle, Washington (USA), to update contemporary standards for ECG interpretation in athletes. The objective of the meeting was to define and revise ECG interpretation standards based on new and emerging research and to develop a clear guide to the proper evaluation of ECG abnormalities in athletes. This statement represents an international consensus for ECG interpretation in athletes and provides expert opinion-based recommendations linking specific ECG abnormalities and the secondary evaluation for conditions associated with SCD

    Quantification of Ventricular Repolarization Dispersion Using Digital Processing of the Surface ECG

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    Digital processing of electrocardiographic records was one of the first applications of signal processing on medicine. There are many ways to analyze and study electrical cardiac activity using the surface electrocardiogram (ECG) and nowadays a good clinical diagnostic and prevention of cardiac risk are the principal goal to be achieved. One aim of digital processing of ECG signals has been quantification of ventricular repolarization dispersion (VRD), phenomenon which mainly is determined by heterogeneity of action potential durations (APD) in different myocardial regions. The APD differs not only between myocytes of apex and the base of both ventricles, but those of endocardial and epicardial surfaces (transmural dispersion) and between both ventricles. Also, it was demonstrated that several electrophysiologically and functionally different myocardial cells, like epicardial, endocardial and mid-myocardial M cells. The APD inequalities develop global and/or local voltage gradients that play an important role in the inscription of ECG T-wave morphology. In this way, we can assume that T-wave is a direct expression of ventricular repolarization inhomogeneities on surface ECG. Experimental and clinical studies have demonstrated a relationship between VRD and severe ventricular arrhythmias. In addition, patients having increased VRD values have a higher risk of developing reentrant arrhythmias. Frequently the heart answer to several pathological states produced an increase of VRD; this phenomenon may develop into malignant ventricular arrhythmia (MVA) and/or sudden cardiac death (SCD). Moreover, it has been showed that the underlying mechanisms in MVA and/or SCD are cardiac re-entry, increased automation, influence of autonomic nervous system and arrhythmogenic substrates linked with cardiac pathologies. These cardiac alterations could presented ischemia, hypothermia, electrolyte imbalance, long QT syndrome, autonomic system effects and others. Digital processing of ECG has been proved to be useful for cardiac risk assessment, with additional advantages like of being non invasive treatments and applicable to the general population. With the aim to identify high cardiac risk patients, the researchers have been tried to quantify the VRD with different parameters obtained by mathematic-computational processing of the surface ECG. These parameters are based in detecting changes of T-wave intervals and T-wave morphology during cardiac pathologies, linking these changes with VRD. In this chapter, we have presented a review of VRD indexes based on digital processing of ECG signals to quantify cardiac risk. The chapter is organized as follows: Section 2 explains ECG preprocessing and delineation of fiducial points. In Section 3, indexes of VRD quantification, such as: QT interval dispersion, QT interval variability and T-wave duration, are described. In Section 4, different repolarization indexes describing T-wave morphology and energy are examined, including complexity of repolarization, T-wave residuum, angle between the depolarization and repolarization dominant vectors, micro T-wave alternans, T-wave area and amplitude and T-wave spectral variability. Finally, in Section 5 conclusions are presented.Fil: Vinzio Maggio, Ana Cecilia. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Saavedra 15. Instituto Argentino de Matemática Alberto Calderón; ArgentinaFil: Bonomini, Maria Paula. Universidad de Buenos Aires. Facultad de Ingeniería. Instituto de Ingeniería Biomédica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Laciar Leber, Eric. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de San Juan. Facultad de Ingeniería; ArgentinaFil: Arini, Pedro David. Universidad de Buenos Aires. Facultad de Ingeniería. Instituto de Ingeniería Biomédica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Saavedra 15. Instituto Argentino de Matemática Alberto Calderón; Argentin

    Quality Control in ECG-based Atrial Fibrillation Screening

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    This thesis comprises an introductory chapter and four papers related to quality control in ECG-based atrial fibrillation (AF) screening. Atrial fibrillation is a cardiac arrhythmia characterized by an irregular rhythm and constitutes a major risk factor for stroke. Anticoagulation therapy significantly reduces this risk, and therefore, AF screening is motivated. Atrial fibrillation screening is often done using ECGs recorded outside the clinical environment. However, the higher susceptibility of such ECGs to noise and artifacts makes the identification of patients with AF challenging. The present thesis addresses these challenges at different levels in the data analysis chain. Paper I presents a convolutional neural network (CNN)-based approach to identify transient noise and artifacts in the detected beat sequence before AF detection. The results show that by inserting a CNN, prior to the AF detector, the number of false AF detections is reduced by 22.5% without any loss in the sensitivity, suggesting that the number of recordings requiring expert review can be significantly reduced. Paper II investigates the signal quality of a novel wet electrode technology, and how the improved signal quality translates to improved beat detection and AF detection performance. The novel electrode technology is designed for reduction of motion artifacts typically present in Holter ECG recordings. The novel electrode technology shows a better signal quality and detection performance when compared to a commercially available counterpart, especially when the subject becomes more active. Thus, it has the potential to reduce the review burden and costs associated with ambulatory monitoring.Paper III introduces a detector for short-episode supraventricular tachycardia (sSVT) in AF screening recordings, which has been shown to be associated with an increased risk for future AF. Therefore, the identification of subjects with suchepisodes may increase the usefulness of AF screening. The proposed detector is based on the assumption that the beats in an sSVT episode display similar morphology, and that episodes including detections of deviating morphology should be excluded. The results show that the number of false sSVT detections can be significantly reduced (by a factor of 6) using the proposed detector.Paper IV introduces a novel ECG simulation tool, which is capable of producing ECGs with various arrhythmia patterns and with several different types of noise and artifacts. Specifically, the ECG simulator includes models to generate noise observed in ambulatory recordings, and when recording using handheld recording devices. The usefulness of the simulator is illustrated in terms of AF detection performance when the CNN training in Paper I is performed using simulated data. The results show a very similar performance when training with simulated data compared to when training with real data. Thus, the proposed simulator is a valuable tool in the development and training of automated ECG processing algorithms. Together, the four parts, in different ways, contribute to improved algorithmic efficiency in AF screening

    Potential Predictors of Sudden Cardiac Death in Aortic Valve Disease

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    Although sudden death continues to claim 15 to 20% of patients with aortic valve disease, the exact cause still remains speculative. It has been the assumption of many workers that these deaths result from ventricular tachyarrhythmias. The major aim of this thesis was therefore to assess the prevalence of ventricular arrhythmias in patients with aortic valve disease and to evaluate their significance by signal-averaged electrocardiography (SAECG). A total of 100 patients, 55 with predominant aortic stenosis (AS) with a mean transaortic gradient of 81+/-27 mmHg, 16 with predominant aortic regurgitation (AR) and 29 with combined AS and AR were studied prior to aortic valve replacement (AVR). Substantial left ventricular hypertrophy was present with a mean echocardiographic left ventricular mass index of 210+/-72 g/m2. Left ventricular systolic and diastolic function were normal in 94% and 61% of patients respectively. Coronary angiography was performed in 89 patients of whom 50 (56%) had chest pain typical of angina pectoris and 21 (24%) had significant coronary artery disease. Angina was present in 20 of these 21 patients (95%). Thus angina could predict the presence of significant coronary artery disease with 95% sensitivity and 54% specificity. In agreement with previous work, this study has shown a high prevalence of complex ventricular arrhythmias. Nonsustained ventricular tachycardia (NSVT) was detected by ambulatory electrocardiographic monitoring in 9 (9%) patients of whom only one had late potentials on SAECG. The frequency of ventricular arrhythmias was not related to the degree of left ventricular hypertrophy or the severity of aortic valve disease. Left ventricular function did not have any effect on ventricular arrhythmias. A high prevalence of complex ventricular arrhythmias was also seen in the early (5 to 7 days post AVR) and late (121+/-24 days post AVR) post-operative periods. The frequency of ventricular arrhythmias was not affected by AVR. In the late post-operative period, 4 patients had NSVT, but none of them had late potentials on SAECG. As with the pre-operative results, there was little to suggest the presence of an arrhythmogenic substrate in these patients in view of the absence of late potentials on SAECG. Furthermore, no sustained ventricular arrhythmias were detected in the 3 study periods. Aortic valve replacement was accompanied by a significant regression in echocardiographic left ventricular hypertrophy in patients with predominant AS and those with combined AS and AR. Of the total 100 patients in this study, 75 were on the cardiac surgical waiting list of whom 60 have already undergone operation. There have been 7 deaths (7%) during the study period, 3 of them occurring suddenly in patients awaiting surgery. Thus, the incidence of sudden death while awaiting operation was 4%. It has been suggested that patients with decreased heart-rate variability have decreased vagal tone, increased sympathetic activity or both and hence are at a higher risk of developing ventricular fibrillation and sudden death. Cardiovascular autonomic function was assessed in 47 patients prior to AVR and repeated in 10 patients 3 months following AVR. Abnormal heart-rate variation during deep breathing was detected in 18 (38%) patients. AVR was not accompanied by any improvement in cardiovascular autonomic function at least in the shortterm . Thus, despite a high prevalence of ventricular arrhythmias in aortic valve disease patients with substantial left ventricular hypertrophy, there was little to suggest the presence of an arrhythmogenic substrate. The potential mechanism of sudden death in these patients could be speculated on the basis of impaired cardiovascular autonomic function

    Combined Nonlinear Analysis of Atrial and Ventricular Series for Automated Screening of Atrial Fibrillation

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    [EN] Atrial fibrillation (AF) is the most common cardiac arrhythmia in clinical practice. It often starts with asymptomatic and short episodes, which are difficult to detect without the assistance of automatic monitoring tools. The vast majority of methods proposed for this purpose are based on quantifying the irregular ventricular response (i.e., RR series) during the arrhythmia. However, although AF totally alters the atrial activity (AA) reflected on the electrocardiogram(ECG), replacing stable P-waves by chaotic and time-variant fibrillatory waves, this information has still not been explored for automated screening of AF. Hence, a pioneering AF detector based on quantifying the variability over time of the AA morphological pattern is here proposed. Results from two public reference databases have proven that the proposed method outperforms current state-of-the-art algorithms, reporting accuracy higher than 90%. A less false positive rate in the presence of other arrhythmias different from AF was also noticed. Finally, the combination of this algorithm with the classical analysis of RR series variability also yielded a promising trade-off between AF accuracy and detection delay. Indeed, this combination provided similar accuracy than RR-based methods, but with a significantly shorter delay of 10 beats.This work was supported by the Spanish Ministry of Economy and Competitiveness (Project TEC2014-52250-R).Rodenas, J.; Garcia, M.; Alcaraz, R.; Rieta, JJ. (2017). Combined Nonlinear Analysis of Atrial and Ventricular Series for Automated Screening of Atrial Fibrillation. Complexity. (2163610):1-13. doi:10.1155/2017/2163610S113216361

    A critical analysis of the spectral filtered, signal-averaged electrocardiogram

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    Frequency Analysis of Atrial Fibrillation From the Surface Electrocardiogram

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    Atrial fibrillation (AF) is the most common arrhythmia encountered in clinical practice. Neither the natural history of AF nor its response to therapy are sufficiently predictable by clinical and echocardiographic parameters. Atrial fibrillatory frequency (or rate) can reliably be assessed from the surface electrocardiogram (ECG) using digital signal processing (filtering, subtraction of averaged QRST complexes, and power spectral analysis) and shows large inter-individual variability. This measurement correlates well with intraatrial cycle length, a parameter which appears to have primary importance in AF domestication and response to therapy. AF with a low fibrillatory rate is more likely to terminate spontaneously, and responds better to antiarrhythmic drugs or cardioversion while high rate AF is more often persistent and refractory to therapy. In conclusion, frequency analysis of AF seems to be useful for non-invasive assessment of electrical remodeling in AF and may subsequently be helpful for guiding AF therapy
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