scholarly journals Electrocardiographic specificities in athletes

2013 ◽  
Vol 66 (5-6) ◽  
pp. 225-232
Author(s):  
Sanja Mazic ◽  
Biljana Lazovic ◽  
Marina Djelic ◽  
Zoran Stajic ◽  
Zdravko Mijailovic
Keyword(s):  
Cardiovascular Diseases ◽  
Sudden Cardiac Death ◽  
European Society ◽  
Qt Interval ◽  
Cardiac Death ◽  
Sinus Bradycardia ◽  
Screening Method ◽  
Bundle Branch Block ◽  
European Society Of Cardiology

Introduction. The use of electrocardiogram in athletes as a routine screening method for diagnosing potentially dangerous cardiovascular diseases is still an issue of debate. According to the guidelines of the European Society of Cardiology, the recording of electrocardiogram is necessary in all athletes as a screening method, whereas the guidelines of the American Heart Association do not necessitate an electrocardiogram as a screening method and they insist on detailed personal and family history and clinical examination. Classification of electrocardiogram changes in athletes. According to the classification of the European Society of Cardiology, electrocardiogram changes in athletes are divided into two groups: a) usual (physiological) that are connected with training; b) unusual (potentially clinically relevant) that are not connected with training. Sudden cardiac death in athletes. The most frequent causes include hypertrophic cardiomyopathy and congenital coronary artery anomalies, while others may be found only sporadically at autopsy. Physiological electrocardiogram changes are frequent in asymptomatic athletes and they do not require further assessment. They include sinus bradycardia, atrioventricular blocks of I and II degree - Wenkebach, isolated increased QRS voltage, incomplete right bundle branch block and early repolarization. Potentially pathological electrocardiogram changes in athletes are not frequent but they are alarming and they urge further assessment to diagnose the underlying cardiovascular disease as well as the prevention of sudden cardiac death. They include: T wave inversion, ST segment depression, complete right or left bundle branch block, atrial pre-excitation syndrome-WPW, long QT interval, short QT interval, Brugada like electrocardiogram finding. Conclusion. Introduction of electrocardiogram recording into the screening protocol in athletes increases the sensitivity of evaluation and may help to discover asymptomatic cardiovascular diseases that may cause sudden cardiac death. Special attention and further assessment are required when the above potentially pathological electrocardiogram changes are found in athletes.

scholarly journals Artificial Intelligence Segmented Dynamic Video Images for Continuity Analysis in the Detection of Severe Cardiovascular Disease

2021 ◽  
Vol 14 ◽  
Author(s):  
Xi Zhu ◽  
Wei Xia ◽  
Zhuqing Bao ◽  
Yaohui Zhong ◽  
Yu Fang ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Cardiovascular Diseases ◽  
High Risk ◽  
Sudden Cardiac Death ◽  
Sudden Death ◽  
Cardiac Death ◽  
Deep Neural Network ◽  
Video Image ◽  
Bundle Branch Block

In this paper, an artificial intelligence segmented dynamic video image based on the process of intensive cardiovascular and cerebrovascular disease monitoring is deeply investigated, and a sparse automatic coding deep neural network with a four layers stack structure is designed to automatically extract the deep features of the segmented dynamic video image shot, and six categories of normal, atrial premature, ventricular premature, right bundle branch block, left bundle branch block, and pacing are achieved through hierarchical training and optimization. Accurate recognition of heartbeats with an average accuracy of 99.5%. It provides technical assistance for the intelligent prediction of high-risk cardiovascular diseases like ventricular fibrillation. An intelligent prediction algorithm for sudden cardiac death based on the echolocation network was proposed. By designing an echolocation network with a multilayer serial structure, an intelligent distinction between sudden cardiac death signal and non-sudden death signal was realized, and the signal was predicted 5 min before sudden death occurred, with an average prediction accuracy of 94.32%. Using the self-learning capability of stack sparse auto-coding network, a large amount of label-free data is designed to train the stack sparse auto-coding deep neural network to automatically extract deep representations of plaque features. A small amount of labeled data then introduced to micro-train the entire network. Through the automatic analysis of the fiber cap thickness in the plaques, the automatic identification of thin fiber cap-like vulnerable plaques was achieved, and the average overlap of vulnerable regions reached 87%. The overall time for the automatic plaque and vulnerable plaque recognition algorithm was 0.54 s. It provides theoretical support for accurate diagnosis and endogenous analysis of high-risk cardiovascular diseases.

scholarly journals A validation study of the European Society of Cardiology guidelines for risk stratification of sudden cardiac death in childhood hypertrophic cardiomyopathy

EP Europace ◽  
2019 ◽  
Vol 21 (10) ◽  
pp. 1559-1565 ◽  
Author(s):  
Gabrielle Norrish ◽  
Tao Ding ◽  
Ella Field ◽  
Karen McLeod ◽  
Maria Ilina ◽  
...  
Keyword(s):  
Ventricular Tachycardia ◽  
Hypertrophic Cardiomyopathy ◽  
Sudden Cardiac Death ◽  
Risk Stratification ◽  
European Society ◽  
Cardiac Death ◽  
Left Ventricular ◽  
Sustained Ventricular Tachycardia ◽  
Rank Test ◽  
European Society Of Cardiology

Abstract Aims Sudden cardiac death (SCD) is the most common cause of death in children with hypertrophic cardiomyopathy (HCM). The European Society of Cardiology (ESC) recommends consideration of an implantable cardioverter-defibrillator (ICD) if two or more clinical risk factors (RFs) are present, but this approach to risk stratification has not been formally validated. Methods and results Four hundred and eleven paediatric HCM patients were assessed for four clinical RFs in accordance with current ESC recommendations: severe left ventricular hypertrophy, unexplained syncope, non-sustained ventricular tachycardia, and family history of SCD. The primary endpoint was a composite outcome of SCD or an equivalent event (aborted cardiac arrest, appropriate ICD therapy, or sustained ventricular tachycardia), defined as a major arrhythmic cardiac event (MACE). Over a follow-up period of 2890 patient years (median 5.5 years), MACE occurred in 21 patients (7.5%) with 0 RFs, 19 (16.8%) with 1 RFs, and 3 (18.8%) with 2 or more RFs. Corresponding incidence rates were 1.13 [95% confidence interval (CI) 0.7–1.73], 2.07 (95% CI 1.25–3.23), and 2.52 (95% CI 0.53–7.35) per 100 patient years at risk. Patients with two or more RFs did not have a higher incidence of MACE (log-rank test P = 0.34), with a positive and negative predictive value of 19% and 90%, respectively. The C-statistic was 0.62 (95% CI 0.52–0.72) at 5 years. Conclusions The incidence of MACE is higher for patients with increasing numbers of clinical RFs. However, the current ESC guidelines have a low ability to discriminate between high- and low-risk individuals.

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