Brugada ECG patterns in athletes

The Recognition Method of Athlete Exercise Intensity Based on ECG and PCG

Athletes usually arrange their training plans and determine their training intensity according to the coach's experience and simple physical indicators such as heart rate during exercise. However, the accuracy of this method is poor, and the training plan and exercise intensity arranged according to this method can easily cause physical damage, or the training cannot meet the actual needs. Therefore, in order to realize the reasonable arrangement and monitoring of athletes' training, a method of human exercise intensity recognition based on ECG (electrocardiogram) and PCG (Phonocardiogram) is proposed. First, the ECG and PCG signals are fused into a two-dimensional image, and the dataset is marked and divided according to the different motion intensities. Then, the training set is trained with a CNN (convolutional neural network) to obtain the prediction model of the neural network. Finally, the neural network model is used to identify the ECG and PCG signals to judge the exercise intensity of the athlete, so as to adjust the training plan according to the exercise intensity. The recognition accuracy of the model on the dataset can reach 95.68%. Compared with the use of heart rate to detect the physical state during exercise, ECG records the total potential changes in the process of depolarization and repolarization of the heart, and PCG records the waveform of the beating sound of the heart, which contains richer feature information. Combined with the CNN method, the athlete's exercise intensity prediction model constructed by extracting the features of the athlete's ECG and PCG signals realizes the real-time monitoring of the athlete's exercise intensity and has high accuracy and generalization ability.

Brugada syndrome in a 4-year-old child with Lemierre syndrome—A case report

Brugada syndrome is a rare arrhythmogenic disease with characteristic electrocardiogram (ECG) findings. Fever represents an important triggering factor. We report the case of a 4-year-old Saudi boy who was diagnosed with Lemierre syndrome and subsequently developed Brugada syndrome.

International criteria for electrocardiographic interpretation in athletes: Consensus statement

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.

A meta-analytic review of prevalence for Brugada ECG patterns and the risk for death

BACKGROUND

The prevalence of Brugada ECG pattern (BrEP) is different in different regions, and its mean prevalence over the world is unknown. The risk of people with BrEP for death remains unknown. We performed a meta-analysis to determine the prevalence of BrEP and risk ratio (RR) for death.

METHODS

Relevant studies published between July 1, 2000 and August 20, 2016, which contain prevalence and RR for all-cause death and cardiac death, were included. The prevalence and RR are analyzed using meta-analysis.

RESULTS

We finally retrieved 24 studies of the prevalence for BrEP and 5 studies of the RR for all-cause death and cardiac death. The worldwide mean prevalence of BrEP is 0.4%, with highest in Asia (0.9%) and lowest in North America (0.2%). Additionally, the mean prevalence in male is 0.9%, whereas it is 0.1% in female. The RR of BrEP for all-cause death is 0.78 (95% confidence interval 0.45-1.37), and for cardiac death it is 0.92 (95% confidence interval 0.23-3.66).

CONCLUSION

The prevalence of BrEP is about 0.4% around the world with different prevalence in region and sex. Our study shows that BrEP may not be taken as a predictor of all-cause death and cardiac death.