Electrocardiographic pattern of apparently healthy African adolescent athletes in Nigeria

The International Criteria for Electrocardiogram Interpretation in Athletes: Common Pitfalls and Future Directions

Preparticipation cardiovascular screening (PPCS) in young athletes is performed to detect conditions associated with sudden cardiac death. Many medical societies and sports governing bodies support the addition of a 12-lead electrocardiogram (ECG) to the history and physical to improve PPCS sensitivity. The current standard for ECG interpretation in athletes, the International Criteria, was developed to distinguish physiologic from pathologic ECG findings in athletes. Although application of the International Criteria has reduced the PPCS false-positive rate, interpretative challenges and potential areas of improvement remain. This review provides an overview of common pitfalls and future directions for ECG interpretation in athletes.

MLP-RL-CRD: diagnosis of cardiovascular risk in athletes using a reinforcement learning-based multilayer perceptron

Objective.Pre-participation medical screening of athletes is necessary to pinpoint individuals susceptible to cardiovascular events.Approach.The article presents a reinforcement learning (RL)-based multilayer perceptron, termed MLP-RL-CRD, designed to detect cardiovascular risk among athletes. The model underwent training using a publicized dataset that included the anthropological measurements (such as height and weight) and biomedical metrics (covering blood pressure and pulse rate) of 26 002 athletes. To address the data imbalance, a novel RL-based technique was adopted. The problem was framed as a series of sequential decisions in which an agent classified a received instance and received a reward at each level. To resolve the insensitivity to the initialization of conventional gradient-based learning methods, a mutual learning-based artificial bee colony (ML-ABC) was proposed.Main Results.The model outcomes were validated against positive (P) and negative (N) ECG findings that had been labeled by experts to signify individuals 'at risk' and 'not at risk,' respectively. The MLP-RL-CRD approach achieves superior outcomes (F-measure 87.4%; geometric mean 89.6%) compared with other deep models and traditional machine learning techniques. Optimal values for crucial parameters, including the reward function, were identified for the model based on experiments on the study dataset. Ablation studies, which omitted elements of the suggested model, affirmed the autonomous, positive, stepwise influence of these components on performing the model.Significance.This study introduces a novel, effective method for early cardiovascular risk detection in athletes, merging reinforcement learning and multilayer perceptrons, advancing medical screening and predictive healthcare. The results could have far-reaching implications for athlete health management and the broader field of predictive healthcare analytics.

The International Criteria for Electrocardiogram Interpretation in Athletes: Common Pitfalls and Future Directions

Preparticipation cardiovascular screening (PPCS) in young athletes is performed to detect conditions associated with sudden cardiac death. Many medical societies and sports governing bodies support the addition of a 12-lead electrocardiogram (ECG) to the history and physical to improve PPCS sensitivity. The current standard for ECG interpretation in athletes, the International Criteria, was developed to distinguish physiologic from pathologic ECG findings in athletes. Although application of the International Criteria has reduced the PPCS false-positive rate, interpretative challenges and potential areas of improvement remain. This review provides an overview of common pitfalls and future directions for ECG interpretation in athletes.

Patterns of resting electrocardiogram of male college track and field athletes

This study sought to determine the resting Electrocardiogram of male college Track and Field athletes, with a view to identifying electrocardiographic changes attributable to cardiac adaptations and possible differences in ECG morphologies by the athletic event. A descriptive survey design was used in the study. The population was male track and field athletes of Obafemi Awolowo University (OAU), Ile-Ife, Nigeria. Eighteen athletes; track (n=10) and field (n=8), were purposively selected from the OAU Athletics team that participated in the 14th West-African Universities’ Games, held at the University of Port-Harcourt, Rivers State Nigeria in 2018. Resting ECG was recorded with a 12-lead Electrocardiograph (SCHILLER-Cardiovit AT-2 plus), blood pressure was recorded with a digital blood pressure monitor (OMRON-M6 Comfort), weight, height, and BMI were recorded with an electronic BMI scale (SECA-220). Descriptive statistic of mean and standard deviation was computed and a paired-sample t-test was used to compare the resting ECG of athletes in the two divisions. Track and field athletes had mean RR-interval, P-wave, PR-interval, QRS-interval, QT-interval, and QTc of 993.22 ± 111.51ms, 97.12 ± 10.87ms, 157.00 ± 34.34ms, 120.67 ± 58.22ms, 376.94 ± 55.34ms and 416.78 ± 46.98mm respectively. With regards to voltage, athletes had mean voltages of 0.11 ± 0.09mm, -0.98 ± 0.77mm, and 1.54 ± 0.73mm for P-wave in lead II, S in VI, and R in V5 respectively. When athletes’ ECGs in the two divisions were compared, a significant difference was only found in the RR-interval (t = -3.08; P < 0.05). The study concluded that there were no distinctive morphological differences in the resting ECG of Nigerian collegiate track and field athletes.

The Impact of Ethnicity on Athlete ECG Interpretation: A Systematic Review

Athlete ECG interpretation criteria have been developed and refined from research in athlete populations; however, current guidelines are based on available data primarily from Caucasian and Black athletes. This study aimed to assess the impact of ethnicity on ECG interpretation in athletes. A systematic review was conducted of the MEDLINE, EMBASE, Scopus, SPORTDiscus, and Web of Science databases, for papers that assessed athlete screening ECGs and compared findings on the basis of ethnicity. Fifty-one papers which compared ECGs from various ethnicities were included. Most studies assessed Black athletes against Caucasian athletes and found a greater prevalence of T-wave inversion (TWI) (2.6–22.8% vs. 0–5.0%) and anterior TWI (3.7–14.3% vs. 0.6–2.0%). Black athlete subgroups in Africa had TWI (20–40%) and anterior TWI (4.3–18.7%) at a higher prevalence than other Black athletes. Athletes who were defined as mixed-race, Asian, and Pacific Islander are potentially more like Black athletes than Caucasian athletes. Black ethnicity is known to have an impact on the accurate interpretation of athlete ECGs; however, there is nuance related to origin of both parents. Asian and Pacific Islander origin also may impact athlete ECG interpretation. Further research is required to assist in distinguishing abnormal and normal athlete ECGs in different ethnic populations.