Automated Signal Quality Assessment of Single-Lead ECG Recordings for Early Detection of Silent Atrial Fibrillation

[Digital competence in rhythmology : Training and education]

BACKGROUND

The digital transformation in medicine, particularly in technology-orientated areas such as rhythmology, is leading to a rapid change in diagnostic and therapeutic options. Digital skills are helpful and need to keep up with this pace of change.

RESEARCH QUESTION

Which digital technologies and resources with rhythmological relevance play a role today and in the future?

METHODS

Review of the various digital technologies for rhythm detection and monitoring, as well as current digital resources for training and education.

RESULTS

Rhythm detection and monitoring can be optimized with smart devices and telemedicine, while digital platforms such as social media and virtual reality offer new perspectives in the training of rhythmology specialists.

CONCLUSION

Acquiring digital skills will be the basis for future work in rhythmology.

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.