Analyzing ECG signals in professional football players using machine learning techniques

• Published in: Heliyon 2024-03, Vol.10 (5), p.e26789, Article e26789
• Main Authors: Munoz-Macho, A.A., Dominguez-Morales, M.J., Sevillano-Ramos, J.L.
• Format: Article
• Language: English
• Subjects: arrhythmia; Artificial neural network; cardiac arrest; computer software; data collection; Electrocardiogram; electrocardiography; Machine learning; males; Pro football players; sports; Sudden death prevention
• ISSN: 2405-8440; 2405-8440
• DOI: 10.1016/j.heliyon.2024.e26789

Football player's health is important, and preventing sudden cardiac arrest may be a critical issue. Professional football players have different ECG signals than the average population, yet there are considerable gaps in study whereas the general population has been extensively studied. (a) Generate a reference and innovative resting 12-lead ECG database from 54 UEFA PRO level male football players from La Liga. This is a novel approach to cope the ECG and possible arrythmias in athletes. (b) Manage each XML athlete ECG data and develop a free-use program to visualize, denoise and filter the signal with the capacity to automate the labelling of the waves and save the reports. (c) Study the ECG wave shape and generate models through ML to analyse its utility to automate basic diagnosis. The dataset collection is based on a prospective observational cohort and includes 10 s, 12-lead ECGs and rhythm and condition labels for each athlete. Physiological sport arrhythmias, T-Wave shape and other findings were studied and labelled. ECG Visualizer was developed and used for 3 machine learning (ML) methods to automate sinus bradycardia arrhythmia diagnosis. A dataset with 163 ECGs in XML format was collected comprising the Pro Football 12-lead Resting Electrocardiogram Database (PF12RED). “ECG Visualizer” software was developed, and ML was shown to be useful in detecting sinus bradycardia. The study demonstrates that AI and machine learning can detect simple arrhythmias with accuracy, also it provides a valuable dataset and a free software application. •12-lead resting ECG database contained La Liga male UEFA PRO footballers.•This database assists sports cardiology research by filling the study gap.•“ECG Visualizer' advances the field processing ECG wave labelling and report preparation for clinical practise and research.•Machine-learning sinus bradycardia detection highlights how such technology can improve diagnosis.•The study may improve pre-competition health examinations, athlete protection laws, and season-long ECG monitoring.