Munoz-Macho A A, Dominguez-Morales M J, Sevillano-Ramos J L
Computer Architecture and Technology Department, University of Seville, Spain.
Performance and Medical Department, RCD Mallorca SAD, Palma de Mallorca, Spain.
Heliyon. 2024 Feb 27;10(5):e26789. doi: 10.1016/j.heliyon.2024.e26789. eCollection 2024 Mar 15.
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.
足球运动员的健康至关重要，预防心脏骤停可能是一个关键问题。职业足球运动员的心电图信号与普通人群不同，但在研究方面仍存在相当大的差距，而普通人群已得到广泛研究。
(a) 从西甲54名欧足联职业级男性足球运动员中生成一个参考性且具有创新性的静息12导联心电图数据库。这是一种应对运动员心电图及可能出现的心律失常的新方法。(b) 管理每个XML格式的运动员心电图数据，并开发一个免费使用的程序，用于可视化、去噪和过滤信号，具备自动标记波形和保存报告的能力。(c) 研究心电图波形并通过机器学习生成模型，以分析其在自动进行基本诊断方面的效用。
数据集收集基于前瞻性观察队列，包括每位运动员的10秒12导联心电图以及心律和状况标签。对生理性运动心律失常、T波形状和其他发现进行了研究和标记。开发了心电图可视化器，并将其用于3种机器学习方法以自动诊断窦性心动过缓心律失常。
收集了一个包含163份XML格式心电图的数据集，即职业足球12导联静息心电图数据库（PF12RED）。开发了“心电图可视化器”软件，并且机器学习在检测窦性心动过缓方面显示出有用性。
该研究表明，人工智能和机器学习能够准确检测简单的心律失常，同时还提供了一个有价值的数据集和一个免费的软件应用程序。
