Department of Public Sports, Huanghe Jiaotong University, Jiaozuo, Henan 454950, China.
Comput Intell Neurosci. 2022 May 9;2022:7792958. doi: 10.1155/2022/7792958. eCollection 2022.
In order to train at high-intensity, athletics can again cause varying degrees of myocardial damage. Evaluating the balance between exercise myocardial injury and exercise intensity should actively prevent myocardial injury caused by high-intensity athletic training. In this paper, an intelligent optimization algorithm is used to investigate the degree of myocardial injury. The basic idea is to define the measured data and the output of the numerical model as an objective function of the structural parameters, to obtain the structural parameters by finding ways to continuously optimize the objective function to be close to the observed values, and to identify the injury based on the changes in these parameters before and after myocardial injury. The objective function can be defined in various ways, and the myocardial injury optimization algorithm can be chosen. In order to obtain the best computational results, numerical simulations of damage identification are performed using the objective function and three machine learning-based optimization algorithms. The computational results show that the combination of the objective function and the machine learning algorithms provides good accuracy and computational speed in identifying myocardial injury.
为了进行高强度训练，运动员可能会再次造成不同程度的心肌损伤。评估运动性心肌损伤与运动强度之间的平衡，应积极预防高强度运动训练引起的心肌损伤。本文使用智能优化算法研究心肌损伤程度。基本思路是将测量数据和数值模型的输出定义为结构参数的目标函数，通过不断优化目标函数使其接近观测值的方式找到结构参数，然后根据心肌损伤前后这些参数的变化来识别损伤。目标函数可以通过各种方式定义，也可以选择心肌损伤优化算法。为了获得最佳的计算结果，使用目标函数和三种基于机器学习的优化算法对损伤识别进行了数值模拟。计算结果表明，目标函数与机器学习算法的组合在识别心肌损伤方面具有良好的准确性和计算速度。
