@article{10.37349/ec.2025.101241,
abstract = {Aim: This study aimed to investigate the relationship between heart rate variability (HRV) parameters and performance in soccer players. Methods: This study used a cross-sectional design to assess HRV parameters in a cohort of twenty-nine male athletes, aged 18 to 20 years, randomly selected from the Macapá Sports Club team in the Amazon region. Resting HRV data for ten minutes while maintaining normal breathing, acquired with a Polar V800 heart rate monitor recording at a sampling rate of 1,000 Hz, were analyzed using Kubios HRV software to extract time domain: mean of the normal sinus intervals (MRR), the standard deviation of normal sinus (NN) intervals (SDNN), root mean square of successive differences (RMSSD), the percentage of times that the change in consecutive normal sinus intervals exceeded 50 ms (pNN50), and frequency domain: low frequency (LF), high frequency (HF), and LF/HF ratio parameters. Factor analysis was then performed using principal component (PC) extraction and varimax rotation. The logarithmic transformation [normalized LF/HF by logarithmic transformation (LF/HFNormlog)] was applied to address this non-normality before factor analysis. Results: The first two PCs showed that 87.4% of the total variance was explained by the original variables. The LF (–0.93), HF (0.93), and LF/HFNormlog (–0.92) parameters contributed significantly to PC1, also known as the frequency domain component. In contrast, the MRR (0.60), SDNN (0.91), RMSSD (0.89), and pNN50 (0.79) parameters contributed to PC2, also known as the time domain component. Conclusions: This study provides valuable evidence of the complex relationship between autonomic factors affecting HRV parameters in soccer players. Identifying two distinct PCs related to sympathetic and parasympathetic activity highlights the importance of monitoring HRV to optimize performance and recovery. Machine learning is important to monitor these changes in the possible molecular mechanisms controlling HRV in soccer players.},
author = {Materko, Wollner and Miranda, Sávio Andrei Medeiros and Bezerra, Thiago Henrique Lobato and de Oliveira Figueira, Carlos Alberto Machado},
doi = {10.37349/ec.2025.101241},
journal = {Exploration of Cardiology},
elocation-id = {101241},
title = {Heart rate variability in soccer players and the application of unsupervised machine learning},
url = {https://www.explorationpub.com/Journals/ec/Article/101241},
volume = {3},
year = {2025}
}